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Systems and techniques for generating language from an input use a symbolic generator and a statistical ranker. The symbolic generator may use a transformation algorithm to transform one or more portions of the input. For example, mapping rules such as morph rules, recasting rules, filling rules, and/or ordering rules may be used. The symbolic generator may output a plurality of possible expressions, while the statistical ranker may rank at least some of the possible expressions to determine the best output.
1. A method, comprising:
receiving an input representing one or more ideas to be expressed;
transforming at least a portion of the input using a recasting rule;
transforming at least a portion of the input using a morph rule;
producing a plurality of possible expressions for the one or more ideas based on the transforming;
ranking at least some of the one or more possible expressions; and
producing an output sentence expressing the one or more ideas based on the ranking.
2. The method of
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6. A system, comprising:
a symbolic generator to receive input representing one or more ideas to be expressed, the symbolic generator to process the input according to mapping rules including one or more recasting rules and one or more morph rules, the symbolic generator to produce a plurality of possible expressions based on the processing; and
a statistical ranker to determine the best choice of the plurality of possible expressions.
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22. An apparatus comprising:
means for transforming a portion of an input including a relation into a new portion including a different relation;
means for adding an additional portion to the input;
means for transforming a second portion of the input to produce a morphologically inflected portion;
means for ordering portions of the input; and
means for producing a plurality of possible expressions based on the input.
23. The apparatus of
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29. The apparatus of
means for statistically ranking at least some of the plurality of possible expressions; and
means for producing an output sentence based on the statistical ranking.
30. An article comprising a machine-readable medium storing instructions operable to cause one or more machines to perform operations comprising:
receiving an input representing one or more ideas to be expressed;
transforming at least a portion of the input using a recasting rule;
transforming at least a portion of the input using a morph rule;
producing a plurality of possible expressions for the one or more ideas based on the transforming;
ranking at least some of the one or more possible expressions; and
producing an output sentence expressing the one or more ideas based on the ranking.
31. The article of
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[0001] This application claims priority to U.S. Provisional Application Serial No. 60/361,757, filed Mar. 4, 2002, entitled "HALOGEN STATISTICAL SENTENCE GENERATOR," which is hereby incorporated by reference.
[0002] This invention was made with Government support under National Science Foundation Award Number 9820291. The Government has certain rights in this invention.
[0003] This invention relates to language generation.
[0004] From early computing days, computers have been used to process and generate human language. Early efforts focused on machine translation, while today the use of natural language generation has expanded to encompass a wide variety of applications.
[0005] For example, sentence generation may be used to enable human-computer dialogue, summarization, report creation, automatic technical documentation, proof/decision explanation, customized instructions, item and event descriptions, question answering, tutorials, and stories.
[0006] A sentence generator may be customized to the application or may be general purpose. General purpose sentence generators may facilitate the reuse of resources and thus reduce the costs of building applications. Examples of general purpose sentence generators include FUF/Surge, RealPro, Penman/KPML, and Nitrogen.
[0007] It is difficult for a general purpose sentence generator to achieve high quality output and at the same time to cover a broad range of inputs. Usually, rules and class features implemented with general purpose sentence generators are too general to rule out some undesirable combinations, while at the same time they are too restrictive to allow some valid combinations. Higher quality is generally easier to achieve with smaller-scale applications or in limited domains.
[0008] In general, in one aspect, a method for generating sentences includes receiving an input representing one or more ideas to be expressed. The method may include transforming at least a portion of the input using a transformation algorithm.
[0009] Transforming the input may include transforming at least a portion of the input using a recasting rule, a morph rule, a filling rule, and/or an ordering rule. The rules may transform the same or similar portions of the input.
[0010] The method may include producing a plurality of possible expressions for the one or more ideas based on the transforming. The method may include ranking at least some of the plurality of possible expressions, and may include producing an output sentence expressing the one or more ideas based on the ranking.
[0011] The method may include processing inputs which may include one or more labeled feature values. The feature type may be a relation feature, a property feature, or other feature type.
[0012] In general, in one aspect, a system may include a symbolic generator and a statistical generator. The symbolic generator may receive input representing one or more ideas, process the input, and produce a number of possible expressions based on the processing. The statistical ranker may receive at least some of the possible expressions, may rank at least some of the possible expressions, and may determine the best choice of the possible expressions.
[0013] The symbolic generator may process the input according to a transformation algorithm. The transformation algorithm may include one or more mapping rules such as recasting rules, morph rules, filling rules, and ordering rules. The symbolic generator may access a knowledge base, which may include a lexicon such as a closed lexicon and/or an application specific lexicon. The knowledge base may include a dictionary.
[0014] The symbolic generator may process minimally specified inputs, fully specified inputs, or inputs with specification between the two. The symbolic generator may assign a weight to a possible choice. The statistical ranker may use the weight to determine the best choice.
[0015] The symbolic generator may process inputs with a plurality of nesting levels including a top nesting level and one or more lower nesting levels. The input may have meta OR nodes at a lower nesting level. The symbolic generator may process input having an instance relation with compound values. The symbolic generator may process input including a template relation.
[0016] The details of one or more implementations are set forth in the accompanying drawings and the description below. Other features and advantages will be apparent from the description and drawings, and from the claims.
[0017]FIG. 1A is a representation of machine translation.
[0018]FIG. 1B is a representation of human-computer dialog.
[0019]FIG. 2 shows a system that may be used to generate sentences based on input.
[0020]FIG. 3 shows a process that may be used to generate sentences.
[0021]FIG. 4A shows a Penn Treebank annotation and associated sentence.
[0022]FIG. 4B shows a minimally specified input for the example of FIG. 4A.
[0023]FIG. 4C shows an almost fully specified input for the example of FIG. 4A.
[0024]FIG. 5 shows an algorithm that may be used to preserve ambiguities.
[0025]FIG. 6A shows a recasting rule.
[0026]FIG. 6B shows another recasting rule.
[0027]FIG. 7 shows a filling rule.
[0028]FIG. 8 shows an ordering rule.
[0029]FIG. 9 shows a morph rule.
[0030]FIG. 10 shows a forest.
[0031]FIG. 11A shows another forest.
[0032]FIG. 11B shows an internal PF representation of the top three levels of nodes of the forest of FIG. 11A.
[0033]FIG. 12 illustrates a pruning process that may be used with a bigram model.
[0034]FIG. 13 shows pseudocode that may be used for a ranking algorithm.
[0035] Like reference symbols in the various drawings indicate like elements.
[0036] The goal of sentence generation is to transform an input into a linearly-ordered, grammatical string of morphologically inflected words; that is, a fluent sentence.
[0037]FIG. 1A illustrates a process of sentence generation in a machine translation system. A user may input a sentence in Arabic to be translated into English. The meaning of the sentence is represented by language-neutral terms (referred to generally as interlingua). The language-neutral terms are input to a sentence generator, which produces a translated English sentence. FIG. 1B illustrates a process of sentence generation in the context of a human-computer dialogue application. The input to the sentence generator is, for example, the output of a database.
[0038] Systems and techniques described herein may provide a number of benefits over available systems. The system input and mapping rules may be structured so that the system may provide complete coverage of English. Some previous systems limited the coverage in order to reduce the generation of ungrammatical sentences. In the current system, although ungrammatical sentences may be generated as part of the forest of possible expressions, the statistical ranker may be used to reduce the generation of ungrammatical output. Therefore, the current system does much to resolve the conflict between broad coverage and accuracy.
[0039] Referring to FIG. 2, a system 200 includes a symbolic generator 210 for receiving input 220 expressing one or more ideas. Symbolic generator 210 may transform one or more portions of the input according to one or more transformation algorithms. Symbolic generator 210 may access, for example, recasting rules 211, filling rules 212, morph rules 213, and ordering rules 214 for processing at least a portion of input 220. Note that rules 211-214 may be integrated with symbolic generator or may be separate.
[0040] Symbolic generator 210 may use a knowledge base 230 to map input to one or more possible output expressions 240 (e.g., a forest). Knowledge base 230 may include a dictionary 231 such as a Wordnet-based dictionary, one or more lexicons 232 such as a closed-class lexicon and an application-specific lexicon, and morphological inflection tables. Recasting rules 211, filling rules 212, morph rules 213, and ordering rules 214 may be considered part of knowledge base 230.
[0041] Symbolic generator 210 may use an ontology such as the Sensus concept ontology, which is a WordNet-based hierarchy of word meanings segregated into events, objects, qualities, and adverbs. The Sensus concept ontology includes a rank field to order concepts by sense frequency for a given word.
[0042] Expression(s) 240 of symbolic generator 210 may be provided to a statistical ranker 250 to choose among expressions 240. Statistical ranker 250 may use an ngram scheme (e.g., a bigram or trigram scheme) to produce an output sentence 260.
[0043] System 200 of FIG. 2 may be used to produce a sentence using an input. Referring to FIG. 3, a process using a system such as system 200 may include receiving an input (310), where the input may represent one or more ideas to be expressed in a sentence.
[0044] The input may be processed using one or more mapping rules (320) to produce one or more possible expressions (330). The one or more possible expressions may in turn be processed using a statistical ranker (340), which may output the best expression based on the statistical ranking (350).
[0045] System Inputs
[0046] Current systems and techniques may use a labeled feature-value structure for input. Labels, when included, may be arbitrary symbols used to identify a set of feature-value pairs. Features are represented as symbols preceded by a colon. Features may express relationships between entities, or properties of a set of relationships or of an atomic value. The value of a feature can be an atomic entity, or a label, or recursively another labeled set of feature value pairs.
[0047] The most basic input is a leaf structure of the form: (label/word-or-concept). Inputs that may be used to represent phrases such as "the dog," "the dogs," "a dog," or "dog" include Examples 1 and 2 below:
[0048] The slash (/) is shorthand for the ":instance" feature (a fundamental relation). In logic notation, the input above may be written as Instance (m1, DOG).
[0049] The ":instance" feature also represents the semantic or syntactic head of a set of relationships. The value of the instance feature can be a word or a concept. A word may be enclosed in string quotes, and the system may require that the word be in root form.
[0050] A concept may be expressed as a valid Sensus symbol, which is a mnemonic name for a WordNet synset enclosed in vertical bars. The Sensus Ontosaurus Browser may be accessed, for example, via http://Mozart.isi.edu:8003/sensus2, and may be used to look up concept names for words and synset classes. A concept generally represents a unique meaning and can map to one or more words.
[0051] Fully specified and minimally specified inputs
[0052] The current system may use inputs that are not fully specified. FIG. 4A shows a Penn Treebank annotation for the sentence "Earlier the company announced it would sell its aging fleet of Boeing Co. 707s because of increasing maintenance costs." FIG. 4B shows an example of a minimally specified input for the sentence of FIG. 4A and the output that may be obtained using a system as described herein. FIG. 4C shows an example of an almost fully specified input for the sentence of FIG. 4A and the output that may be obtained.
[0053] Relation Features
[0054] Relation features describe the relationship between the instance value and another content-bearing value. A content-bearing value may be a simple word or concept (e.g. "dog" in Examples (1) and (2) above), or may be a compound value including, e.g., nested feature-value structures.
[0055] Examples (3) and (4) below both express the idea "The dog eats a meaty bone." Example (3) uses syntactic relations, while Example (4) also uses semantic relations. Note that the value labeled 'b1' in each example is a compound value.
[0056] As shown in Examples (3) and (4), multiple relations can appear at a nesting level in the input. Some relations can only occur once at any given nesting level. Others, including modifier and adverbial relations (adjuncts), can occur multiple times.
[0057] Relations may be order-independent, so that the order in which the relations occur in the input does not affect the order in which their values occur in the output. However, there may be exceptions. For example, a conditional exception may occur when the same relation occurs more than once in a nesting level.
[0058] The system may deal with this in a number of ways. For example, a "permute nodes" flag may be used, where setting the flag to "nil" causes the values with the same relation to occur adjacent to each other in the output in the same order that they appeared in the input. Setting the flag to "true" causes the values to occur adjacent to each other in an order determined by a statistical model.
[0059] The system may recognize relations such as shallow syntactic relations, deep syntactic relations, and semantic relations. These relations may be recognized by mapping rules used by the symbolic generator to produce the forest of possible expressions. The mapping rules may be extended to recognize other relations (e.g., non-linguistic and/or domain-specific relations). Table 1 below lists relations that may be used by the system, organized by relation type.
[0060] Different, more, or fewer relations may be used, according to different implementations. Although the relations in Table 1 are grouped according to degree of abstraction, there need not be a formal definition of a level of abstraction to separate the different levels. Instead, relations at different levels of abstraction may be mixed in the same input and at the same level of nesting.
[0061] Mappings from a deeper relation to a shallower relation may capture an equivalence that exists at the shallower level. Abstraction may be treated as a continuum rather than as a discrete set of abstraction levels. The continuum approach may increase the flexibility of the input from a client perspective, and may also increase the conciseness and modularity of the symbolic generator's mapping rules.
[0062] The continuum approach may also simplify the definition of paraphrases. The ability to paraphrase may be important in a general purpose sentence generator. Herein, the term paraphrase refers to one or more alternations sharing some equivalence that is encapsulated in a single representation using one or more relations at a deeper level abstraction. Alternations sharing the equivalency are produced using the deeper input. Generation of paraphrases may be controlled or limited using a property feature if desired.
[0063] Property features (see below) may be used to at least partially overcome the problems of subjectivity that may plague deeper levels of abstraction. Examples of property features that may be used to define deeper levels of abstraction include voice, subject-position, and the syntactic category of a dominant constituent (i.e., whether the phrasal head is a noun versus a verb).
[0064] Using this definition style, equivalencies at higher levels of abstraction generally produce a greater number of variations or paraphrases than those at lower levels of abstraction. Therefore, the system has the ability to generate a large number of paraphrases given an input at a deep level of abstraction, as well as the ability to limit the variation in a principled way by specifying relevant property features or using a shallower level of abstraction.
[0065] The system may recognize and process semantic relations, such as those defined and used in the GAZELLE machine translation project. Additionally, the system may map semantic relations to one or more syntactic relations.
[0066] As a result, the system may be able to paraphrase concepts such as possibility, ability, obligatoriness, etc. as modal verbs (e.g., may, might, can, could, would, should, must) using the :domain relation. By having access to other syntactic structures to express these ideas, the system can generate sentences even when a domain relation is nested inside another domain, and when any combination of polarity is applied to inner and outer domain instances (even though modal verbs themselves cannot be nested).
[0067] For example, the following sentence is not grammatical: "You may must eat chicken" (i.e., the nested modal verb structure is ungrammatical). However, the system may access other syntactic structures to paraphrase the concepts. For example, the system may produce the grammatically correct paraphrase: "You may be required to eat chicken."
[0068] Another consequence of the ability to map semantic relations to syntactic relations allows for the system to capture the equivalence between alternations like "Napoleon invaded France" and "Napoleon's invasion of France." The :agent and :patient semantic relations are used to represent the similarity between expressions whose semantic head is realized as a noun versus as a verb. That is, :agent (i.e. Napoleon) can map to either :logical-subject (to produce "Napoleon invaded France"), or to :generalized-possession-inverse, which can produce a possessive phrase using an 's construction (i.e., "Napoleon's invasion of France.") The :patient relation (i.e. France) maps to either :logical-object or to :adjunct with a prepositional anchor like "of."
[0069] Deep syntactic relations may capture equivalencies that exist at the shallow syntactic level. For example, the :logical-subject, :logical-object, and :logical-dative relations capture the similarity that exists between sentences that differ in active versus passive voice. For example, the two sentences "The dog ate the bone" and "The bone was eaten by the dog" would both be represented at the deep syntactic level as shown in Example (5) below:
[0070] To further specify the voice, the :voice feature may be used. With "active" voice, :logical-subject would map to :subject and :logical-object would map to :object. In contrast, with "passive" voice, :logical-object would map to :subject and :logical-subject would map to :adjunct with the addition of a prepositional anchor "by."
[0071] The adjunct relation at the deep syntactic level maps to either :premod, :postmod, or :withinmod at the syntactic level, abstracting away from ordering information to capture the similarity that all three syntactic relations are adjuncts. The :closely-related relation can be used to represent the uncertainty of whether a particular constituent is, for example, a required argument of a verb, or an optional adjunct. The question relation consolidates in one relation the combination of three syntactic features that can sometimes be independent.
[0072] For example, Example (6) and Example (7) below show two equivalent inputs to represent "What did the dog eat?"
[0073] The :punc relation generalizes the :leftpunc, :rightpunc, and :sandwichpunc relations. The :sandwichpunc relation is itself a generalization of the combination of both :leftpunc and :rightpunc.
[0074] The shallow syntactic relations shown in Table 1 include subject, object, predicate, etc., as well as other relations. For example, the :predet relation broadly represents any head noun modifier that precedes a determiner. The :topic relation may include question words/phrases. The :anchor relation represents both prepositions and function words like "that," "who," "which," etc., that may be viewed as explicitly expressing the relation that holds between two content-bearing elements of a sentence.
[0075] Other shallow syntactic relations may relate to coordinated phrases. Coordinated phrases may be represented in a number of ways. Examples (8) and (9) below show two ways of representing coordinated phrases:
[0076] The representation of coordinated phrases may combine elements of dependency notation and phrase-structure notation. At the lowest level of abstraction, coordination may be signaled by the presence of more than one instance relation. Besides :conj, the relations that may be involved in coordinated phrases include :coordpunc, :bcpp, and :introconj. The system may be configured so that, if not specified, :coordpunc usually defaults to :comma, but defaults to :semicolon when coordinated phrases already contain commas. However, the system may be configured so that :coordpunc may be specified to be words like "or" (as in the example "apples or oranges or bananas). Alternately, :coordpunc may be specified to be other types of punctuation.
[0077] The relation :bcpp is a Boolean property that may be used to control whether a value specified by :coordpunc occurs immediately before the conjunction. For example, if :bcpp is specified as true, then "a, b, and c" may be generated, while if :bcpp is specified as false, "a, b and c" may be generated. The default for :bcpp may be false unless more than two entities are being coordinated.
[0078] The relation :introconj may be used to represent the initial phrases that occur in paired conjunctions. For example, :introconj may represent phrases such as "not only . . . but," and "either . . . or."
[0079] Relations may be aliased to accommodate the varying nomenclature of different applications. For example, :agent may be referred to as :sayer or :sensor, while :dative may be referred to as :indirect-object.
[0080] The system may also allow instances to be compound nodes rather than being restricted to atomic values as in some prior art systems. This may provide a number of benefits, including providing a flexible means of controlling adjunct generation and allowing the representation of scope. Examples (10) and (11) below illustrate controlling adjunct generation using compound nodes.
[0081] The inputs shown in Examples (10) and (11) have equivalent meanings. However, Example (11) constrains the set of possible outputs. That is, the input of Example (10) may produce both "a flight to Los Angeles on Monday" and "a flight on Monday to Los Angeles," while the input of Example (11) constrains the output to only the second variant.
[0082] Such output constraints may be desired by some applications of the general purpose system described herein. For example, in some applications the outputs may be constrained for rhetorical reasons (such as to generate a response that parallels a user utterance).
[0083] The nesting of the Instance relation specifies a partial order on the set of relations so that those in the outer nest are ordered more distantly from the head than those in the inner nest. In some implementations, the same thing may be accomplished by setting a "permute-nodes" flag to false.
[0084] The semantic notion of scope can be added to a nested feature-value set via the :unit feature. Example (12) below shows how the :unit feature may be used to generate "the popular University of Southern California," where the :unit feature and the nested structure indicate that the adjunct "popular" modifies the entire phrase "University of Southern California" rather than the term "University" alone.
[0085] The system may also be configured so that a meta-level *OR* may be used to express an exclusive-or relationship between a group of inputs or values. Semantically, it represents ambiguity or a choice between alternate expressions. It may also be viewed as a type of under-specification. The statistical ranker may then choose among the alternate expressions, as described below.
[0086] The input shown in Example (13) below represents two semantic interpretations of the clause "I see a man with a telescope," with a choice between the words "see" and "watch" and with an ambiguity about whether John said it or Jane sang it.
[0087] The system may also enable template-like capability through the :template and :filler features. Example (14) shows an input using the :template and :filler features to produce the output "flights from Los Angeles."
[0088] Property Features
[0089] The system may also be configured to process inputs including property features such as atomic-valued property features. Property features describe linguistic properties of an instance or a clause. In an implementation, property features are not generally included as inputs, but may be used to override defaults. Table 2 shows some property features that may be used.
[0090] Example (15) below shows an input using a property feature that specifies that a noun concept is to be plural:
[0091] Using the :number property narrows the meaning to "the dogs" or "dogs." If the :number property were not specified, the statistical ranker would choose among singular and plural alternatives.
[0092] Property features may allow more specific inputs and thus better output. However, the current system is able to deal with underspecified inputs effectively, by virtue of the mapping rules and the statistical ranker.
[0093] Property features may also be used to generate auxiliary function words. For example, verb properties such as :modal, :taxis, aspect, and :voice may be used to generate auxiliary function words. In combination with the verbal :mood property, these four features may be used to generate the entire range of auxiliary verbs used in English.
[0094] Example (16) below illustrates a possible use of verbal properties by explicitly specifying values for possible properties.
[0095] The output based in the input shown in Example (16) is "Jane might be eating ice cream."
[0096] The :taxis feature generates perfect tense when specified ("might have been eating"). The default may be that :taxis none is generated. The :aspect feature may generate continuous tense when specified as in Example (16). If :aspect is not specified, the default may be :aspect simple, which would generate "Jane might eat ice cream."
[0097] The :voice feature may be passive or active. Had passive voice been specified above, "Ice cream might have been eaten by Jane" would have been generated. The default of the :modal feature may be set to none.
[0098] The :person feature has six primary values corresponding to each combination of person (i.e., first, second, and third person) and verbal number (singular or plural), as shown in Table 3 below.
[0099] Since verbs (except "be") generally have a distinct value for only third-person singular, the :person feature value may be abbreviated as just "s" (for "3s") or "p" (for all others). If :person is not specified, the system may generate a set of unique inflections, and choose among them using the statistical ranker.
[0100] The :subject-position feature may have two non-default values: "post-aux" and "post-vp." The post-aux value may be used to produce questions and some inverted sentences, such as "Might Jane be eating ice cream?" and "Marching down the street was the band" (e.g., by also using the :topic relation with the main verb). The :post-vp value may be used, for example, in combination with the verb "say" and its synonyms, together with the :topic relation, which shifts verbal constituents to the front of the sentence. An example output would be "'Hello!,' said John."
[0101] Sentence Generation
[0102] Sentence generation may include two parts. First, the input is processed by a symbolic generator to produce a set of possible expressions (referred to as "a forest"). Second, the possible expressions are ranked using a statistical ranker.
[0103] Symbolic Generator
[0104] The symbolic generator maps inputs to a set of possible expressions (a forest). The tasks that the symbolic generator performs may include mapping higher-level relations and concepts to lower-level ones (e.g., to the lowest level of abstraction), filling in details not specified in the input, determining constituent order, and performing morphological inflections.
[0105] The symbolic generator may use lexical, morphological, and/or grammatical knowledge bases in performing these tasks. Some linguistic decisions for realizing the input may be delayed until the statistical ranking stage. Rather than making all decisions, the symbolic generator may itemize alternatives and pack them into an intermediate data structure.
[0106] The knowledge bases may include, for example, a dictionary such as a Wordnet-based dictionary, a lexicon such as a closed-class lexicon and an application-specific lexicon, morphological inflection tables, and input mapping rules.
[0107] Sensus Concept Ontology
[0108] The system may use Sensus concept ontology, which is a WordNet-based hierarchy of word meanings segregated at the top-most level into events (verbal concepts), objects (nominal concepts), qualities (adjectives), and adverbs. Each concept represents a set of synonyms, referred to as a synset. The ontology lists approximately 110,000 tuples of the form: (<word><part-of-speech><rank><concept>), such as ("Eat" VERB 1 |eat, take in|). The <rank> field orders the concepts by sense frequency for the given word, with a lower number signifying a more frequent sense.
[0109] Unlike other generators, the current system can use a simple lexicon without information about features like transitivity, sub-categorization, gradability (for adjectives), countability (for nouns), etc. Other generators may need this additional information to produce correct grammatical constructions. In contrast, the current system uses a simple lexicon in the symbolic generator and uses the statistical ranker to rank different grammatical realizations.
[0110] At the lexical level, issues in word choice arise. WordNet maps a concept to one or more synonyms. However, depending on the circumstances, some words may be less appropriate than others, or may be misleading in certain contexts.
[0111] For example, the concept |sell<cozen| represents the idea of deceit and betrayal. The lexicon maps it to both "betray" and "sell" (as in a traitor selling out his friends). However, use of the word "sell" to convey the meaning of deceit and betrayal is less common, and may be misleading in contexts such as "I cannot |sell<cozen| their trust." It is thus less appropriate than using the word "betray." Word choice problems such as these may occur frequently.
[0112] The system may use the word-sense rankings to deal with the problem. According to the lexicon, the concept |sell<cozen| expresses the second most frequent sense of the word "betray," but only the sixth most frequent sense of the word "sell."
[0113] The system may use a heuristic of associating with each word a preference score. For example, a weight may be assigned according to a formula such as Equation (1):
[0114] The statistical ranker may use the weight to choose the most likely alternative. Other methods may be used to weight particular alternatives. For example, Bayes' Rule or a similar method may be used, or probabilities computed using a corpus such as SEMCOR may be used weighting factors may also be specified in inputs, included in some other aspect of the knowledge base, and/or be included in one or more rules.
[0115] Another issue in word choice relates to the broader issue of preserving ambiguities, which may be important for applications such as machine translation. It may be difficult to determine which of a number of concepts is intended by a particular word. In order to preserve the ambiguity, the system may allow alternative concepts to be listed together in a disjunction. For example, the input (m6/(*OR*|sell<cosen| |cheat on| |betray| |betray, fail| |rat on|)) reflects the ambiguity in the term "sell." The system may attempt to preserve the ambiguity of the *OR*.
[0116] However, if several or all of the concepts in a disjunction can be expressed using the same word or words, the lookup may return only that word or those words in preference to other alternatives. In the example above, the lookup may return only the word "betray." By doing so, the system may reduce the complexity of the set of candidate sentences.
[0117]FIG. 5 shows an algorithm that may be used to preserve ambiguities. The ambiguity preservation process may be triggered when an input contains a disjunction, where a parenthesized list with *OR* is the first element and two or more additional elements representing inputs or input fragments to be chosen from.
[0118] The ambiguity preservation process may be controlled in a number of ways. There may be a general system flag that can be set to true or false to turn on or off the ambiguity preservation procedure. If the flag is set to false, the alternative forests generated by the disjoined input fragments may simply be packed into the forest as alternatives, with no deliberate preservation of ambiguity. If the flag is set to true, only the result forest that remains from intersecting the respective forests produced by the input fragments may be passed on to the statistical ranker. An alternate scheme involves a different disjunction symbol *AOR* to indicate to the system that the ambiguity preservation procedure should be used to process the corresponding input fragments.
[0119] Closed Class Lexicon
[0120] The system may include a closed class lexicon, which may include entries of the following form: (:cat <cat> :orth <orthography> :sense <sense>). Examples include:
[0121] (:cat ADJ :orth "her":sense 3s_fem_possessive)
[0122] (:cat CC :orth "and":sense cc—0)
[0123] (:cat DT :orth "a":sense indef_det)
[0124] (:cat IN :orth "with":sense with)
[0125] (:cat MD :orth "can":sense modal_verb)
[0126] (:cat NOUN :orth "he":sense 3s_pronoun)
[0127] (:cat PDT :orth "all":sense pdt—0)
[0128] (:cat RB :orth "when":sense wrb—2)
[0129] (:cat RP :orth "up":sense rp—27)
[0130] (:cat WDT :orth "which":sense wdt_clocla)
[0131] (:cat UH :orth "ah":sense uh—0)
[0132] (:cat |-COM-| :orth ",":sense comma)
[0133] Application-Specific Lexicon
[0134] The system may allow for a user-defined lexicon that may be used to customize the general-purpose system described herein. The application-specific lexicon may be consulted before other lexicons, to allow applications to override the provided knowledge bases rather than change them.
[0135] The user-defined lexicon may include entries of the form: (<concept> <template-expansion>). An example of an entry is the following: (|morning<antemeridian| (*OR* (:cat NN :lex "a.m.") (:cat NN :lex "morning"))).
[0136] Morphological Knowledge
[0137] The system may include morphological knowledge. The lexicon generally includes words in their root form. To generate morphological inflections (e.g., plural nouns and past tense verbs), a morphological knowledge base may be used.
[0138] The system may also include morphological knowledge such as one or more tables for performing derivational morphology such as adjective to noun and noun to verb derivation (e.g., "translation" becomes "translate.") Morphological knowledge may enable the system to perform paraphrasing more effectively, and may provide more flexibility in expressing an input. It may else help mitigate problems of syntactic divergence in machine translation applications.
[0139] The system may implement a morphological knowledge base by providing pattern rules and exception tables. The examples below show a portion of a table for pluralizing nouns:
[0140] The last example instructs the system that if a noun ends in a consonant followed by "-o," the system should produce two plural forms, one ending in "-os" and one ending in "-oes," and store both possibilities for the statistical ranker to choose between later. Again, corpus-based statistical knowledge may greatly simplify the task of symbolic generation.
[0141] Mapping Rules
[0142] The symbolic generator may use a set of mapping rules in generating alternative expressions. Mapping rules map inputs into an intermediate data structure for subsequent ranking. The left hand side of a mapping rule specifies the conditions for matching, such as the presence of a particular feature at the top-level of the input. The right-hand-side lists one or more outcomes.
[0143] In applying mapping rules, the symbolic generator may compare the top level of an input with each of the mapping rules. The mapping rules may decompose the input and recursively process the nested levels. Base input fragments may be converted into elementary forests and then recombined according to the mapping rules to produce the forests to be processed using the statistical ranker.
[0144] In an implementation, there are 255 mapping rules of four kinds: recasting rules, ordering rules, filling rules, and morphing rules.
[0145] Recasting Rules
[0146] Recasting rules map one relation to another. They are used, for example, to map semantic relations into syntactic ones, such as :agent into :subject or :object. Recasting rules may enable constraint localization. As a result, the rule set may be more modular and concise. Recasting rules facilitate a continuum of abstraction levels from which an application can choose to express an input. They may also be used to customize the general-purpose sentence generator described herein. Recasting rules may enable the system to map non-linguistic or domain-specific relations into relations already recognized by the system.
[0147]FIG. 6A shows an example of a recasting rule 600, an English interpretation 610 of rule 600, and an illustration 620 of rule 600. FIG. 6B shows an example of a recasting rule 630, an English interpretation 640 of rule 630, and an illustration 650 of rule 630.
[0148] Recasting rules may also allow the system to handle non-compositional aspects of language. One area in which this mechanism may be used is in the domain rule. The sentence "It is necessary that the dog eat" may be represented as shown in Example (17):
[0149] Examples (17) and (18) may be defined as semantically equivalent. Both may be accepted, and the first may be automatically transformed into the second.
[0150] Alternate forms of this sentence include "The dog is required to eat," or "The dog must eat." However, the grammar formalism may not directly express this, because it would require inserting the result for |obligatory<necessary| within the result for m9 or m12, while the formalism may only concatenate results. The recasting mechanism may be used to solve this problem by recasting Example (18) as in Example (19) below:
[0151] so that the sentences may be formed by concatenation of the constituents. The syntax for recasting the first input to the second is:
[0152] A filling rule may add missing information to underspecified inputs. Filling rules generally test to determine whether a particular feature is absent. If so, the filling rule generates one or more copies of the input, one for each possible value of the missing feature, and add the feature-value pair to the copy. Each copy may then be independently circulated through the mapping rules. FIG. 7 shows an example of a filling rule 700, an interpretation 710 of rule 700, and an illustration 720 of rule 700.
[0153] Ordering Rules
[0154] Ordering rules assign a linear order to the values whose features matched with the rule. Ordering rules generally match with syntactic features at the lowest level of abstraction.
[0155] An ordering rule may split an input into several pieces. The values of the features that matched with the rule may be extracted from the input and independently recirculated through the mapping rules. The remaining portion of the original input may then continue to circulate through the rules where it left off. When each of the pieces finishes circulating through the rules, a new forest node may be created that composes the results in the designated linear order.
[0156]FIG. 8 shows an example of an ordering rule 800, an English interpretation 810 of rule 800, and an illustration 820 of rule 800.
[0157] Morphological Inflection (Morph) Rules
[0158] A morph rule produces a morphological inflection of a base lexeme, based on the property features associated with it. FIG. 9 shows an example of a morph rule 900, an English interpretation 910 of rule 900, and an illustration 920 of rule 900.
[0159] Forest Representation
[0160] The results of symbolic generation may be stored in an intermediate data structure such as a forest structure. A forest compactly represents a large, finite set of candidate realizations as a non-recursive context-free grammar. It may also be thought of as an AND-OR graph, where AND nodes represent a sequence of elements, and OR nodes represent a choice between mutually exclusive alternatives. A forest may or may not encode information about linguistic structure of a sentence.
[0161]FIG. 10 shows an example of a forest 1000, its internal representation 1010, and a list of different sentences 1020 it represents. Nodes of forest 1000 are labeled with a symbol including an arbitrary alpha-numeric sequence, then a period, then a number. The alpha-numeric sequence may be used to improve readability of the forest. The number identifies a node. The TOP node is special and is labeled simply "TOP."
[0162]FIG. 11A shows another example of a forest 1100, while FIG. 11B shows an internal PF representation (see below) of the top three levels of nodes in the forest shown in FIG. 11A.
[0163] A forest may include two types of rules: leaf and non-leaf. A leaf rule has only one item on its right-hand side: an output word enclosed in double quotes. A non-leaf node may have any number of items on its right-hand side, which are labels for a sequence of child nodes. The presence of multiple rules with the same left-hand side label represents a disjunction, or an OR node.
[0164] Alternatively, a third type of rule may be used to represent OR nodes to simplify implementation. This third type of rule may have the same structure as a non-leaf sequence node, except that it contains an OR-arrow symbol ("OR→") in place of a simple arrow. This alternate representation of OR nodes may be referred to as a generation forest (GF) representation, while the first form is referred to as a parse forest (PF) representation. In a GF representation, a label appears on the left-hand side of a rule only once. In a GF representation, the four rules in FIG. 10 that represent the two OR-nodes would be represented textually using only two rules:
[0167] Realization Algorithm
[0168] The system may realize one or more outputs as follows. The symbolic generator may compare the top level of an input with each of the mapping rules in turn. Matching rules are executed. The mapping rules transform or decompose the input and recursively process the new input(s). If there is more than one new input, each may be independently recirculated through the rules. The system converts base input fragments into elementary forests and then recombines them according to the specification of the respective mapping rules as each recursive loop is exited.
[0169] When a rule finishes executing, the result is cached together with the input fragment that matched it. Since the system may extensively overgenerate, caching may be used to improve efficiency. Each time a matched rule transforms or decomposes an input, the new sub-input(s) may be matched against the cache before being recursively matched against the rule set.
[0170] If execution of a particular rule is not successful, the original input may continue matching against the rest of the rule set. Rematching takes similar advantage of the cache. If no match or rematch exists, generation of a particular sub-input may fail.
[0171] Rules may be ordered so that those dealing with higher levels of abstraction come before those dealing with lower levels. Ordering rules generally provide the lowest level of abstraction. Among ordering rules, those that place elements farther from the head come before those that place elements closer to the head. As rule matching continues, ordering rules extract elements from the input until only the head is left. Rules that perform morphological inflections may operate last. Filling rules may come before any rule whose left-hand-side matching conditions might depend on the missing feature.
[0172] Dependencies between relations may thus govern the overall ordering of rules in the rule set. The constraints on rule order define a partial-order, so that within these constraints it generally does not matter in what order the rules appear, since the output will not be affected. The statistical ranker processes the resulting forest after the mapping rules have executed.
[0173] Statistical Ranker
[0174] The statistical ranker determines the most likely output among possible outputs. The statistical ranker may apply a bottom-up dynamic programming algorithm to extract the N most likely phrases from a forest. It may use an ngram language model, for example, an ngram language model built using Version 2 of the CMU Statistical Modeling Toolkit. The ranker finds an optimal solution with respect to the language model.
[0175] The statistical ranker may decompose a score for each phrase represented by a particular node in the forest into a context-independent (internal) score, and a context-dependent (external) score. The internal score may be stored with the phrase, while the external score may be computed in combination with other nodes such as sibling nodes.
[0176] An internal score for a phrase associated with a node p may be defined recursively as shown in Equation (2) below:
I(p)=πj=1 J I(c j)*E(c j|context(c 1 . . . c j−1)) Equation (2)
[0177] where I is the internal score, E is the external score, and cj is a child node of p. The formulation of I and E, as well as the definition of context may be chosen according to the language model being used. For example, in a bigram model, I=1 for leaf nodes, and E may be expressed as shown in Equation (3) below:
E=P(FirstWord(c j)|LastWord(c j−1)) Equation (3)
[0178] In Equation (3), P refers to a probability. A bigram model is based on conditional probabilities, where the likelihood of each word in a phrase is assumed to depend on only the immediately previous word. The likelihood of a whole phrase is the product of the conditional probabilities of each of the words in the phrase.
[0179] Depending on the language model being used, a phrase may have a set of externally relevant features. These features are the aspects of the phrase that contribute to the context-dependent scores of sibling phrases, according to the definition of the language model. In a trigram model, for example, it is generally the first and last two words. In more elaborate language models, features might include elements such as head word, part of speech tag, constituent category, etc. The degree to which the language model used matches reality, in terms of what features are considered externally relevant, will affect the quality of the output.
[0180] Using a forest-based method, only the best internally scoring phrase may be maintained. Other phrases may be pruned, which exponentially reduces the total number of phrases to be considered. That is, the ranking algorithm is able to exploit the independence that exists between most disjunctions in the forest.
[0181]FIG. 12 illustrates a pruning process that may be used with a bigram model. The rule for node VP.344 in the forest shown in FIG. 11A is shown, with the set of phrases corresponding to each of the nodes. If every possible combination of phrases is considered for the sequence of nodes on the right hand side, there are three unique first words: might, may, and could. There is only one unique final word: eaten. Since the first and last words of a phrase are externally relevant features in a bigram model, only the three best scoring phrases (out of the twelve total) need be maintained for node VP.344 (one for each unique first-word and last-word pair). In the bigram model, the other nine phrases will not be ranked higher than the three maintained, regardless of the elements vP.344 may later be combined with. Note that although the internal words of VP.344 are identical, this is not the general case. The most likely internal phrase depends on the context words and may vary accordingly.
[0182] Pseudocode for a ranking algorithm that may be used is shown in FIG. 13. "Node" may be a record including at least an array of child nodes, "Node->c[1 . . . N]," and best-ranked phrases "Node->p[1 . . . M]." The function ConcatAndScore concatenates two strings together, and computes a new score. The function Prune causes the best phrase for each set of features values to be maintained.
[0183] The core loop in the algorithm considers the children of the node one at a time, concatenating and scoring the phrases of the first two children and pruning the results before considering the phrases of the third child, and concatenating them with the intermediate results from the first two nodes, etc.
[0184] The complexity of the algorithm illustrated by the pseudocode of FIG. 13 is dominated by the number of phrases associated with a node rather than the number of rules used to represent the forest or the number of nodes on the right hand side of a node rule.
[0185] More specifically, because of the pruning, it depends on the number of features associated with the language model, and the average number of unique combinations of feature values. If f is the number of features, v the average number of unique values seen in a node for each feature, and N the number of N best phrases being maintained for each unique set of feature values (but not a cap on the number of phrases), then the algorithm has the complexity of O((vN)2f) (assuming that children of AND nodes are concatenated in pairs). Note that f=2 for the bigram model and f=4 for the trigram model.
[0186] In comparison, prior art systems using a lattice structure rather than a forest structure may have a complexity O((vN)l), where l is approximately the length of the longest sentence in the lattice. That is, the current system may provide an exponential reduction in complexity while providing an optimal solution. Generators using a capped N-best heuristic search algorithm have lower complexity O(vNl), but generally fail to find optimal solutions to longer sentences.
[0187] Testing
[0188] It can be difficult to quantify the results of sentence generation. One reason is that there may be more than one "correct" output for a given input. For example, FIG. 1B illustrates a simple situation in which two different outputs are correct.
[0189] The sentence generator described herein was evaluated using a portion of the Penn Treebank as a test set. The Penn Treebank offers a number of advantages as a test set. It contains real-world sentences, it is large, and it can be assumed to exhibit a very broad array of syntactic phenomena. Additionally, it acts as a standard for linguistic representation.
[0190] To perform the test, inputs to the sentence generator were automatically constructed from the Treebank annotation and then regenerated by the system. The output was then compared to the original sentence. For mostly specified inputs, coverage and accuracy ranged from 76% and 84%, with exact matches generated 34% of the time. For minimally specified inputs, coverage and accuracy ranged from 80% and 48%.
[0191] A number of implementations have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of the invention. For example, the form and symbols used in the input may be different. Different labeling schemes may be used. Different methods of weighting may be used. Different statistical rankers may be used. Process steps may be performed in the order given, or in a different order. Accordingly, other embodiments are within the scope of the following claims.
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NFL rookie gather for symposium
Troy Vincent wants to make it absolutely clear that the upcoming NFL rookie symposium is not the beginning of the players' journey into the pros.
That trip began years ago.
''This is not something that starts when you become a professional,'' said Vincent, the league's vice president of player engagement and a five-time Pro Bowler. ''There are elements that surround an athlete in his development, from youth sports through high school and college. It's a lifestyle.
''Parents are a key. What do they do with their youngsters who are playing sports as far as their mental health, their safety in the sport. Are we doing enough to encourage balancing their lives for those in athletics?
''Parents have questions, and rightfully so. And we should answer them, dispel the myths.''
The symposium helps do that. This is the 15th year the league has brought together draftees to hear speakers on such subjects as ''Are You Bigger Then The Game'' and ''What Defines Success?'' This gathering, which is a bit different because of the location in Aurora, Ohio, with a side trip to the Pro Football Hall of Fame in nearby Canton, begins Sunday with four days of orientation for NFC draft picks. The AFC choices attend from Wednesday through next Saturday.
Scheduled to speak are several players who have experienced serious off-field issues, including Michael Vick, Adam Jones and Michael Irvin. Also on the docket are Terrell Owens, New England's Devin McCourty, Antonio Freeman, LaVar Arrington, Aeneas Williams, Jamie Dukes, Luther Ellis and Carl Eller.
Irvin and Eller are Hall of Fame members.
''Each rookie should leave the symposium with the knowledge and history of where our game began, where it is today, and challenged to make a positive personal impact on the future of our game,'' added Vincent, a former president of the players' union. ''The NFL and its clubs are committed to providing players with the best resources to succeed both on and off the field.''
The program will includes presentations and speeches, videos and workshops focused on NFL history, experience, player expectations, and professional and social responsibility. Other topics included player health and safety, decision making and maintaining positive relationships.
''Athletes are not immune to what takes place in society. They are not exempt to societal issues,'' Vincent said.
''We do have things in place to help deal with any issues, and we are very proactive in doing so. You have to retrain them or develop a new culture of thinking, and it starts with young men and ladies as they are maturing. This is a challenge not just as a sports organization, but as parents, as mentors and teachers. We welcome that challenge and charge as a league, and hope to become part of a greater conversation, be part of the continuing campaigns to raise awareness of these issues and problems that can come up.''
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Add an element of fun to blasé paperwork with these adorable paperclips! Each slide-open box contains thirty irresistible paperclips shaped like airplaines. Surprise a friend, colleague, or co-pilot, and use one of these little dudes to hold all of your most important assignments together!
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DMV to Internet-less New Yorkers: Get in Line
By Jared Chausow
By Katie Toth
By Elizabeth Flock
City Hid Facts From Feds About Rikers
By Albert Samaha
Flood Wall Street Gets Pepper Sprayed and Arrested
By Anna Merlan
By Jon Campbell
Report Sheds Light on Anti-Muslim Bias
By Jon Campbell
Rikers Island Lawsuits Over Diabetic Care
By Albert Samaha
So it's no great surprise that Jeff Van Gundy's crew was sporting a 5-2 record as of Monday and looking as though it might gain some early traction, temporarily mute the cries for a major trade, and prevent the 76ers from sprinting too far ahead in the Atlantic Division standings. The quick start, though, is misleading, and it disguises a weakness that could trouble the Knicks between now and June.
Contrary to the belief of diehards from Soho to South Ozone Park, the Heat's loss of Alonzo Mourning for the season, the Pacers' decision to jettison some key veterans, and the shaky relationship between Philly superstar Allen Iverson and coach Larry Brown don't mean New York's path to an Eastern Conference title is illuminated like a runway at LaGuardia. The Knicks may have added a premier sharpshooter in three-time All-Star Glen Rice, but they lost a lot more than a surly attitude and a set of rickety knees when the tribal council of Van Gundy, GM Scott Layden, and MSG president Dave Checketts voted Patrick Ewing off the island of Manhattan.
For the four full seasons that Van Gundy has worn the whistle, the charm of the Knicks has been that they've been able to get the round peg to fit into the square hole. They've made the playoffs each year despite never ranking higher than 19th in the league in points scored per game, and placing as low as 27th twice. Moreover, the Knicks won an Eastern Conference title and reached the conference finals twice in the past two years even though they're poor rebounders, they have a starting point guard, Charlie Ward, who's not even among the dozen or so best in the game, and they enjoyed home court in only one of the seven postseason series in which they played.
The Knicks have been successful because their practice sessions have even less slack in them than their rope does. They've been all about sweat puddles, floor burns, and tenacious defense. Make that DEE-fense. The Garden fans have chanted it, Van Gundy has preached it, and the Knicks have won with it. It's a defense that, in two of the past three years, has ranked second in the NBA in fewest points allowed per game. But at times already this season, it's a defense that has been as unreliable as a network-television electoral map.
Philadelphia humiliated New York in the season opener, 101-72, and abused Knicks center Marcus Camby repeatedly. Theo Ratliff and Toni Kukoc, who've never been mistaken for David Robinson and Tim Duncan, had more fun rolling through the paint than Farrah Fawcett did in her pay-per-view special. A two-point loss to Cleveland last week also featured poor interior defense and led a couple of Cavaliers players to remark that it's much easier to attack the rim against the Knicks than it used to be.
Sorry to have to break this to you, but the Knicks miss Ewing already. Though Rice should more than make up for the loss of Ewing's 15 points a game, the skinny, unpolished, and inconsistent Camby is no replacement for Ewing's presence in the middle. Nor is backup Travis Knight or, when he returns from a knee injury, the bumbling Luc Longley.
Even when slowed by age and injuries the past few years, Ewing still intimidated more than just autograph seekers and reporters. He served as a firewall if a slasher beat Ward off the dribble, Allan Houston played matador on the perimeter, or the 6-7 Larry Johnson couldn't contain a much larger power forward. Ewing may have been outmuscled by a Shaquille O'Neal, but he never let Ratliff or Kukoc cruise through the lane as though they were wearing E-Z Passes around their necks.
"Camby was better alongside Ewing or Chris Dudley than he is being the guy who's counted on," says Fox Sports Net analyst Marques Johnson. "Marcus complains about playing out of position, but Rice is out of position and Sprewell has been playing out of position for two years. It hurts the Knicks not having Ewing, but people don't realize that trading Dudley had an impact too. He was an eyesore on offense, but he knocked guys down in the paint and was serviceable defensively. Now, they're just giving up too much easy stuff inside."
Possessing their most lethal perimeter game of the Van Gundy era, the Knicks could compensate for their defensive deficiencies with consistent double-figure scoring from Rice, Houston, and Sprewell, bolstered by the occasional clutch three-pointer from Ward and LJ. But as we saw last year, Houston can wear down badly after the All-Star break (another summer spent with the U.S. National Team allowed him little off-season rest), and Sprewell will stubbornly try to carry the team even when his jumpers are clanging. As long as Van Gundy is the coach, and the battle-scarred LJ and role-players like Chris Childs and Kurt Thomas are on the roster, the Knicks will go only as far as their defense takes them.
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Any of you video player folk ever have this issue?
A video returns error 150 (101) when it can't be embedded outside of youtube
For example, this video
Try and copy the embed code into a html doc and execute, it won't play. It gives you an error
In theird ocs they claim that if you use "format" in your query string you can return videos that are embeddable but it doesn't appear to work, I just get lists of videos that I can't play. :s
Anyone hit this issue before?
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"This is a very timely, much needed and unique text in that it demystifies the dissertation in a way that both academics and students will benefit hugely. Authored by three leading academics, who between them have 60 years of dissertation experience to share, the book will excite readers from the very first page in that it is written in a highly engaging, thoughtful, practical and sometimes humorous manner. The book is systematic and comprehensive in coverage, with its logical structure ensuring a seamless journey through process and procedure, idea generation, methods, ethics and presentation. Containing many excellent and highly pertinent examples throughout, the book will be of considerable benefit to all students for whom the dissertation represents that one last hurdle prior to graduating." – Professor Alan Fyall, Rosen College of Hospitality Management, University of Central Florida, USA.
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New Real HD 1080p H264 5M Car Dashboard Camera Recorder Accident DVR - Car-Dashboard-Camera
Learn more about New Real HD 1080p H264 5M Car Dashboard Camera Recorder Accident DVR:
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|The DVR can capture clear videos for owners who want to monitor their cars. For professionals engaged in the work of police officers, journalists, detectives, lawyers, Taxi, etc.. Occasionally, if something unexpected happens on your way, you can record and share it on your website. What's more, as a picture & video enthusiast, you will never let beautiful scenery pass you by. So you don't need to take many big and heavy instruments, just use this high quality DVR to help you.
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Nice little camera12/1/2012
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Very clear picture. You can zoom in and read the numbers on the car. I'm pleased.0 Helpful | 1 Not Helpful


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UPDATED at 2:40 p.m., April 8.
If Washington, D.C. lawmakers fail to reach a budget deal by Friday, the federal government will shut down except for "essential" services - those include food inspectors, border patrols, and prison guards - and self-funding agencies like the U.S. Postal Service and U.S. Mint. This would include a halt to processing income tax returns submitted by mail.
Senate Majority Leader Harry Reid said this morning -April 7 - that the two sides are not far apart, with provisions on abortion and the environment being key obstacles - but he said he was not optimistic a deal would be reached by the Friday midnight deadline.
Federal tax returns would still be due April 18, but those who submit their returns by paper would experience delays in getting money back as processing of refunds would be suspended, according to the Los Angeles Times. Electronic returns, which are processed by automation, would not be delayed.
The White House says that if no agreement is reached for a budget for the last six months of the fiscal year, an estimated 800,000 federal employees would be furloughed, according to Politico and others.
The VA Long Beach Healthcare System will continue to treat patients in its hospital, emergency room and clinics during a shutdown. The Long Beach VA cares for about 45,000 veterans in any given year and has clinics in Laguna Hills, Santa Ana and Anaheim, in addition to its hospital in Long Beach.
"It's a critical function," Public Affairs Officer Richard Beam said. "We don't anticipate that changing at all."
The Department of Defense said in a statement released Thursday that inpatient and essential outpatient care would continue in its medical treatment facilities. Emergency dental care would also be available.
A government shutdown would not affect military operations in Iraq and Afghanistan, in Japan, or in support of Libya-related operations, the Pentagon said Thursday. But service members and military civilians will not be paid during the shutdown. The military will pay them retroactively for work done during a shutdown, once the Department of Defense receives funding.
The Department of Homeland Security expects to keep its "frontline" officers on duty during any government shutdown – including immigration agents, border-patrol officers and airport screeners. About 80 percent of the agency's employees are considered frontline and therefore protected from furloughs.
Members of Congress would also continue working.
It's likely that no passports would be issued or renewed, and all national museums and parks would be closed. It's likely to halt paychecks to most government workers, and could also interfere with Social Security and military benefit checks.
Federal courts will remain open, although court officials say the loss of "non-essential" personnel could paralyze operations, according to Reuters.
"During the last major shutdown, the Social Security Administration mailed checks throughout the crisis, and a close reading of established law makes clear the agency has the legal authority to do so again," according to CNN. However, it's possible that no new claims would be processed.


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Recreation Centers
Tierrasanta Recreation Center
What's Coming
Kid's Night Out 2015
Come out for some fun with Games, Movies, Arts and Craft and so much more!
See our new Kid's Night Out Flyer for detailed information!
Online Registration is here!
Visit SDRecConnect (link) to conveniently browse a variety of activities offered by the City of San Diego Park and Recreation Department and Recreation Councils. Its as easy as One, Two, and Three. Sign up for an account, find a class or activity, and register!
If you are registering a child for an activity, please do not use the childs information when completing a New Account. Adding family members after successfully creating an account can be completed later.
Please note: An email which includes a confirmation message with a special first-time activation link will be sent after signing up a new primary account. It is possible the email may go to your junk or spam folder. You will not be able to use your new account until you've used this special link!
If you need assistance,please see the FAQ (Frequently Asked Questions) link at the bottom of the SDRecConnect page.
See our new Program Guide for detailed information!
- Gymnastics
- Zumba
- Karate
- Day Camp
- Youth Basketball
- Guitar
- Piano
- Art
- Evolve Dance (Ballet)
- San Diego Civic Dance Arts
- Adult Softball
- Adult Basketball
- Yoga
- Get Fit Cross Training/Yoga/Dance Fitness
- Senior Workout
History of the Center
The center was built in 1990 to accommodate the needs of the rapidly growing family community.
Directions and Bus Route
From Interstate 15, exit Clairemont Mesa Boulevard and proceed east for two miles. The center is located on the left side of the street, just past the De-Portola Middle School.
The center is accessible by bus via SDMTS route 928.


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Dundee Capital Markets provides a wide range of financial and M&A advisory services and is recognized as a leader in the mining sector. The Mining Investment Banking team has significant experience in assisting its clients raise equity and debt capital through initial public offerings, bought deals and private placements. In addition, Dundee Capital Markets has extensive expertise in providing clients with advisory services related to acquisitions and divestitures, takeover defences, valuations and fairness opinions. As an affiliate of Dundee Corporation and with offices in Toronto, Montreal, Calgary, Vancouver and London England, Dundee Capital Markets has unparalleled insight into emerging stories and proprietary intelligence.
Acquisition of Queenston Mining
Financial Advisor
Sale of EI Cubo and Guadalupe y Calvo to Endeavour Silver
Acquisition of the Fosterville and Stawell Mines from AuRico Gold
Merger with Titan Uranium
Financial Advisor
Acquisition of St. Eugene Mining
Acquisition of Las Cruces (30%)
Acquisition of Capital Gold
Sale to Osisko Mining
Sale to Agnico-Eagle
Sale to Fronteer Gold
Sale to First Quantum
Sale to Kinross Gold
Sale to Agnico-Eagle Mines
Sale to Coeur d'Alene Mines
Public Offerings Units
Public Offering FT Common Shares
Public Offering Common Shares
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Diamond Stream Convertible Debt Senior Secured Debt Units
Private Placement Special Warrants
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Lead Manager
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Debt Facility


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Chicago Food Truck Festival at Cellular Field Armour Square Park
- Food & Drink
- 3309 S Shields Ave, Chicago, IL 60616, Chicago, IL, 60616
Fall Donation Fitness Series - Week 3: Stroller Barre
More Than Milk
Tuesday, October 1, 2013 from 11:00 AM to 12:00 PM (CDT)
All classes include workout, raffle tickets towards series drawing, giveaways, & healthy treats!
Location Specifics: Meet at the blue & white "Grill in the Park" building next to the driving range & parking lot.
Little Giraffe Foundation: An organization dedicated to funding advances in neonatal medicine and supporting NICU (Neonatal Intensive Care Unit) families.
Community Partners:
More than Milk is non-profit organization uniting, inspiring, and energizing moms through philanthropy. Together, we are supporting non-profit organizations with our babies and toddlers in tow & building a culture of giving in our families. www.morethanmilk.org


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- Home
- About
- Hydro Facilities
- Services
- Emergency
- Safety
- Forms
- Contact
In the mid eighteen hundreds, the Kesslen Shoe Factory was the largest employer in the Town of Kennebunk. Located in the heart of downtown beside the Mousam River, the factory obtained the power to operate pulleys and conveyor belts from a water wheel powered by the river. In 1893, the Town purchased the turbine rights at the Kesslen Shoe Factory. This purchase provided the necessary cash flow for the shoe factory to remain in business and started the municipally owned electric light department.
Over the years, the Kennebunk Light & Power District acquired two more factory sites on the river that had timber and dirt dams and water powered turbines. In the mid nineteen eighties the District updated the Kesslen turbine and dam, removed and replaced both dams at the other sites – Twine Mill and Dane Perkins – and upgraded the turbines at those sites as well. The three dams were licensed by the Federal Energy Regulatory Commission ("FERC") during this time period receiving a license that expires in March of 2022. Given the current complicated process for re-licensing, the FERC requires a notice of intent to be filed five years before the expiration date of the license. In order to prepare for the notice of intent filing, the District has begun the process of analyzing and evaluating the condition and value of the dams and turbines. The power point presentation you can access here will give you a detailed description of the process the District will be following over the next few years. If you have any questions, please contact the General Manager at (207) 985-3311 or firstname.lastname@example.com. If you would like to be kept informed of this process, please email your name, address, phone number and email address to firstname.lastname@example.com
July 16, 2013 Presentation on our Hyrdofacilities
March 31, 2015 Town Hall Meeting
March 31, 2015 River Modeling Presentation Click Here To Preview
March 31, 2015 Photo Simulations Click Here To Preview
Hydro Projects Activities Update
A number of activities have been completed over the summer and early fall that were noted in the last newsletter under "What is on the Horizon"?. The following summarizes these accomplishments.
The gate repair project at the Kesslen dam was completed on October 22. This project involved replacement of the gate and the mechanism that allows for safe operation of it by our employees. As many likely noticed, this effort required the drawdown of the impoundment. This upgrade project was very successful and now this operation is a much safer activity for our staff.
As required by the Federal Energy Regulatory Commission, the inspections of the three dams were completed by an experienced and licensed professional engineer from the firm of Wright-Pierce. While the inspection reports are still being developed, we are happy to report that the dams remain to be structurally sound.
The possibility of selling the three dams and hydropower projects has been identified as one option for the future of these assets. Sixteen companies were contacted to determine if they would be interested in obtaining these facilities. After going through review of information about the assets, four companies elected to move through the due diligence activities including a site visit to the facilities and review of more detailed data. At the conclusion of this process, none of the four companies elected to propose a bid for the projects.
Outreach was also done with a number of equipment vendors who specialize in new technologies for generating power under "low-head" conditions. Although this research is still underway, three companies did conduct site visits during the summer. One determined that due to other project priorities, they declined to continue their assessment of the sites. Discussions will continue with the remaining two companies and additional research will be conducted to see if other technologies can be applied.
To facilitate our efforts to address the multitude of assessments and outreach programs being planned to help understand what path should be followed regarding the three dams and hydropower projects, a new project manager was hired in late October to assist our General Manager, Sharon Staz. Wright-Pierce was awarded the contract for the services of Patricia McIlvaine, following review of five responses received to our Request for Proposal. Pat and others at Wright-Pierce have been assisting KLPD on a number of studies already, starting in early 2011. We welcome the help Pat will be able to provide in helping us move these studies along at a faster pace than we have been able to do, given all of Sharon's other responsibilities to the ratepayers.
The hydrologic modeling of the Mousam River is well underway. This modeling will help determine what the river conditions (river width and depth) at key locations would likely be during typical summer flow conditions should one or more of the dams be removed. This modeling will be completed in November. These results will also help in the development of photo-simulations being prepared at these same key locations that will be useful at future public outreach meetings to help the residents of the area have some understanding of how the river characteristics could change.


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Spectrum Geography, Grade 5 United States of America (Paperback)
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2008 Portfolio
Photographs by Robert E. Larson
by relarson
I am a self-taught amateur photographer who has been energized by the evolution of digital cameras, Photoshop and high quality ink jet printing. Composition and color drive my images.
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2009 Portfolio
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Re: Fred's Paradox
Wlady: yep. Thing about movies, you get as many takes as you want, you can shoot the story completely out of order, 'fix it in post.' Could it be that American politics, in spite of the advertising budgets, the blast faxes, the email lists, the YouTubery, still centers around very old-fashioned theater?
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I've been doing sprints and lots of cardio, workouts usually are taking like 90minutes-2hours (Warm up, Stretch, Sprints, Weights, Pylo's, Stretch). I've got school, work, homework, and other stuff so sprinting is just taking up too much time.
What would be a good routine to do, if i'm looking for a sprinter look...? Probably lots of squats, lunges, hamstring, and calve work? Lots of upper body work... cardio a couple times a week?
If you could help me come up with a weekly split that would be great, thx


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Supreme Court stops poll money ban
Bankers' group sought relief from high court
More News from Jerome Aning
More News from Philippine Daily Inquirer
The Supreme Court has issued a status quo ante order against the "money ban" resolution of the Commission on Elections (Comelec) that would have prevented the withdrawal and the carrying of large amounts of cash before and during the elections on Monday.
Chief Justice Ma. Lourdes Sereno issued the order suspending the implementation of Comelec Resolution No. 9688 upon the recommendation of Justice Arturo Brion, who wrote the majority opinion on the petition against the Comelec resolution by the Bankers Association of the Philippines (BAP).
The Comelec was given 10 days from receipt of the order to comment on the BAP petition.
The status quo ante order, though having an immediate effect, would still need confirmation by the other justices when the high court resumes regular sessions on June 4.
But when the tribunal en banc is on recess, the Chief Justice has the power to issue certain orders on its behalf, subject to the eventual approval of the entire court.
Resolution No. 9688 sought to prohibit cash withdrawals exceeding P100,000 per day and the carrying of P500,000 in cash beginning May 8, Wednesday, until May 13, Monday.
The poll body later amended the resolution, leaving it to the discretion of bank tellers and officials whether to allow cash withdrawals of at least P100,000.
President Benigno Aquino III, Justice Secretary Leila de Lima and the Bangko Sentral ng Pilipinas all expressed doubts or opposition to the resolution.
Mr. Aquino called it a "shotgun approach" that would kill good business. He said that was the reason why he had not given his concurrence to the money ban.
The BAP, in its petition to the high court, accused the Comelec of acting beyond its jurisdiction when it issued Resolution No. 9688.
"The powers conferred on [the] Comelec under the Constitution… do not include the power to impose limitations on the withdrawal of cash, encashment of checks, conversion of monetary instruments into cash, and the possession or transport of cash," it said.
The BAP also said the ban violated the constitutional right to due process, right to be presumed innocent and the non-impairment clause.
It also said the resolution invalidly amended Republic Act No. 9160, or the Anti-Money Laundering Act, and violated bank secrecy laws.
Focus on polls
Following the latest rebuff of the Comelec from the high court, Malacañang quickly called on the Comelec to focus on preparations for Monday's polls.
"Hopefully, the action of the Supreme Court will allow those concerned with the elections to focus on delivering honest, credible, and peaceful elections on Monday," said deputy presidential spokesperson Abigail Valte.
Valte stressed that the high court order was not an offshoot of the meeting between Executive Secretary Paquito Ochoa and Comelec Chair Sixto Brillantes Jr. on Thursday.
"I understand that the conversation took place before the Supreme Court decision came out," she said.
Asked whether this latest rebuke from the high court would be enough reason for Brillantes to quit, Valte said that would be up to Brillantes.
The Supreme Court has in recent months stopped or reversed at least three controversial Comelec resolutions—those on the extended liquor ban, airtime limits for political campaign advertisements and the qualifications of party-list groups. With a report from Michael Lim Ubac
First posted 12:31 am | Saturday, May 11th, 2013
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text: The Möbius strip is a fascinating geometric surface, with only a single side, which represents an unending loop of possibility and discovery. It is this characteristic that Luis Pereira Miguel Architects presents in the United Colors for Benetton Chair, designed to honor Luciano Benetton as part of Art on Chairs. The chair is a plywood construction, its frame derived from the Möbius strip folded upon itself and the seat a colorful Macramé of red, green, blue, and yellow. The hand-woven seat highlights the origins of the company, as well as exudes the elegance and vibrant creativity of the brand. via http://www.thenestway.com The Nest Way A masterpiece! I have not other words! http://www.studioattolini.com Edgardo Attolini I am sorry to inform you that the 1st Benetton Chair in the world was already designed in 2010 by Edgardo Attolini and its currently a reserved project shared personally by the designer and Mr.Luciano Benetton. Duly filed and countersigned by a deed in 2010.


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You can now filter search results with text. Example: "person: john" will search only person pages for "john".
Welcome to Killville! Hope you survive the experience!...but probably you won't.
As if the Earth getting destroyed wasn't bad enough, this section of Battleworld has gone to hell...literally.
Natasha finally confronts the leader of CHAOS and does exactly what she needs to. In record time, in fact.
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An American journalist, editor, and comic book writer.
Stan "The Man" Lee, Marvel Comics's legendary founder, is the writer and co-creator of such timeless characters as the Avengers, the X-Men, Spider-Man, Iron Man, the Fantastic Four, and the Hulk. In his words, "'Nuff said!" His best-known catch-phrase is New York's state motto, "Excelsior!!!"
James Shooter is a comic book writer, occasional fill-in artist, editor, and publisher. He has appeared as a character in a number of stories.
An American comic book artist and comic book writer. He is best known as the co-creator of Spider-Man and Doctor Strange.
Al Milgrom is a comic book writer, illustrator (pen and inks) and editor for Marvel Comics. He co-created Firestorm with Gerry Conway in 1978. He was Marvel's editor for the majority of the 80's, until being fired in early 2000 after insulting former editor Bob Harras in a phrase hidden in a comic. He has since been working for Marvel again and other comic book companies.
Arthur "Artie" Simek, sometimes credited as Art Simek (January 6, 1916 - February 1975), was an American calligrapher best known as a letterer for Marvel Comics during the period fans and historians call the Silver Age of Comic Books.
John Byrne is a prolific superhero comic book writer and artist, and is well recognized for the changes he imparts to comic mythologies. His is famed for his work on Fantastic Four, Wonder Woman, and Uncanny X-Men.
Swingin' Sammy Rosen was one of Marvel's principal letterers during the 1960's.
A colorist for Marvel.
Artist extraordinaire.
An artist mostly known for his work on the Spider-woman series from Marvel Comics and Fables from Vertigo
Michael Golden is an American comic-book artist . He is best known for his work on Marvel's "The Micronauts" in the 70's, and "The Nam" in the 1980's. He also co-created "Rogue" in the 1980's for Marvel, and "Bucky O'Hare" in the 1990's for Continuity.
A beloved comic book writer and editor. Roger Stern has had long and incredibly well-received runs on titles such as Avengers, Superman, Captain America and Spider-Man as well as countless others.
Bob Sharen is a comic book colorist.
He's worked for Marvel on "The Amazing Spider-Man", "She-Hulk", "Ghost Rider", and "the West Coast Avengers" among others.
Comic book artist.
Bill Sienkiewicz has worked on a variety of superhero books for the Big Two.
American comic artist, film storyboard artist and character designer.
Inker, been in the business for decades.
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