text:
A 3D movie about invisible aliens? How could that have not been a runaway blockbuster? Not to worry if you didn't see Speed Racer fight invisible energy aliens with light bulbs on the big screen this past December because The Darkest Hour (review here) will be on DVD, Blu-ray, and Blu-ray 3D this April.
The Timur Bekmambetov produced, Chris Gorack directed alien invasion flick starring Emilie Hirsch, Max Minghella, and Olivia Thirlby as American douchebags struggling to stay alive in Moscow after invisible aliens show up and begin disintegrating everyone didn't exactly light up the box office the same way the aliens light up anything with a light bulb when it opened in theaters Christmas Day of last year. Nor was it a critical hit what with a Rotten Tomatoes score of 6 fresh, 44 negative. I know I wasn't amongst the fresh crowd.
Summit Entertainment has just announced its intention to release The Darkest Hour on DVD, Blu-ray, and Blu-ray 3D come April 10th. No disc artwork has been released yet (one has to assume the "SURVIVE THE HOLIDAYS" poster art will not be recycled) nor any word on what, if any, extras will be included on the various discs. We'll bring you that news when it's made available.
Got news? Click here to submit it!
Spend your darkest hour in the comments section below!


text:
Harry Potter's maize maze
Tom Pearcy has converted a field of maize into a maze and a spot the difference competition.
Mr Pearcy said: "I'm a big fan of Harry Potter and the release of the final film this summer marks the end of an era. I wanted to do something imaginative to say farewell to Harry, so creating the biggest image of him ever made and making it a spot the difference competition seemed like an interesting way to do that."
Each portrait of Daniel Radcliffe measures over 50m in diameter, thought to be the largest image of the actor in the world, and The Guinness Book of Records has been invited to confirm that notion.
The images have been painstakingly carved out of more than one million maize plants.
There are over 6 miles of pathways at Elvington, which form a challenging maze for visitors to explore.
And it's not the first time Mr Pearcy has attempted an elaborate puzzle, having previously created mazes in the design of a Spitfire, an astronaut, the Statue of Liberty and the Flying Scotsman.
York Maze opens to visitors this Saturday and will be open every day until September 4.
Farmer Tom Pearcy striking a pose amongst his new maze.
Most Viewed Pictures On Heart
Gallery Bare-Naked Ladies: Stars Without Makeup!
Gallery Pregnant Celebs: Cristiano Ronaldo Is Expecting His Fourth Child!
Gallery George From 'Stuart Little' Is All Grown Up... And You Won't Believe How Hot He Is!
Gallery Top Of The Chops! Martine McCutcheon Has A Drastic New Hair Cut
Gallery Jamie Dornan: Sexiest Pictures Of The Christian Grey Actor
Gallery Celebrity Weddings: Stevie Wonder Ties The Knot To Tomeeka Bracey
Gallery Kelly Brook's Most Gorgeous Pictures
Gallery The Most Beautiful Celebrity Wedding Dresses
Gallery The Top 25 Sexiest Men
Gallery Best Dressed Celebrity Baby Bumps: Ciara Works The Red Carpet
Gallery 10 David Beckham Quotes Straight Out Of 'Only Fools & Horses'
Recently Played Tracks
To listen live, choose your preferred station:
Stay tuned...
Now playing: The best feel good music

subset: commoncrawl


text:
Tom Ford wears the same suit 'over and over' again
Tom Ford has worn the same suit "over and over" again.
The 55-year-old American fashion designer - who was made creative director of Gucci for 10 years before starting his own eponymous fashion line in 2006 - has admitted he wears a "uniform" at all times, which sees him sport a sleek tailored suit, which looks the same as his other tuxedos every day.
Speaking to the Telegraph.co.uk about his wardrobe, the mogul said: "I wear this same suit over and over and over. I have probably quite a lot of them but they're pretty much all the same. I have a uniform; it's easy: I get up in the morning and I put on that uniform."
And Tom - who launched his film company in 2005 and made his directorial debut with 'Single Man' - has revealed he will happily wear a suit on set, much to the surprise of the film crew.
He explained: "Somebody said to me, 'Are you going to wear a suit on the film set?' And I said, 'Yeah, this is who I am and this is the way I dress. Why would I change my clothes? I'm most comfortable like this.'
"I'm not saying this egotistically but even on 'A Single Man' I felt very, very comfortable in the role as a director. But in fashion you get to do something new in two months, then it's gone, it's over and you move on. But film is something that lasts forever and I think because of that you give it a certain gravitas."
Meanwhile, the Texas-born star has admitted he has sampled "good fortune" but tries not to be materialistic.
Tom - who has adopted a son Alexander with his husband Richard Buckley - said: "I have had the good fortune in life to experience that sort of materialism that our culture tells us is going to make us happy. I'm not saying that it's not something that I enjoy; we live in a material world; we touch things that feel good and we get to see beautiful things.
"But you have to keep it in perspective and the most important things in life, certainly for me, are loyalty. I've been with the same person for 30 years and I've worked with the same people for many years."


text:
Trophy Central Coupon Code
Save big on quality Trophies or Awards
Huge savings on industry-leading selection of custom Trophies, Plaques, Medals and Engraved Awards.
Free Shipping on Orders of Trophy & Award Over $100
Get Free Shipping on Orders of Trophy & Award Over $100 at trophycentral.com. Free Shipping is Applicable to Continental USA only. No Coupon code required. Just click on the Link to get the Offer.
High-quality gift baskets from top merchant.
Find great selection of high-quality gift baskets at discounted prices.
Recently Expired Trophy Central Coupons
Free ground shipping on orders over $100
Spend $100 or more and get Free ground shipping at Trophycentral.com.
31% off Value Line Football Trophy
Buy Value Line Football Trophy just $5.58 (was $8.10) at Trophycentral.com.
Save 24% on Star Shield Engraved Trophy
Purchase Star Shield Engraved Trophy - Football Running Back just $8.90 (was $11.70) at Rophycentral.com.
36% savings on Classic Line Trophies - Football
Shop for Classic Line Trophies - Football just $7.25 (was $11.40) at Trophycentral.com.
39% off Participation Trophies with Marble Platform
Buy Participation Trophies with Marble Platform - Basketball just $4.86 at Trophycentral.com.
$9.50 off Silverplate Bowl Trophy
Purchase Silverplate Bowl Trophy for just $120.60 (was $130.10) at Trophycentral.com.
Free Engraving on Trophies & Plaques
Get Free Engraving on Trophies & Plaques at trophycentral.com.
$5 off your total order
Buy and get $5 discount on your entire order at trophycentral.com.
Up to 37% off Engraved Photo Plaques
Purchase Engraved Photo Plaques with discount up to 37% at trophycentral.com.
Up to 40% off Crystal Cup Trophies and Awards
Buy Crystal Cup Trophies and Awards with savings up to 40% at trophycentral.com.


text:
David Arnold Profile
The big question that remains, though, is whether the Wolverines will step up with an offer, and what about the in-state Ohio State Buckeyes?
"I was born in Ann Arbor, and of course my dad player there, so it has always been a dream of mine to play at Michigan," Arnold told Scout.com.
"I'm planning to attend their camp later this summer. Coach (Mike) Debord just told me they want to see me at camp, and allow Coach (Ron) English the chance to work with me and evaluate me."
"I'm attending Ohio State's camp on June 23rd. Coach (Jim) Bollman called me to make sure I was coming to camp, and told me they are real interested in me, right now. Ohio is my home state now so if they were to offer I would definitely give it strong consideration."
While Arnold will hope to perform well enough to earn scholarship offers at those two camps this summer, he still has plenty of focus on the schools that have offered him, in addition, to the schools who have shown him interest in May, i.e. Indiana, Purdue, West Virginia, and Minnesota.
"Coach (Ed) Warinner is recruiting me for Illinois, and has told me they are really interested in me. I've taken two visits there and really felt the coaches there are caring, real people. They are recruiting me as a safety. Coach (Curt) Mallory actually played with my dad at Michigan," Arnold said.
"Coach (Steve) Russ from Syracuse is recruiting me, and it is nice to know he has won a couple of Super Bowls with the Denver Broncos. When I visited Syracuse, I felt real comfortable, and their entire coaching staff was real classy. They have nice new facilties. They are also recruiting me as a safety."
"Akron has a good program, and I'm going to consider them. I like the MAC Conference, and Akron is located right next to my hometown."
"Indiana has told me they are need of safeties so there is a great chance to play early. They just told me they are going to continue to recruit me."
"Purdue has told me I'm real high on their list. They just need to evaluate more tape. The coach told me they think the world of me."
"Minnesota and West Virginia both came by the school during May, and said they would be recruiting me."
Arnold competed in track this spring with best handheld times being 10.7 and 21.7 in the 100 and 200, respetively.
He also noted he is still open to attending other camps this summer, but has not made any decisions.


text:
What Is This, Anyway: Automatic Hypernym Discovery
- Gwendolyn Park
- 2 years ago
- Views:
1 What Is This, Anyway: Automatic Hypernym Discovery Alan Ritter and Stephen Soderland and Oren Etzioni Turing Center Department of Computer Science and Engineering University of Washington Box Seattle, WA 98195, USA Abstract Can a system that learns from reading figure out on it s own the semantic classes of arbitrary noun phrases? This is essential for text understanding, given the limited coverage of proper nouns in lexical resources such as WordNet. Previous methods that use lexical patterns to discover hypernyms suffer from limited precision and recall. We present methods based on lexical patterns that find hypernyms of arbitrary noun phrases with high precision. This more than doubles the recall of proper noun hypernyms provided by WordNet at a modest cost to precision. We also present a novel method using a Hidden Markov Model (HMM) to extend recall further. Introduction and Motivation A goal of Machine Reading is an automatic system that extracts information from text and supports a wide range of inferencing capabilities (Etzioni, Banko, and Cafarella 2006). To enable such inferencing, an Information Extraction (IE) system must go beyond representing extracted entities as text strings and ontologize the text strings (Pennacchiotti and Pantel 2006; Soderland and Mandhani 2007). This involves semantically typing the text strings, grounding the string in real world entities where possible, and mapping the string to a concept in a formal taxonomy. In many cases, particularly for proper nouns, the text string is not found in an existing taxonomy and must be added on the fly as a child of an existing concept. This paper focuses on just one step in this ontologizing process: finding hypernyms for an arbitrary noun phrase (NP). This is a necessary first step in semantically typing the NP and mapping it to a node in a taxonomy. The problem that we address here is as follows. Given an NP e, find a set of NPs c i such that each c i is a hypernym of e in some sense as judged by a human. hypernymof(e) ={c 1,c 2,...c k } Note that we define hypernymy as a relation between surface forms and not synsets, as is done in WordNet. Copyright c 2009, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Consider the example of an NP extracted from the Web, Borland Delphi. This term is not found in WordNet, but lexical patterns in a large corpus suggest that Borland Delphi may have the following hypernyms: hypernymof(borland Delphi) = development environment = software development system = software tool = language = technology The rest of this paper is organized as follows. We discuss previous attempts at solving this problem. The manually engineered WordNet thesaurus (Miller et al. 1990) has good coverage of common nouns, but contains relatively few entries for Proper Nouns. Several researchers have used methods based on lexical patterns to discover hypernyms (Hearst 1992; Roark and Charniak 1998; Caraballo 1999; Snow, Jurafsky, and Ng 2005), although each of these methods suffers from limited precision and recall. We then present a series of methods for finding hypernyms of arbitrary NPs: HYPERNYMFINDER freq which is based on the frequency of Hearst pattern matches and HYPERNYMFINDER svm that uses a Support Vector Machine (SVM) classifier to incorporate additional features. Each of these systems learns to find hypernyms from lexical patterns and corpus statistics. Since this is an inherently error-prone process, each version of HYPERNYMFINDER assigns a probability to each c i it finds. Finally, we present HYPERNYMFINDER hmm, which uses an HMM language model to extend the recall of HY- PERNYMFINDER to cover NPs that do not match any of the lexical patterns used by the previous versions. Previous Work on Hypernym Discovery The WordNet thesaurus (Miller et al. 1990) is a highprecision lexical resource that has served as a baseline for hypernym discovery since the 1990 s. On a test set of 953 Noun phrases randomly selected from our Web corpus, we found that only 17% of the proper nouns and 64% of the common nouns were covered by WordNet. Information is often spotty even for NPs that are found in WordNet. For example, WordNet has an entry for Mel Gibson as an instance of the class actor, a subclass of human, but does not indicate that he is a man. We would need to use other 88
2 knowledge sources or extractions from a corpus to find that Mr. Gibson is also an instance of movie star, Australian, or Catholic. Some of the earliest research in automatically discovering hypernyms from text was by Hearst (1992). She presented the manually discovered lexical patterns listed in Figure 1 that detect hypernyms. These patterns are fairly good evidence that the entity E is a subclass or member of C. For the remainder of this paper, we will refer to these patterns as the Hearst patterns. EisaC E [,] and other C E [,] or other C C [,] such as E C [,] including E C [,] especially E suchcase Figure 1: The Hearst patterns : Lexical patterns that predict that entity E belongs to class C. The Hearst patterns suffer from limited recall not all pairs <E,C> will be found in such patterns. Also, due to the local nature of the patterns, they inevitably make errors. A sentence with... urban birds in cities such as pigeons... matches the pattern C such as E with C bound to city and E bound to pigeon, leading to city as a hypernym of pigeon. We made use of the Hearst patterns in our IE systems, KNOWITALL (Etzioni et al. 2004a; 2004b; 2005) and TEX- TRUNNER (Banko et al. 2007). We found that we needed to refine the rules somewhat. We increased recall by replacing the NP E with a list of NPs for most patterns. For example, the pattern C [,] such as NList would identify k NPs with hypernym C when NList is a list of k simple NPs. We also found that some patterns only have high precision when the class name is identified as a plural noun by a statistical part of speech tagger. Correct match on plural class name: Developing countries such as China and India have emerged... hypernymof(china) = country hypernymof(india) = country Error from singular class name: I like to listen to country such as Garth Brooks. hypernymof(garth Brooks) = country The accuracy of proposed hypernyms is correlated with extraction frequency, the number of distinct sentences that match a Hearst pattern. We estimated that, on a corpus of 117 million Web pages, hypernyms with frequency 1 had accuracy of 30% for common nouns and 49% for proper nouns. With a frequency 40, the accuracy increased to 86% (at recall less than 0.03) for common nouns and accuracy of 87% (at recall 0.01) for proper nouns. Snow, Jurafsky, and Ng (2005) induced a large number of weak patterns to detect hypernyms, using WordNet to automatically tag pairs of NPs from a corpus of 6 million newswire sentences. They then built a classifier using nearly 70,000 of these patterns and the frequency that each pattern occurs for a given pair of noun phrases in their corpus. Most of the patterns gave very weak evidence of a hypernym relation. The Hearst patterns, which were among those induced, were among the highest precision patterns. Snow et al. found that their classifier outperformed a baseline of simply looking for a single occurrence of a Hearst pattern and also outperformed a WordNet classifier. They evaluated their classifier on a set of 5,300 handtagged pairs of NPs taken from random paragraphs in their newswire corpus. Their recall-precision graph indicates precision 0.85 at recall 0.10 and precision 0.25 at recall of 0.30 for their hypernym classifier. A variant of their classifier that included evidence from coordinate terms (terms with common ancestor classes) also had precision 0.85 at recall 0.10 but increased precision to 0.35 at recall More recently, (McNamee et al. 2008) applied the techniques described in (Snow, Jurafsky, and Ng 2005) to detecting hypernyms of Named Entities (i.e. Proper Nouns) in order to improve performance of Question Answering systems. Their corpus consisted of about 16 million sentences which was larger than that used by Snow et. al, but still an order of magnitude smaller than the corpus used in this work. They obtained 53% mean average precision on automatically detected hyponyms of 75 categories. Their automatically discovered hyponyms resulted in a 9% boost in performance on a TREC Question Answering data set. Exploiting Lexico-syntactic Patterns Our goal is an efficient, practical HYPERNYMFINDER that takes an arbitrary NP e and returns a ranked list of up to k terms that are hypernyms of e for any sense of e. We first present HYPERNYMFINDER freq, a classifier based on the frequency of Hearst pattern matches. Then we present HYPERNYMFINDER svm that combines a variety of features using an SVM classifier to distinguish the correct hypernyms from among matches to the Hearst patterns. Later we explore methods that extend recall beyond hypernyms found by the Hearst patterns. Frequency-based Classifiers As a first step in building each of the following versions of HYPERNYMFINDER, we ran TEXTRUNNER on corpus of 117 million Web pages and recorded candidate hypernym pairs that matched any of the Hearst patterns. For each of these pairs of noun phrases <e, c> we counted the frequency of matches from distinct sentences for each Hearst pattern. Although the precision of a candidate hypernym generally correlates with the frequency of pattern matches, we found that even very high frequency candidates are often errors. Simply setting a threshold on frequency is not sufficient to obtain high precision. 89
3 The reason for this is the local nature of the Hearst patterns, which means that they can be fooled by certain sentence structures that occur with high frequency. Here are some examples of high frequency matches on Hearst patterns that produce incorrect hypernyms. A.... all around the world including Australia,... hypernymof(australia) = world B.... information about this hotel such as rates,... hypernymof(rates) = hotel C.... first prize is a trip to... hypernymof(prize) = trip Figure 2: Certain sentence structures that cause errors match Hearst patterns with high frequency. Fortunately, most of these sentence structures match only right patterns (as in A and B) or only left patterns (as in C). High precision can be obtained by filtering out hypernyms that do not have at least one right and one left pattern. Most of these high frequency errors occur only with right patterns (where e comes to the right of c) or only with left patterns (where e comes to the left of c). Precision is improved by requiring at least one match on both right and left pattern. Figure 3 compares the recall-precision curve for two methods of using Hearst pattern frequencies to find hypernyms. Both methods rank the proposed hypernyms by the number of distinct sentences that match a Hearst pattern. The baseline frequency method simply ranks by total pattern matches. The filtered frequency method (labeled as 1 left and 1 right ) assigns low probability to any proposed hypernyms that are not found in at least one right pattern and at least one left pattern. For this and each of the other experiments, we used a test set of 953 well-formed simple NPs e that were randomly selected from TEXTRUNNER extractions from a corpus of 117 million Web pages. We had each method output up to k =5proposed hypernyms for each e. We hand-tagged the proposed hypernyms as correct, incorrect, or vague. We compute recall in these experiments as the percentage of NPs in the test set that are covered by each method those NPs for which at least one correct hypernym is found. Precision is the total number of correct hypernyms divided by the total number of guessed hypernyms (up to a maximum of 5 for each e). Note that this differs from the method used to compute Precision and recall used by (Snow, Jurafsky, and Ng 2005). Their method computed precision and recall over candidate hypernym/hyponym word pairs. In contrast we are more concerned with whether or not we can find a good hypernym for a given noun phrase. The precision-recall curve for the baseline frequency method has recall 0.11 at precision 0.90 after an initial dip below 0.90 at recall The filtered frequency method avoids this initial dip in precision and reaches recall 0.23 at precision 0.90, more than twice the recall of the baseline method. Each method reaches recall of 0.66 at precision precision frequency 1 left and 1 right recall Figure 3: Ranking hypernyms by the frequency of Hearst pattern matches covers 66% of the NPs in our test set. Filtering these hypernyms to require at least one left and one right pattern gives a boost in precision Note that there is a limit in recall for any method built on the Hearst patterns about 1/3 of the noun phrases in our test set do not occur in any Hearst patterns in our large web corpus. Combining Evidence with SVM Although simple rules based on extraction frequency and the existence of both right and left patterns proved surprisingly effective, there are other types of evidence that can help distinguish correct hypernyms. We created HYPERNYMFINDER svm that uses an SVM classifier to assign a probability of correctness (Platt 1999) to a proposed hypernym pair <e, c> that has matched at least one Hearst pattern on a large text corpus. We used the following features to classify the proposed hypernym <e, c>: Total number of distinct pattern matches. Total number of left pattern matches. Total number of right pattern matches. Total number of matches for e is a c. Fraction of matches e which follow articles, determiners, or quantifiers. The rank by frequency among hypernyms of e. While frequency is the best indicator of hypernym correctness, we wanted to combine this flexibly with other weaker evidence. Our rule-based classifier filtered out proposed hypernyms where either left or right pattern matches had zero frequency, but this is a bit extreme. Many hypernyms with unbalanced left-right patterns are actually correct. 90
4 We included the frequency of matches on e is a c because this is somewhat less reliable than the other patterns. The ratio of e which follow articles, determiners and quantifiers helps avoid matches on sentences such as Our dog is a bloodhound.... This does not imply that all dogs are bloodhounds. We trained a Support Vector Machine (SVM) classifier on hand-labeled data using 10 fold cross validation. For these experiments, we split the test referenced earlier into a set of 583 proper nouns and a set of 370 common nouns, and evaluated each set separately 1. We consider a pair of NPs <e, c> in our test sets as correct if e belongs to the class c for some sense of e. Thus the entity cedar has hypernyms wood, softwood, and material for one sense of cedar, and also has hypernyms tree and conifer for another sense. We let each method output up to 5 hypernyms for each NP in the test set. WordNet is a baseline for high-precision hypernyms if a word is found in WordNet, it is guaranteed to have at least one hypernym. By our definition of correct hypernyms, WordNet has nearly perfect precision. So a practical hypernym finder would first look up a word in WordNet, before augmenting this with other methods. In the remainder of the experiments, we begin by looking up each e in WordNet and consider the words found in WordNet to be covered with at least one correct hypernym. Thus, each of our recall-precision curves starts at recall 0.17 and precision 1.00 for proper nouns, and starts at recall 0.64 and precision 1.00 for common nouns. These represent the words found in WordNet. We found 43% of the proper nouns and 9% of the common nouns which were covered by WordNet to be multi-word phrases. Our interest is in extending the recall to words not in WordNet. As in earlier experiments, we count recall as the number of e for which at least one correct hypernym c is found. Precision is computed as the fraction of c which are correct. Figures 4 and 5 present results for proper nouns and common nouns, respectively. The lines labeled Rule-based Classifier are for the filtered frequency implementation of HYPERNYMFINDER freq Ṫhe lines labeled SVM are from HYPERNYMFINDER svm using the features enumerated in this section. A simple WordNet lookup covers 17% of proper nouns. HYPERNYMFINDER freq extends the recall to 0.32 at precision 0.90 and recall 0.65 at precision The curve for HYPERNYMFINDER svm is consistently higher than for HYPERNYMFINDER freq and has recall 0.37 at precision 0.90 and a higher precision of 0.73 at the tail of the curve. There was less room for improvement over WordNet for common nouns, of which WordNet covers 64%. Here HYPERNYMFINDER freq extends recall to 0.67 at precision 0.90 and gives recall of 0.80 at precision Again, the curve for HYPERNYMFINDER svm is consistently higher than for HYPERNYMFINDER freq with recall 0.70 at precision These methods are straightforward ways to find hyper- 1 56% of the proper nouns and 27% of the common nouns were multi-word phrases precision Proper Nouns Rule based classifier SVM SVM + HMM T classifier percentage of entities with one or more good hypernym Figure 4: Each of our methods extends recall for proper nouns far beyond the recall of 0.17 from WordNet lookup. An SVM classifier uniformly raises precision over simple rules using Hearst pattern frequency. Adding an HMM language model extends recall even further. nyms for arbitrary proper nouns that extend recall beyond that of WordNet, particularly the SVM classifier, with a graceful decline in precision at higher recall. Yet these results are limited by the coverage of Hearst patterns. For 20% of the common nouns and 36% of the proper nouns in our test set, there were no matches of Hearst patterns that give a correct hypernym. In the following section, we describe experiments to extend the coverage of our HYPER- NYMFINDER to find hypernyms for some of these NPs that are beyond the reach of Hearst patterns. Extending Recall Beyond Patterns Here is the intuition of our approach to extending recall beyond the Hearst patterns. If we cannot find hypernyms for entity e using the methods presented thus far, perhaps we can find a sufficiently similar entity e whose hypernyms we know. Given a good similarity metric, there should be a high probability that e and e share the same hypernyms. HMM Language Models Inspired by REALM (Downey, Schoenmackers, and Etzioni 2007) we investigated using Hidden Markov Models (HMMs) (Rabiner 1989) as the similarity metric for another version of HYPERNYMFINDER. REALM uses HHMs to perform type checking of arguments in an open-domain IE system. Given an HMM with n hidden states, which has been trained to model the generation of our corpus, we can compute the probability that each NP is generated by each of the 91
5 precision Common Nouns Rule based classifier SVM SVM + HMM T classifier percentage of entities with one or more good hypernym Figure 5: Performance on common nouns has qualitatively similar results to that of proper nouns, but with a higher initial recall from WordNet and a smaller gain in recall from our other methods. HMM s hidden states. This produces for each NP in our corpus, a vector of n probabilities that can be used as features to a classifier or distance measure. Downey et al. performed experiments that suggest that HMMs produce better similarity measures than traditional term-vector-based approaches. The intuitive argument for this is that many coordinate terms (terms that share the same hypernym) appear in similar, though not identical contexts. For example Pickerington and Chicago are coordinate terms, because they share the common hypernym, City. Now suppose that we see the following two contexts: Pickerington, Ohio Chicago, Illinois These two contexts are not identical, so in a term-vectorbased similarity measure, they provide no evidence that Pickerington and Chicago are coordinate terms. However the two contexts,, Ohio and, Illinois are quite similar and so it is likely that they were generated by the same hidden states in the HMM. So by viewing the hidden state distribution of an HMM as context vectors, these two cities have a high similarity given the above evidence. Using HMMs for Hypernym Discovery We wish to create a HYPERNYMFINDER hmm that retains the performance of HYPERNYMFINDER svm on NPs that are covered by Hearst patterns, but which additionally finds hypernyms for NPs that do not match any of the Hearst patterns in our corpus. This HYPERNYMFINDER hmm assigns to each NP a probability that is a linear combination of the probability from HYPERNYMFINDER svm and the probability from a new classifier HYPERNYMFINDER coord. HYPERNYMFINDER coord uses HMM hidden state distribution as features to find potential hypernyms c for an arbitrary NP e as follows. Rather than compute the probability that c is a hypernym of e, this classifier computes the probability that e has a coordinate term e that is a hyponym of c. The HYPERNYMFINDER coord increased the pool of proposed hypernym pairs <e, c>, but had relatively low precision. Just because two NPs have highly similar context does not necessarily mean that they share the same hypernyms. We had the highest precision for the more coarse-grained classes such as person, organization, and region. For that reason, we chose a relatively high level in the WordNet hierarchy as a cutoff (6 levels from the top). We also set a threshold on the HMM similarity metric, so HYPERNYMFINDER coord only outputs <e, c> if it finds a highly similar e such that e is a hyponym of a high-level WordNet class c. We experimented with various weights to combined the probabilities and found the best results on our test set to be where HYPERNYMFINDER svm had weight 0.8 and HYPERNYMFINDER coord had weight 0.2. The result of these settings is shown in Figures 4 and 5 as SVM + HMM-T classifier. For both common nouns and proper nouns, adding evidence from HYPERNYMFINDER coord extends the curve to the right. This increases recall for common nouns from 0.80 to 0.82 and increases recall for proper nouns from 0.65 to Unfortunately, in both cases the curve for HYPERNYMFINDER hmm has an initial dip. This is because a term e with the most similar context to e is often not actually a coordinate terms, so assigning the hypernyms of e to e results in a error. Conclusions We have presented a series of methods to find hypernyms of an arbitrary noun phrase e. These provide easyto-implement methods, the simplest of which requires no tagged training. The main lessons we learned is that fairly simple methods are sufficient to find hypernyms at high precision and with fairly high recall. This is particularly true for proper nouns where our HYPERNYMFINDER more than doubles the recall for proper nouns at precision 0.90 compared to looking up terms in WordNet. The gain in recall is less for common nouns, where WordNet has coverage of 64%. It is still an open question how best to extend recall beyond hypernyms that match one of the Hearst patterns, although we had some success in using an HMM language model. Some of our future research in machine reading depends on finding hypernyms with high coverage and high precision. We are inclined to try a hybrid approach combine the methods presented here with using a named-entity tagger to assign a coarse-grained class to proper nouns, using a large suite of weak hypernym patterns, and using lexical patterns as well as a language model to find coordinate terms. 92
6 Acknowledgments This research was supported in part by NSF grants IIS and IIS , ONR grant N as well as gifts from the Utilika Foundation and Google, and was carried out at the University of Washington s Turing Center. References Banko, M.; Cafarella, M.; Soderland, S.; Broadhead, M.; and Etzioni, O Open information extraction from the Web. In Procs. of IJCAI. Caraballo, S Automatic construction of a hypernymlabeled noun hierarchy from text. In Procs. of the 37th Annual Meeting of the Association for Computational Linguistics, Downey, D.; Schoenmackers, S.; and Etzioni, O Sparse information extraction: Unsupervised language models to the rescue. In Proc. of ACL. Etzioni, O.; Banko, M.; and Cafarella, M Machine Reading. In AAAI. Etzioni, O.; Cafarella, M.; Downey, D.; Kok, S.; Popescu, A.; Shaked, T.; Soderland, S.; Weld, D.; and Yates, A. 2004a. Web-Scale Information Extraction in KnowItAll. In WWW, Etzioni, O.; Cafarella, M.; Downey, D.; Popescu, A.; Shaked, T.; Soderland, S.; Weld, D.; and Yates, A. 2004b. Methods for domain-independent information extraction from the Web: An experimental comparison. In Procs. of the 19th National Conference on Artificial Intelligence (AAAI-04), Etzioni, O.; Cafarella, M.; Downey, D.; Kok, S.; Popescu, A.; Shaked, T.; Soderland, S.; Weld, D.; and Yates, A Unsupervised named-entity extraction from the web: An experimental study. Artificial Intelligence 165(1): Hearst, M Automatic Acquisition of Hyponyms from Large Text Corpora. In Procs. of the 14th International Conference on Computational Linguistics, McNamee, P.; Snow, R.; Schone, P.; and Mayfield, J Learning named entity hyponyms for question answering. In To appear in Proceedings of IJCNLP Miller, G.; Beckwith, R.; Fellbaum, C.; Gross, D.; and Miller, K Introduction to WordNet: An on-line lexical database. International Journal of Lexicography 3(4): Pennacchiotti, M., and Pantel, P Ontologizing semantic relations. In COLING/ACL-06, Platt, J. C Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In Advances in Large Margin Classifiers, MIT Press. Rabiner, L. R A tutorial on Hidden Markov Models and selected applications in speech recognition. Proceedings of the IEEE 77(2): Roark, B., and Charniak, E Noun-phrase cooccurence statistics for semi-automatic semantic lexicon construction. In Procs. of COLING-ACL 1998, Snow, R.; Jurafsky, D.; and Ng, A. Y Learning syntactic patterns for automatic hypernym discovery. In NIPS Soderland, S., and Mandhani, B Moving from textual relations to ontologized relations. In AAAI Spring Symposium on Machine Reading. 93
2. REPORT TYPE Final Technical Report
REPORT DOCUMENTATION PAGE Form Approved OMB No. 0704 0188 The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions,More information
Open Information Extraction from the Web
Open Information Extraction from the Web Michele Banko, Michael J Cafarella, Stephen Soderland, Matt Broadhead and Oren Etzioni Turing Center Department of Computer Science and Engineering University ofMore information
Extraction of Hypernymy Information from Text
Extraction of Hypernymy Information from Text Erik Tjong Kim Sang, Katja Hofmann and Maarten de Rijke Abstract We present the results of three different studies in extracting hypernymy information fromMore information
Building a Question Classifier for a TREC-Style Question Answering System
Building a Question Classifier for a TREC-Style Question Answering System Richard May & Ari Steinberg Topic: Question Classification We define Question Classification (QC) here to be the task that, givenMore information
3 Paraphrase Acquisition. 3.1 Overview. 2 Prior Work
Unsupervised Paraphrase Acquisition via Relation Discovery Takaaki Hasegawa Cyberspace Laboratories Nippon Telegraph and Telephone Corporation 1-1 Hikarinooka, Yokosuka, Kanagawa 239-0847, Japan firstname.lastname@example.com information
Identifying Focus, Techniques and Domain of Scientific Papers
Identifying Focus, Techniques and Domain of Scientific Papers Sonal Gupta Department of Computer Science Stanford University Stanford, CA 94305 firstname.lastname@example.com Christopher D. Manning Department ofMore information
Taxonomy learning factoring the structure of a taxonomy into a semantic classification decision
Taxonomy learning factoring the structure of a taxonomy into a semantic classification decision Viktor PEKAR Bashkir State University Ufa, Russia, 450000 email@example.com Steffen STAAB Institute AIFB,More information
Detecting Parser Errors Using Web-based Semantic Filters
Detecting Parser Errors Using Web-based Semantic Filters Alexander Yates Stefan Schoenmackers University of Washington Computer Science and Engineering Box 352350 Seattle, WA 98195-2350 Oren Etzioni {ayates,More information
SVM Based Learning System For Information Extraction
SVM Based Learning System For Information Extraction Yaoyong Li, Kalina Bontcheva, and Hamish Cunningham Department of Computer Science, The University of Sheffield, Sheffield, S1 4DP, UK {yaoyong,kalina,email@example.com information
Interactive Dynamic Information Extraction
Interactive Dynamic Information Extraction Kathrin Eichler, Holmer Hemsen, Markus Löckelt, Günter Neumann, and Norbert Reithinger Deutsches Forschungszentrum für Künstliche Intelligenz - DFKI, 66123 SaarbrückenMore information
Domain Classification of Technical Terms Using the Web
Systems and Computers in Japan, Vol. 38, No. 14, 2007 Translated from Denshi Joho Tsushin Gakkai Ronbunshi, Vol. J89-D, No. 11, November 2006, pp. 2470 2482 Domain Classification of Technical Terms UsingMore information
Methods for Domain-Independent Information Extraction from the Web: An Experimental Comparison
Methods for Domain-Independent Information Extraction from the Web: An Experimental Comparison Oren Etzioni, Michael Cafarella, Doug Downey, Ana-Maria Popescu Tal Shaked, Stephen Soderland, Daniel S. Weld,More information
A Survey on Product Aspect Ranking Techniques
A Survey on Product Aspect Ranking Techniques Ancy. J. S, Nisha. J.R P.G. Scholar, Dept. of C.S.E., Marian Engineering College, Kerala University, Trivandrum, India. Asst. Professor, Dept. of C.S.E., MarianMore information
Unsupervised Named-Entity Recognition: Generating Gazetteers and Resolving Ambiguity
1 Unsupervised Named-Entity Recognition: Generating Gazetteers and Resolving Ambiguity David Nadeau 1,2, Peter D. Turney 1 and Stan Matwin 2,3 1 Institute for Information Technology National Research CouncilMore information
PRODUCT REVIEW RANKING SUMMARIZATION N.P.Vadivukkarasi, Research Scholar, Department of Computer Science, Kongu Arts and Science College, Erode. Dr. B. Jayanthi M.C.A., M.Phil., Ph.D., Associate Professor,More information
Learning First-Order Horn Clauses from Web Text
Learning First-Order Horn Clauses from Web Text Stefan Schoenmackers, Oren Etzioni, Daniel S. Weld Turing Center University of Washington Computer Science and Engineering Box 352350 Seattle, WA 98125,More information
Extracting Word Relationships from Unstructured Data (Learning Human Activities from General Websites)
Extracting Word Relationships from Unstructured Data (Learning Human Activities from General Websites) Anirudha S. Bhat a, Krithika K. Iyer b, and Rahul Venkatraj c Abstract One of the biggest challengesMore information
Ngram Search Engine with Patterns Combining Token, POS, Chunk and NE Information
Ngram Search Engine with Patterns Combining Token, POS, Chunk and NE Information Satoshi Sekine Computer Science Department New York University firstname.lastname@example.com Kapil Dalwani Computer Science DepartmentMore information
Facebook Friend Suggestion Eytan Daniyalzade and Tim Lipus
Facebook Friend Suggestion Eytan Daniyalzade and Tim Lipus 1. Introduction Facebook is a social networking website with an open platform that enables developers to extract and utilize user informationMore information
Generatin Coherent Event Schemas at Scale
Generatin Coherent Event Schemas at Scale Niranjan Balasubramanian, Stephen Soderland, Mausam, Oren Etzioni University of Washington Presented By: Jumana Table of Contents: 1 Introduction 2 System OverviewMore information
Guest Editors Introduction: Machine Learning in Speech and Language Technologies
Guest Editors Introduction: Machine Learning in Speech and Language Technologies Pascale Fung (email@example.com) Department of Electrical and Electronic Engineering Hong Kong University of Science andMore information
Open Domain Information Extraction. Günter Neumann, DFKI, 2012
Open Domain Information Extraction Günter Neumann, DFKI, 2012 Improving TextRunner Wu and Weld (2010) Open Information Extraction using Wikipedia, ACL 2010 Fader et al. (2011) Identifying Relations forMore information
Domain Independent Knowledge Base Population From Structured and Unstructured Data Sources
Proceedings of the Twenty-Fourth International Florida Artificial Intelligence Research Society Conference Domain Independent Knowledge Base Population From Structured and Unstructured Data Sources MichelleMore information
Web Document Clustering
Web Document Clustering Lab Project based on the MDL clustering suite http://www.cs.ccsu.edu/~markov/mdlclustering/ Zdravko Markov Computer Science Department Central Connecticut State University New Britain,More information
A Survey on Product Aspect Ranking
A Survey on Product Aspect Ranking Charushila Patil 1, Prof. P. M. Chawan 2, Priyamvada Chauhan 3, Sonali Wankhede 4 M. Tech Student, Department of Computer Engineering and IT, VJTI College, Mumbai, Maharashtra,More information
Exploiting Strong Syntactic Heuristics and Co-Training to Learn Semantic Lexicons
Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Philadelphia, July 2002, pp. 125-132. Association for Computational Linguistics. Exploiting Strong Syntactic HeuristicsMore information
POSBIOTM-NER: A Machine Learning Approach for. Bio-Named Entity Recognition
POSBIOTM-NER: A Machine Learning Approach for Bio-Named Entity Recognition Yu Song, Eunji Yi, Eunju Kim, Gary Geunbae Lee, Department of CSE, POSTECH, Pohang, Korea 790-784 Soo-Jun Park BioinformaticsMore information
Chinese Open Relation Extraction for Knowledge Acquisition
Chinese Open Relation Extraction for Knowledge Acquisition Yuen-Hsien Tseng 1, Lung-Hao Lee 1,2, Shu-Yen Lin 1, Bo-Shun Liao 1, Mei-Jun Liu 1, Hsin-Hsi Chen 2, Oren Etzioni 3, Anthony Fader 4 1 InformationMore information
Facilitating Business Process Discovery using Email Analysis
Facilitating Business Process Discovery using Email Analysis Matin Mavaddat firstname.lastname@example.com Stewart Green Stewart.Green Ian Beeson Ian.Beeson Jin Sa Jin.Sa Abstract Extracting business processMore information
Extracting Events from Web Documents for Social Media Monitoring using Structured SVM
IEICE TRANS. FUNDAMENTALS/COMMUN./ELECTRON./INF. & SYST., VOL. E85A/B/C/D, No. xx JANUARY 20xx Letter Extracting Events from Web Documents for Social Media Monitoring using Structured SVM Yoonjae Choi,More information
Collecting Polish German Parallel Corpora in the Internet
Proceedings of the International Multiconference on ISSN 1896 7094 Computer Science and Information Technology, pp. 285 292 2007 PIPS Collecting Polish German Parallel Corpora in the Internet Monika RosińskaMore information
ANALYSIS OF LEXICO-SYNTACTIC PATTERNS FOR ANTONYM PAIR EXTRACTION FROM A TURKISH CORPUS Gürkan Şahin 1, Banu Diri 1 and Tuğba Yıldız 2 1 Faculty of Electrical-Electronic, Department of Computer EngineeringMore information
Using Knowledge Extraction and Maintenance Techniques To Enhance Analytical Performance
Using Knowledge Extraction and Maintenance Techniques To Enhance Analytical Performance David Bixler, Dan Moldovan and Abraham Fowler Language Computer Corporation 1701 N. Collins Blvd #2000 Richardson,More information
Brill s rule-based PoS tagger
Beáta Megyesi Department of Linguistics University of Stockholm Extract from D-level thesis (section 3) Brill s rule-based PoS tagger Beáta Megyesi Eric Brill introduced a PoS tagger in 1992 that was basedMore information
Dublin City University at QA@CLEF 2008
Dublin City University at QA@CLEF 2008 Sisay Fissaha Adafre Josef van Genabith National Center for Language Technology School of Computing, DCU IBM CAS Dublin sadafre,firstname.lastname@example.com Abstract WeMore information
Domain Adaptive Relation Extraction for Big Text Data Analytics. Feiyu Xu
Domain Adaptive Relation Extraction for Big Text Data Analytics Feiyu Xu Outline! Introduction to relation extraction and its applications! Motivation of domain adaptation in big text data analytics! Solutions!More information
Analysis and Synthesis of Help-desk Responses
Analysis and Synthesis of Help-desk s Yuval Marom and Ingrid Zukerman School of Computer Science and Software Engineering Monash University Clayton, VICTORIA 3800, AUSTRALIA {yuvalm,email@example.com information
Research on News Video Multi-topic Extraction and Summarization
International Journal of New Technology and Research (IJNTR) ISSN:2454-4116, Volume-2, Issue-3, March 2016 Pages 37-39 Research on News Video Multi-topic Extraction and Summarization Di Li, Hua Huo AbstractMore information
Intrusion Detection via Machine Learning for SCADA System Protection
Intrusion Detection via Machine Learning for SCADA System Protection S.L.P. Yasakethu Department of Computing, University of Surrey, Guildford, GU2 7XH, UK. firstname.lastname@example.com J. Jiang DepartmentMore information
Web-Scale Extraction of Structured Data Michael J. Cafarella, Jayant Madhavan & Alon Halevy
The Deep Web: Surfacing Hidden Value Michael K. Bergman Web-Scale Extraction of Structured Data Michael J. Cafarella, Jayant Madhavan & Alon Halevy Presented by Mat Kelly CS895 Web-based Information RetrievalMore information
Predicting the Stock Market with News Articles
Predicting the Stock Market with News Articles Kari Lee and Ryan Timmons CS224N Final Project Introduction Stock market prediction is an area of extreme importance to an entire industry. Stock price isMore information
CI6227: Data Mining. Lesson 11b: Ensemble Learning. Data Analytics Department, Institute for Infocomm Research, A*STAR, Singapore.
CI6227: Data Mining Lesson 11b: Ensemble Learning Sinno Jialin PAN Data Analytics Department, Institute for Infocomm Research, A*STAR, Singapore Acknowledgements: slides are adapted from the lecture notesMore information
Simple Language Models for Spam Detection
Simple Language Models for Spam Detection Egidio Terra Faculty of Informatics PUC/RS - Brazil Abstract For this year s Spam track we used classifiers based on language models. These models are used toMore information
Natural Language to Relational Query by Using Parsing Compiler
Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 4, Issue. 3, March 2015,More information
A Mutually Beneficial Integration of Data Mining and Information Extraction
In the Proceedings of the Seventeenth National Conference on Artificial Intelligence(AAAI-2000), pp.627-632, Austin, TX, 20001 A Mutually Beneficial Integration of Data Mining and Information ExtractionMore information
RRSS - Rating Reviews Support System purpose built for movies recommendation
RRSS - Rating Reviews Support System purpose built for movies recommendation Grzegorz Dziczkowski 1,2 and Katarzyna Wegrzyn-Wolska 1 1 Ecole Superieur d Ingenieurs en Informatique et Genie des TelecommunicatiomMore information
Robustness of a Spoken Dialogue Interface for a Personal Assistant
Robustness of a Spoken Dialogue Interface for a Personal Assistant Anna Wong, Anh Nguyen and Wayne Wobcke School of Computer Science and Engineering University of New South Wales Sydney NSW 22, AustraliaMore information
Parsing Software Requirements with an Ontology-based Semantic Role Labeler
Parsing Software Requirements with an Ontology-based Semantic Role Labeler Michael Roth University of Edinburgh email@example.com Ewan Klein University of Edinburgh email@example.com Abstract SoftwareMore information
Enriching the Crosslingual Link Structure of Wikipedia - A Classification-Based Approach -
Enriching the Crosslingual Link Structure of Wikipedia - A Classification-Based Approach - Philipp Sorg and Philipp Cimiano Institute AIFB, University of Karlsruhe, D-76128 Karlsruhe, Germany {sorg,email@example.com information
Phase 2 of the D4 Project. Helmut Schmid and Sabine Schulte im Walde
Statistical Verb-Clustering Model soft clustering: Verbs may belong to several clusters trained on verb-argument tuples clusters together verbs with similar subcategorization and selectional restrictionMore information
Testing Data-Driven Learning Algorithms for PoS Tagging of Icelandic
Testing Data-Driven Learning Algorithms for PoS Tagging of Icelandic by Sigrún Helgadóttir Abstract This paper gives the results of an experiment concerned with training three different taggers on taggedMore information
A Mixed Trigrams Approach for Context Sensitive Spell Checking
A Mixed Trigrams Approach for Context Sensitive Spell Checking Davide Fossati and Barbara Di Eugenio Department of Computer Science University of Illinois at Chicago Chicago, IL, USA email@example.com, firstname.lastname@example.com information
A Semantic Feature for Verbal Predicate and Semantic Role Labeling using SVMs
A Semantic Feature for Verbal Predicate and Semantic Role Labeling using SVMs Hansen A. Schwartz and Fernando Gomez and Christopher Millward School of Electrical Engineering and Computer Science UniversityMore information
Tibetan-Chinese Bilingual Sentences Alignment Method based on Multiple Features
, pp.273-280 http://dx.doi.org/10.14257/ijdta.2015.8.4.27 Tibetan-Chinese Bilingual Sentences Alignment Method based on Multiple Features Lirong Qiu School of Information Engineering, MinzuUniversity ofMore information
Architecture of an Ontology-Based Domain- Specific Natural Language Question Answering System
Architecture of an Ontology-Based Domain- Specific Natural Language Question Answering System Athira P. M., Sreeja M. and P. C. Reghuraj Department of Computer Science and Engineering, Government EngineeringMore information
Mining the Software Change Repository of a Legacy Telephony System
Mining the Software Change Repository of a Legacy Telephony System Jelber Sayyad Shirabad, Timothy C. Lethbridge, Stan Matwin School of Information Technology and Engineering University of Ottawa, Ottawa,More information
A Framework for Ontology-Based Knowledge Management System
A Framework for Ontology-Based Knowledge Management System Jiangning WU Institute of Systems Engineering, Dalian University of Technology, Dalian, 116024, China E-mail: firstname.lastname@example.com Abstract KnowledgeMore information
Learning to Identify Emotions in Text
Learning to Identify Emotions in Text Carlo Strapparava FBK-Irst, Italy email@example.com Rada Mihalcea University of North Texas firstname.lastname@example.com ABSTRACT This paper describes experiments concerned with theMore information
Protein Protein Interaction Networks
Functional Pattern Mining from Genome Scale Protein Protein Interaction Networks Young-Rae Cho, Ph.D. Assistant Professor Department of Computer Science Baylor University it My Definition of BioinformaticsMore information
A Search Engine for Natural Language Applications
A Search Engine for Natural Language Applications Michael J. Cafarella Department of Computer Science and Engineering University of Washington Seattle, WA 98195 U.S.A. email@example.com Oren EtzioniMore information
Customer Intentions Analysis of Twitter Based on Semantic Patterns
Customer Intentions Analysis of Twitter Based on Semantic Patterns Mohamed Hamroun firstname.lastname@example.com Mohamed Salah Gouider email@example.com Lamjed Ben Said email@example.com ABSTRACTMore information
Term extraction for user profiling: evaluation by the user
Term extraction for user profiling: evaluation by the user Suzan Verberne 1, Maya Sappelli 1,2, Wessel Kraaij 1,2 1 Institute for Computing and Information Sciences, Radboud University Nijmegen 2 TNO,More information
Clustering Connectionist and Statistical Language Processing
Clustering Connectionist and Statistical Language Processing Frank Keller firstname.lastname@example.com Computerlinguistik Universität des Saarlandes Clustering p.1/21 Overview clustering vs. classification supervisedMore information
Modern Natural Language Interfaces to Databases: Composing Statistical Parsing with Semantic Tractability
Modern Natural Language Interfaces to Databases: Composing Statistical Parsing with Semantic Tractability Ana-Maria Popescu Alex Armanasu Oren Etzioni University of Washington David Ko {amp, alexarm, etzioni,More information
THE digital age, also referred to as the information
JOURNAL TKDE 1 Survey on Aspect-Level Sentiment Analysis Kim Schouten and Flavius Frasincar Abstract The field of sentiment analysis, in which sentiment is gathered, analyzed, and aggregated from text,More information
Information Retrieval Statistics of Text
Information Retrieval Statistics of Text James Allan University of Massachusetts, Amherst (NTU ST770-A) Fall 2002 All slides copyright Bruce Croft and/or James Allan Outline Zipf distribution VocabularyMore information
Open Information Extraction: the Second Generation
Open Information Extraction: the Second Generation Oren Etzioni, Anthony Fader, Janara Christensen, Stephen Soderland, and Mausam Turing Center Department of Computer Science and Engineering UniversityMore information
Course: Model, Learning, and Inference: Lecture 5
Course: Model, Learning, and Inference: Lecture 5 Alan Yuille Department of Statistics, UCLA Los Angeles, CA 90095 firstname.lastname@example.com Abstract Probability distributions on structured representation.More information
How the Computer Translates. Svetlana Sokolova President and CEO of PROMT, PhD.
Svetlana Sokolova President and CEO of PROMT, PhD. How the Computer Translates Machine translation is a special field of computer application where almost everyone believes that he/she is a specialist.More information
Document Image Retrieval using Signatures as Queries
Document Image Retrieval using Signatures as Queries Sargur N. Srihari, Shravya Shetty, Siyuan Chen, Harish Srinivasan, Chen Huang CEDAR, University at Buffalo(SUNY) Amherst, New York 14228 Gady Agam andMore information
Data Mining Yelp Data - Predicting rating stars from review text
Data Mining Yelp Data - Predicting rating stars from review text Rakesh Chada Stony Brook University email@example.com Chetan Naik Stony Brook University email@example.com ABSTRACT The majorityMore information
Incorporating Window-Based Passage-Level Evidence in Document Retrieval
Incorporating -Based Passage-Level Evidence in Document Retrieval Wensi Xi, Richard Xu-Rong, Christopher S.G. Khoo Center for Advanced Information Systems School of Applied Science Nanyang TechnologicalMore information
A Two-Pass Statistical Approach for Automatic Personalized Spam Filtering
A Two-Pass Statistical Approach for Automatic Personalized Spam Filtering Khurum Nazir Junejo, Mirza Muhammad Yousaf, and Asim Karim Dept. of Computer Science, Lahore University of Management SciencesMore information
Experiments in Web Page Classification for Semantic Web
Experiments in Web Page Classification for Semantic Web Asad Satti, Nick Cercone, Vlado Kešelj Faculty of Computer Science, Dalhousie University E-mail: {rashid,nick,firstname.lastname@example.com Abstract We addressMore information
Accelerating and Evaluation of Syntactic Parsing in Natural Language Question Answering Systems
Accelerating and Evaluation of Syntactic Parsing in Natural Language Question Answering Systems cation systems. For example, NLP could be used in Question Answering (QA) systems to understand users naturalMore information
Training Set Properties and Decision-Tree Taggers: A Closer Look
Training Set Properties and Decision-Tree Taggers: A Closer Look Brian R. Hirshman Department of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 firstname.lastname@example.com Abstract This paperMore information
Bagged Ensemble Classifiers for Sentiment Classification of Movie Reviews
www.ijecs.in International Journal Of Engineering And Computer Science ISSN:2319-7242 Volume 3 Issue 2 February, 2014 Page No. 3951-3961 Bagged Ensemble Classifiers for Sentiment Classification of MovieMore information
A Comparative Study on Sentiment Classification and Ranking on Product Reviews
A Comparative Study on Sentiment Classification and Ranking on Product Reviews C.EMELDA Research Scholar, PG and Research Department of Computer Science, Nehru Memorial College, Putthanampatti, BharathidasanMore information
Statistical Validation and Data Analytics in ediscovery. Jesse Kornblum
Statistical Validation and Data Analytics in ediscovery Jesse Kornblum Administrivia Silence your mobile Interactive talk Please ask questions 2 Outline Introduction Big Questions What Makes Things Similar?More information
Making Sense of the Mayhem: Machine Learning and March Madness
Making Sense of the Mayhem: Machine Learning and March Madness Alex Tran and Adam Ginzberg Stanford University firstname.lastname@example.com firstname.lastname@example.com I. Introduction III. Model The goal of our researchMore information
Recognizing Informed Option Trading
Recognizing Informed Option Trading Alex Bain, Prabal Tiwaree, Kari Okamoto 1 Abstract While equity (stock) markets are generally efficient in discounting public information into stock prices, we believeMore information
ANNLOR: A Naïve Notation-system for Lexical Outputs Ranking
ANNLOR: A Naïve Notation-system for Lexical Outputs Ranking Anne-Laure Ligozat LIMSI-CNRS/ENSIIE rue John von Neumann 91400 Orsay, France email@example.com Cyril Grouin LIMSI-CNRS rue John von Neumann 91400More information
Boosting the Feature Space: Text Classification for Unstructured Data on the Web
Boosting the Feature Space: Text Classification for Unstructured Data on the Web Yang Song 1, Ding Zhou 1, Jian Huang 2, Isaac G. Councill 2, Hongyuan Zha 1,2, C. Lee Giles 1,2 1 Department of ComputerMore information
Taxonomies in Practice Welcome to the second decade of online taxonomy construction
Building a Taxonomy for Auto-classification by Wendi Pohs EDITOR S SUMMARY Taxonomies have expanded from browsing aids to the foundation for automatic classification. Early auto-classification methodsMore information
Recovering Semantics of Tables on the Web
Recovering Semantics of Tables on the Web Petros Venetis Alon Halevy Jayant Madhavan Marius Paşca Stanford University Google Inc. Google Inc. Google Inc. firstname.lastname@example.com email@example.com firstname.lastname@example.com information
Towards Automatic Animated Storyboarding
Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence (2008) Towards Automatic Animated Storyboarding Patrick Ye and Timothy Baldwin Computer Science and Software Engineering NICTAMore information
Overview of MT techniques. Malek Boualem (FT)
Overview of MT techniques Malek Boualem (FT) This section presents an standard overview of general aspects related to machine translation with a description of different techniques: bilingual, transfer,More information
Spot me if you can: Uncovering spoken phrases in encrypted VoIP conversations
Spot me if you can: Uncovering spoken phrases in encrypted VoIP conversations C. Wright, L. Ballard, S. Coull, F. Monrose, G. Masson Talk held by Goran Doychev Selected Topics in Information Security andMore information
Generating Query Substitutions
1 Generating Query Substitutions Rosie Jones Benjamin Rey, Omid Madani and Wiley Greiner Substitutes 2 Query Substitutions 3 Query Substitutions 4 Functions of Rewriting Enhance meaning Spell correctionMore information
Towards SoMEST Combining Social Media Monitoring with Event Extraction and Timeline Analysis
Towards SoMEST Combining Social Media Monitoring with Event Extraction and Timeline Analysis Yue Dai, Ernest Arendarenko, Tuomo Kakkonen, Ding Liao School of Computing University of Eastern Finland {yvedai,More information
Efficient Techniques for Improved Data Classification and POS Tagging by Monitoring Extraction, Pruning and Updating of Unknown Foreign Words
, pp.290-295 http://dx.doi.org/10.14257/astl.2015.111.55 Efficient Techniques for Improved Data Classification and POS Tagging by Monitoring Extraction, Pruning and Updating of Unknown Foreign Words IrfanMore information
CINDOR Conceptual Interlingua Document Retrieval: TREC-8 Evaluation.
CINDOR Conceptual Interlingua Document Retrieval: TREC-8 Evaluation. Miguel Ruiz, Anne Diekema, Páraic Sheridan MNIS-TextWise Labs Dey Centennial Plaza 401 South Salina Street Syracuse, NY 13202 Abstract:More information
M A C H I N E L E A R N I N G P R O J E C T F I N A L R E P O R T F A L L 2 7 C S 6 8 9 CLASSIFICATION OF TRADING STRATEGIES IN ADAPTIVE MARKETS MARK GRUMAN MANJUNATH NARAYANA Abstract In the CAT Tournament,More information
Multi-source hybrid Question Answering system
Multi-source hybrid Question Answering system Seonyeong Park, Hyosup Shim, Sangdo Han, Byungsoo Kim, Gary Geunbae Lee Pohang University of Science and Technology, Pohang, Republic of Korea {sypark322,More information
A Systematic Cross-Comparison of Sequence Classifiers
A Systematic Cross-Comparison of Sequence Classifiers Binyamin Rozenfeld, Ronen Feldman, Moshe Fresko Bar-Ilan University, Computer Science Department, Israel firstname.lastname@example.com, firstname.lastname@example.com,More information
Three types of messages: A, B, C. Assume A is the oldest type, and C is the most recent type.
Chronological Sampling for Email Filtering Ching-Lung Fu 2, Daniel Silver 1, and James Blustein 2 1 Acadia University, Wolfville, Nova Scotia, Canada 2 Dalhousie University, Halifax, Nova Scotia, CanadaMore information
Identifying free text plagiarism based on semantic similarity
Identifying free text plagiarism based on semantic similarity George Tsatsaronis Norwegian University of Science and Technology Department of Computer and Information Science Trondheim, Norway email@example.com information
6.2.8 Neural networks for data mining
6.2.8 Neural networks for data mining Walter Kosters 1 In many application areas neural networks are known to be valuable tools. This also holds for data mining. In this chapter we discuss the use of neuralMore information
Discovering and Querying Hybrid Linked Data
Discovering and Querying Hybrid Linked Data Zareen Syed 1, Tim Finin 1, Muhammad Rahman 1, James Kukla 2, Jeehye Yun 2 1 University of Maryland Baltimore County 1000 Hilltop Circle, MD, USA 21250 firstname.lastname@example.com,More information
Mining Opinion Features in Customer Reviews
Mining Opinion Features in Customer Reviews Minqing Hu and Bing Liu Department of Computer Science University of Illinois at Chicago 851 South Morgan Street Chicago, IL 60607-7053 {mhu1, email@example.com information


text:
|Message view
|« Date » · « Thread »
|« Date » · « Thread »
|James Green <james.mk.gr...@gmail.com>
|Re: maven-failsafe-plugin: what is it actually intended for?
|Fri, 15 Nov 2013 11:06:33 GMT
On 15 November 2013 06:57, Vincent Latombe <email@example.com>wrote: > maven-failsafe-plugin implies some specific steps are required *before* > (pre-integration-test) and *after* (post-integration-test) the test > execution, which allows to do fire up a container then turn it off after > the tests (for example). > > Then, only after these, the test results are evaluated during 'verify' > phase and will fail the build if needed. > > This is unlike maven-surefire-plugin, that performs both the tests > execution and verification in the same step, which prevents you from doing > any kind of cleanup outside of test execution. > > So, using failsafe or surefire boils down to a single question : do you > need to perform any set up/teardown outside of the test execution > framework? If yes, use failsafe, if no, use surefire > That provides ambiguity since junit supplies before and after routines for exactly the same reason but in a very different context. I'm keen to see the knowledge from this thread make it into the documentation. What procedures exist to allow this? Thanks, James
|View raw message


text:
Good morning from The Oval. President Obama has landed in Seoul for a G-20 economic summit that figures to include tough talks with China and intense debates over how to address the global slowdown.
During his just-completed trip to Indonesia, Obama said the G-20 should endorse "a balanced" approach between stimulus spending designed to spark the economy and austerity programs needed to reduce budget deficits.
"We have not yet achieved that balanced growth," Obama said.
Obama also said China continues to unfairly manipulate its currency to get trade advantages; he is scheduled to meet with Chinese President Hu Jintao while in Seoul.
In addition, the president will have to deal with global criticism of the Federal Reserve's decision to pump billions into the U.S. economy.
In other news:
Obama delivered a major speech in Jakarta, in another effort to make outreach to Muslims, reports Calum MacLeod of USA TODAY.
The annual salaries of more and more federal workers are topping $150,000 a year, says USA TODAY's Dennis Cauchon.
U.S. and Afghanistan forces are making bombmaking more difficult by seizing a key ingredient, USA TODAY's Tom Vanden Brook writes.
The Republicans did very well in the elections, but members are trying to ease out controversial party chair Michael Steele.
(Posted by David Jackson)
David's journalism career spans three decades, including coverage of five presidential elections, the Oklahoma City bombing, the 2000 Florida presidential recount and the 9/11 terrorist attacks. He has covered the White House for USA TODAY since 2005. His interests include history, politics, books, movies and college football -- not necessarily in that order. More about David
Follow the Oval on Twitter: @The Oval, @djusatoday.
Also contributing to the Oval: Richard Wolf @richardjwolf; Aamer Madhani @AamerISmad


text:
I Will Stand Out of the Crowd
Personal Stories, Advice, and Support 15 People
OutstagedLiving in Hawaii I have always tended to stand out no matter where I go, this is because of the whiteness of my skin and the loudness of my mouth. But I will be going away to college soon and consequently sucked into the vast majority of the white student body. So I would like to...
smflinchbaugh 18-21, F 0 Jan 3, 2009


text:
Registration takes less than 1 minute.
The world's largest container line is one of many companies and organizations that have reported being hit by a cyber attack this week.
The Port of Los Angeles and General Electric recently launched an ambitious data portal pilot program that will digitize the movement of shipment information in an effort to improve the flow of goods at the nation's busiest seaport.
Chairs of the House PORTS Caucus - Congressmen Alan Lowenthal (CA-47) and Ted Poe (TX-02) - sent a letter to DHS Secretary John Kelly to get a classified understanding on the country's ports' cyber protection plans.


text:
One of the main advantages that can be recognized through condominium dwelling is comfort. Unlike a detached family home that is single, when residing in a condo, you don't have all the associated and landscaping duties that may be quite time consuming. See at website to learn about jewel dwelling.
Coechegoyen41`s Uploads
scotty.goodman1 from Miami Beach joined
- coechegoyen41
- profile viewed 13 times


text: Opened in 2004, this air-conditioned beach hotel is situated in extensive gardens and is surrounded by swimming pools and sun terraces. The hotel comprises a total of 613 rooms. The inviting lobby features a 24-hour reception desk, lifts, a safe and a currency exchange facility. The pleasant complex also features a caf�, a small supermarket, various shops, a hairdressing salon, a bar, a nightclub, and a TV room. Furthermore there is an air-conditioned à la carte restaurant with a non-smoking area and highchairs available for children, and a public Internet connection. Laundry and medical services are available on request. Younger guests may let off steam in the playground as well as participate in kids' club activities. There is a car park for guests arriving by car.Each room comes with an en suite bathroom, with a bath, shower and hairdryer, a direct dial telephone, satellite/ cable TV, individually adjustable air conditioning and central heating. A minibar, a small fridge, a hire safe, and a balcony or terrace also feature as standard.


text:
Audio products can be downloaded instantly after purchase. Video products are on DVD and will be shipped in the United States for $4.95. Additional costs may apply for international orders. Note: Audio downloads will not download directly to Apple portable devices. Download the audio file to your desktop computer first, copy it into iTunes and then sync your device.
Audio Series: Meditations and Programs
Journey to the Other Side
A true story. During a meditation Echo asked God to let her view what is on the other side of the veil. Two days later she found herself on a sacred journey for 45 minutes as her soul was taken from her body and given an Angel-guided tour of what lies beyond. This recording is a recollection of that event set to beautiful original music by Twin Cities keyboardist Larry David.
mp3 download
A Healing with the Angels
Echo's all-time classic meditation. One of the first and one of the favorites.
mp3 download
Getting Unstuck
Feeling stuck in your health, work, relationships or other areas of your life? This gentle guided process by Echo taps your imagination with imagery of colorful balloons
to help you release what no longer serves you.
mp3 download
A Healing For The Body
The next best thing to a personal healing session with Echo. Lie down, relax and let Echo guide a process of grounding and clearing as she prepares you to receive the warm healing energy being directed to your body.
mp3 download
Healing Your Grief
In this gentle process Echo talks about your loved one and their passage to the other side. She helps you understand the connection you still have with them and guides and invitation for them to come and talk with you.
mp3 download
Into the Forest
Be guided on a unique inner journey to encounter, perceive and heal different aspects of yourself. Breathe the essence of the forest and feel the protection and deep wisdom of the trees and earth as you are nurtured and enfolded by the magic of life.
mp3 download
Wisdom of the Elders
Go to a special place deep inside to experience and blend with the ancient wisdom of the Elder Spirits. Be nourished in this sacred atmosphere of light, wisdom and oneness with the Universe.
mp3 download
Short and sweet. Relax in peace as Echo's soothing voice quiets your mind and emotions, relaxes your body and calms your spirit. The beautiful music of Larry David fades slowly away as you drift off to sleep.
mp3 download
The God Within
A powerful meditation that gives you an opportunity to feel and know the incredible essence of God within. Within this sacred space you can talk to God and listen for answers. It is here you will come to know and understand your joy and unique Oneness with Creator.
mp3 download
Touched by a Master
A moving and healing experience is yours as Echo helps you connect to a loving Master in Spirit.
mp3 download
Healing the Heart of Your Soul
This is a guided journey back in time to past lives and the old pain that may still need healing. In this process Echo will assist you with a process to release the old pain and open your heart to healing.
mp3 download
Let it Go
Another one of Echo's popular and classic meditations. The name says it all.
mp3 download
Opening Your Psychic Abilities
In this exercise Echo will guide you through the basic process of opening up psychically so you can practice developing your spiritual gifts.
mp3 download
Relax and Release
Find a comfortable chair, take a deep breath and let Echo guide you to a place of welcome relaxation. Experience the release of stress from your body and life as you make this journey into stillness.
mp3 download
Guidance from the Light Within
In this brilliant and comforting meditation Echo takes you on a very sacred jorney deep inside yourself. Each time you visit this special place you will be rejuvenated by the love and wisdom of the Inner Light that is within you.
mp3 download
Healing the Inner Child
In this meditation Echo guides you to a place of deep healing as you bring to your inner child the guidance and protection you are now able to offer.
mp3 download
An Evening with Echo Bodine
150 minutes recorded live during a conference presentation with stories about Echo's Life, The Soul's Journey, Ghosts, Life After Death, and lots of other fascinating topics. Double download.
mp3 download
Living from your Intuition
Intuition: What is it? Where do we connect with it in the body? How do we listen to it? How do we tell the difference between this voice and personal emotions? How can we trust the inner messages when we receive so many mixed messages from others in the world? Speaking straight from her heart, Echo answers these questions and dozens more in this 4-hour recording of her popular intuition workshop.
mp3 download
Video Series: Classes and Information
Healing Hands; The Universal Art of Laying-on-Hands Healing
This 2-disc video brings Echo's weekend training seminar into your home. With step by step guidance you will learn the art of Laying-in-Hands healing as taught by this experienced practitioner.
Psychic Development Class for Beginners
Enjoy over four hours of engaging, insightful instruction as Echo takes you through what you need to know to open up your psychic gifts.
$49.95 Currently Unavailable
Echo's Mirror Necklaces
If you've read any of Echo's books, you know about mirror necklaces. They are very helpful in protecting us from negative energy.
Other Items
Check out http://moonwisdom.com for some of the helpful products often recommended by Echo. They have Palo Santo wood incense, sage mist, oils and much more.
We Accept It!
We accept VISA, Mastercard, American Express, and Discover cards using the secure PayPal shopping cart system. You do NOT need a PayPal account to make your purchases although we suggest setting one up for ease in using in thousands of sites around the internet.


text:
Gumpaste Figurine Book
Decorating By lv2bake Updated 3 May 2008 , 1:19am by lv2bake
I am not sure if I have seen this here or not but there is a book I am trying to find, I think the author name is Maisy?? not sure but it is a book that deals with making figures out of gumpaste, on the cover it has a little chef, with pots and pans. I seen it on someones website but not sure whose. If anyone know of the book please PM me. Thank you.
Maisie Parrish has a book out called "Cakes with Character" that has figures in it. She also has another one which I forget the name of, but this is her newest. Does that help?
Yes, that was the one, Thank you very much
Quote by @%username% on %date%


text:
Libby and Stephen were hitched in September at Tournerbury Woods Estate on the Hampshire/ West Sussex border. They didn't set out to have a theme, but in throwing together all the things they liked it ended up being a super colourful, festival-esque day.
"Our wedding was an expression of our personalities and everything we love", explained the bride. "We also had lots of colour! We wanted to create a day that was going to be fun for all our loved ones."
"We were true to ourselves and didn't have a theme and we created our own ceremony from scratch with our friend Harry. At the venue, they had this beautiful black metal pagoda which we decorated with ribbon and brightly coloured pots with beautiful plants and pink flamingos. Our guests were all sat on hay bales with crochet blankets sourced from various car boots and charity shops. As I walked down the aisle with my Dad and bridesmaids ahead of me to James Vincent McMorrow's cover of Higher Love the sun came out and apparently dragonflies were flying around the guests! I was oblivious at this point as I must admit I was a little nervous."


text:
The Hong Kong Special Administrative Region (HKSAR) Government has announced that the Creative Industries sector is to form one of the new six pillars to help Hong Kong's economy.
Sector briefing Creative Industries Opportunities in Hong Kong & Macao Why Hong Kong? The Hong Kong Special Administrative Region (HKSAR) Government has announced that the Creative Industries sector is to form one of the new six pillars to help Hong Kong?s economy. The sector is seen as an economic force in itself with around 32,000 creative industry related establishments and more than 170,000 employees. Hong Kong has developed a leading edge in key areas of creative industries such as film, television, music, design, architecture, comics and animation, games and digital entertainment, etc. Together they contribute an added value to Hong Kong?s GDP of more than $60 billion annually, or around 4% of the total GDP. Find general information on Hong Kong market conditions on UKTI?s website. The Doing Business Guide for Hong Kong gives an overview of Hong Kong?s economy, business culture, potential opportunities and an introduction to other relevant issues. UK Trade & Investment Sector briefing: Creative Industries Opportunities in Hong Kong and Macao Opportunities As Opportunities for UK Creative Industries part of the continuing promotion and suppor companies t of the sector, the HKSAR Government se t up CreateHK in June 2009. This o ? Product, graphic and interior design rganisation provides one-stop services and be ? Architectural and building design tter support to the creative industries sector. ? Co-productions in TV and film It is responsible for the administration and managemen ? Technical skills in the screen t of Government funded schemes industries, inc animation and related to Creative Industries. These include; production D ? Branding esignSmart Initiative: The $250 million ? Advertising (£20 million) DesignSmart Initiative, launched ? Art, antiques and crafts in June 2004, aims to strengthen Government ? Film and video support for design and innovation, and to ? Performing Arts promote the wider use of design and ? Publishing innovation in industries to help them move up ? TV and radio the value chain. ? Music The Film Development Fund (Administered by the Secretariat of the HK Film Macao does not have the broad spread of Development Council) Hong Kong in this important sector, however with the sheer number of casinos and more The Film Development Fund (FDF) was first set being built, opportunities exist in up by the Government in 1999 to support design/architecture and in the various shows projects conducive to the long-term the hotels put on. development of the film industry in Hong Kong. In July 2007, the Government injected $300 If you have any questions on the opportunities million (£24 million) into the FDF, and above, please contact the UKTI contacts named expanded its scope to finance small-to-medium in this report. Business opportunities aimed budget film productions. specifically at UK companies are added daily to UKTI?s website. These leads are sourced by our The HKSARG has also invested in a number of staff overseas in British Embassies, High initiatives, including the Hong Kong Design Commissions and Consulates, across all sectors Centre and the InnoCentre, Hong Kong?s first and in over 100 markets. facility aimed at cultivating a cluster of high- value added design activities among design You can be alerted to business opportunities on professional and user industries. The a regular basis by registering on the UKTI Government has earmarked HK$100 million website. Find out more on UKTI?s business (£8 million) to support the Hong Kong Design opportunities service on the UKTI website. Centre and to make the InnoCentre a magnet for design activities. Hong Kong?s creative talents in the fields of film animation, comics, movies and music have won international acclaim. Hong Kong is also the prime strategic gateway for UK companies wishing to develop their business in the Pearl River Delta (PRD) and wider Mainland markets, particularly for advertising, design and film production. UK Trade & Investment Sector briefing: Creative Industries Opportunities in Hong Kong and Macao Major events and activities UKTI contacts John Woodruffe UK Deputy Trade Commissioner Group at Filmart (Hong Kong) www.hkf British Consulate General ilmart.com Contact: Hong Kong John Woodruffe Ema Tel: (852) 2901 3355 il: email@example.com T Email: firstname.lastname@example.com ime: March 2012 www.ukinhongkong.fco.gov.uk Inno Catherine Lee Design Tech Expo www Trade Adviser .hktdc.com/innodesigntechexpo Contact: John Woodruffe British Consulate General Ema Hong Kong il: email@example.com T Tel: (852) 2901 2353 ime: 1-3 December 2011 Email: firstname.lastname@example.com www.ukinhongkong.fco.gov.uk F ind full details of all events in this country Christine Davey and sector on the UKTI website. New export events are added da Creative Industries, Industry Sector Group ily to the site and you can reg UKTI London ister to be alerted to them on a da Tel: +44 (0)20 7215 8000 ily, weekly or monthly basis Email: email@example.com UKTI?s Tradeshow Access Programme (TAP) provides grant support for eligible Small & Medium Sized Enterprises (SME's) to attend trade shows overseas. Find out more about UKTI support for attendance at overseas events. UK Trade & Investment Sector briefing: Creative Industries Opportunities in Hong Kong and Macao Next steps - How UKTI can help ? Arranging appointments British companies wishing to develop their ? Organise seminars or other events for business in the Hong Kong market are you to meet contacts and promote your advised to undertake as much market research company in the local market and planning as possible in the UK. UKTI?s team in Hong Kong, with its wide local This work is available via our Overseas Market knowledge and experience, can provide a Introduction Service (OMIS) a chargeable range of services to British-based companies service which assists British-based companies wishing to grow their business in global m wishing to enter or expand their business in arkets. overseas markets. This can include: To find out more about commissioning this ? Provision of market information work, or accessing other UKTI services and ? Validated lists of agents/distributors specialist advice, please visit the UKTI website ? Key market players or potential to find contact details for your local UKTI customers in the local market office. ? Establishment of interest of such contacts in working with you Whereas every effort has been made to ensure that the information given in this document is accurate, neither UK Trade & Investment nor its parent Departments (the Department for Business, Innovation & Skills, and the Foreign & Commonwealth Office), accept liability for any errors, omissions or misleading statements, and no warranty is given or responsibility accepted as to the standing of any individual, firm, company or other organisation mentioned. Published 2011 by UK Trade & Investment. Crown Copyright © You may reuse this information (not including logos, images and case studies) free of charge in any format or medium, under the terms of the Open Government Licence. To view this licence, visit www.nationalarchives.gov.uk/doc/open-government- licence/ or write to the Information Policy Team, The National Archives, Kew, London TW9 4DU, or email: email@example.com . This publication is also available from our website at www.ukti.gov.uk or for more information please telephone +44 (0)20 7215 8000. UK Trade & Investment Sector briefing: Creative Industries Opportunities in Hong Kong and Macao


text:
It's not like 2014 Manning Award winner Marcus Mariota of Oregon is a stranger to the family for whom the honor that goes to the nation's top college quarterback is named.
For the past three years Mariota was one of the counselors at the Manning Passing Academy in Thibodaux where he not only worked with the youngsters who attend but got to know many of the nation's other top quarterbacks, including 2013 winner and fellow Heisman Trophy recipient Jameis Winston of Florida State.
"I had a great experience at the Manning Camp," Mariota said Friday, two days after announcing he would forego his senior season to enter the NFL draft. "It was awesome working with the kids.
"You felt like you were part of a great team and part of a great family."
Mariota's final collegiate season ended in disappointment Monday with the Ducks' 42-20 loss to Ohio State in the College Football Playoff championship game.
But even though the Manning Award, sponsored by the Allstate Sugar Bowl, takes into account a quarterback's bowl performance, it did little to diminish Mariota's accomplishments.
He set Pac-12 records for total touchdowns (58) and total offense (5,224 yards) this season while becoming the first quarterback in FCS history to have a 50-plus TD to interception ratio.
In the title game Mariota threw for 333 yards and two TDs. He had 338 yards passing with two scores in the Ducks' 59-20 Rose Bowl victory against Winston and Florida State in the semifinals.
In his career, Mariota led the Ducks to a 36-5 record and set an NCAA record by having at least one touchdown pass in all of his starts.
"It's been so much fun watching Marcus grow as a quarterback," Archie Manning said. "You have to be proud of the way he has conducted himself off the field and for his accomplishments on it.
"He was kind of quiet and reserved when he first came to our camp, but he's blossomed there, too. I don't think we've had a finer person win this award."
Mariota said he is in San Diego beginning training for the draft.
Most projections have him as the No. 1 pick in the draft. That spot belongs to Tampa Bay, but there are reports that Chip Kelly, Mariota's old coach at Oregon, is pushing for the Philadelphia Eagles to trade up for the opportunity to take him.
"I'm not too concerned about if I'm the No. 1 pick or not," Mariota said. "The main thing is going to where I fit best."
Mariota is the 11th winner of the Manning Award and the seventh to have played in the national championship game.
He is the first from the Pac 12 since Matt Leinart of Southern California, the award's inaugural winner in 2004.
Along with Mariota, this year's other Manning Award finalists were J.T. Barrett of Ohio State, Trevone Boykin of TCU, Rakeem Cato of Marshall, Garrett Grayson of Colorado State, Brett Hundley of UCLA, Cody Kessler of USC, Bryce Petty of Baylor, Dak Prescott of Mississippi State, Blake Sims of Alabama and Winston.
Mariota will be presented his award at a luncheon in New Orleans at a future date.