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Patent US7643995 - Method of automatically ranking speech dialog states and transitions to aid ... - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign inAdvanced Patent SearchPatentsA method of identifying problems in a speech recognition application is provided and includes the step of obtaining a speech application call log containing log data on question-answer (QA) states and transitions. Then, in accordance with the method, for each of a multiple transitions between states,...http://www.google.com/patents/US7643995?utm_source=gb-gplus-sharePatent US7643995 - Method of automatically ranking speech dialog states and transitions to aid in performance analysis in speech applicationsAdvanced Patent SearchPublication numberUS7643995 B2Publication typeGrantApplication numberUS 11/054,096Publication dateJan 5, 2010Filing dateFeb 9, 2005Priority dateFeb 9, 2005Fee statusPaidAlso published asUS20060178883Publication number054096, 11054096, US 7643995 B2, US 7643995B2, US-B2-7643995, US7643995 B2, US7643995B2InventorsAlejandro Acero, Dong YuOriginal AssigneeMicrosoft CorporationExport CitationBiBTeX, EndNote, RefManPatent Citations (14), Non-Patent Citations (19), Referenced by (2), Classifications (9), Legal Events (2) External Links: USPTO, USPTO Assignment, EspacenetMethod of automatically ranking speech dialog states and transitions to aid in performance analysis in speech applicationsUS 7643995 B2Abstract A method of identifying problems in a speech recognition application is provided and includes the step of obtaining a speech application call log containing log data on question-answer (QA) states and transitions. Then, in accordance with the method, for each of a multiple transitions between states, a parameter is generated which is indicative of a gain in a success rate of the speech recognition application if all calls passing through the transition passed instead through other transitions. In exemplary embodiments, the parameter is an Arc Cut Gain in Success Rate (ACGSR) parameter. Methods of generating the ACGSR, as well as systems and tools for aiding developers are also disclosed.
1. A method of identifying problems in a speech recognition application, the method comprising:
obtaining from a computer storage medium a speech application call log containing log data on question-answer (QA) states and transitions;
generating from the speech application call log, using a processor coupled to the computer storage medium, for each of a plurality of transitions between states in the speech application call log, a parameter which is indicative of a gain in a success rate of the speech recognition application if all calls passing through the transition passed instead through others of the plurality of transitions, wherein generating the parameter for each of the plurality of transitions comprises generating the parameter, for each of the plurality of transitions between the states, indicative of the gain in the success rate of the speech recognition application if all calls passing through the transition were instead evenly distributed between others of the plurality of transitions;
ranking, using the processor, the plurality of transitions as a function of their corresponding generated parameters, indicative of a gain in the success rate of the speech recognition application if all calls passing through the transition passed instead through others of the plurality of transitions, to identify transitions which will most improve performance of the speech recognition application; and
displaying on a display device the plurality of transitions ordered as a function of rank for use by a user in identifying problems in the speech recognition application in order to improve performance of the speech recognition application.
2. The method of claim 1, wherein generating the parameter for each of the plurality of transitions comprises:
generating the parameter, for each of the plurality of transitions between the states, such that it is indicative of the gain in the success rate of the speech recognition application if all calls passing through the transition passed instead through others of the plurality of transitions at a same success rate as other calls originally passing through the others of the plurality of transitions.
3. The method of claim 1, wherein the transitions correspond to arcs between states in a call transition diagram, and wherein the parameter generated for each of the plurality of transitions is an Arc Cut Gain in Success Rate (ACGSR).
determining for each of the plurality of arcs or transitions which calls passing through the arc or transition are sunken calls; and
wherein generating the parameter for each of the plurality of arcs or transitions further comprises generating the ACGSR for each of the arcs or transitions using only sunken calls for that arc or transition.
5. The method of claim 4, wherein determining for each of the plurality of arcs or transitions which calls passing through the arc or transition are sunken calls comprises:
determining which calls passing through the arc or transition are sunken calls based upon a timestamp.
6. The method of claim 4, wherein generating the ACGSR for each of the arcs or transitions comprises generating the ACGSR for each of the arcs or transitions in accordance with a relationship representable by the equation:
ACGSR  ( a ij ) = [ s i - s ij t i - t ij � t ij - s ij ] / N = ( s i � t ij - s ij � t ij - t i � s ij + t ij � s ij ) / [ N  ( t i - t ij ) ] = ( s i � t ij - t i � s ij ) / [ N  ( t i - t ij ) ] . where, aij is the arc or transition, ti is the total number of calls passing through a state qi, si as the total number of successful calls passing through the state qi, Tij is used to represent a set of calls associated with the arc aij, tij=|Tij| is used to represent the number of calls in the set, sij=|Sij| is used to represent the number of successful calls associated with the arc, and N is a total number of calls placed.
7. The method of claim 4, wherein generating the ACGSR for each of the arcs or transitions comprises generating the ACGSR for each of the arcs or transitions in accordance with a relationship representable by the equation:
ACGSR  ( a ij ) = [ s i - s ij t i - t ij � t ij - s ij ] / N = ( s i � t ij - s ij � t ij - t i � s ij + t ij � s ij ) / [ N  ( t i - t ij ) ] = ( s i � t ij - t i � s ij ) / [ N  ( t i - t ij ) ] . where, aij is the arc or transition, ti, is the total number of calls passing through a state qi, si as the total number of successful calls passing through the state qi, Tij is the set of sunken calls of the arc aij, tij =|T ij| is used to represent the number of calls in the set, sij=|Tij| is used to represent the number of successful sunken calls associated with the arc, and N is a total number of calls placed.
8. A computer storage medium having stored thereon computer-executable instructions for implementing speech recognition application problem identifying steps comprising:
accessing a speech application call log containing log data on question-answer (QA) states and transitions, wherein the transitions correspond to arcs between states in a call transition diagram;
generating, for each of a plurality of transitions between states, a parameter which is indicative of a gain in a success rate of the speech recognition application if all calls passing through the transition were routed instead through others of the plurality of transitions using a processor of the computer, wherein the parameter generated for each of the plurality of transitions is an Arc Cut Gain in Success Rate (ACGSR), and wherein generating the parameter for each of the plurality of arcs or transitions further comprises generating the ACGSR for each of the arcs or transitions using only sunken calls for that arc or transition.
9. The computer storage medium of claim 8, and having further stored thereon computer-executable instructions for implementing further speech recognition application problem identifying steps comprising:
ranking the plurality of transitions as a function of their corresponding generated parameters; and
displaying the plurality of transitions as a function of rank.
10. The computer storage medium of claim 9, wherein generating the parameter for each of the plurality of transitions comprises:
11. The computer storage medium of claim 9, wherein generating the parameter for each of the plurality of transitions comprises:
generating the parameter, for each of the plurality of transitions between the states, indicative of the gain in the success rate of the speech recognition application if all calls passing through the transition were instead evenly distributed between others of the plurality of transitions.
12. The computer storage medium of claim 8, wherein determining for each of the plurality of arcs or transitions which calls passing through the arc or transition are sunken calls comprises:
determining which calls passing through the arc or transition are sunken calls based upon a number associated an event.
13. The computer storage medium of claim 8, wherein generating the ACGSR for each of the arcs or transitions comprises generating the ACGSR for each of the arcs or transitions in accordance with a relationship representable by the equation:
14. The computer storage medium of claim 8, wherein generating the ACGSR for each of the arcs or transitions comprises generating the ACGSR for each of the arcs or transitions in accordance with a relationship representable by the equation:
ACGSR  ( a ij ) = [ s i - s ij t i - t ij � t ij - s ij ] / N = ( s i � t ij - s ij � t ij - t i � s ij + t ij � s ij ) / [ N  ( t i - t ij ) ] = ( s i � t ij - t i � s ij ) / [ N  ( t i - t ij ) ] . where, aij is the arc or transition, ti is the total number of calls passing through a state qi, si as the total number of successful calls passing through the state qi, Tij is the set of sunken calls of the arc aij, tij=|Tij| is used to represent the number of calls in the set, sij=|Sij| is used to represent the number of successful sunken calls associated with the arc, and N is a total number of calls placed.
CROSS-REFERENCE TO RELATED APPLICATIONS Reference is hereby made to the following co-pending and commonly assigned patent application: U.S. application Ser. No. 11/054,301, filed Feb. 9, 2005, entitled �INTERACTIVE CLUSTERING METHOD FOR IDENTIFYING PROBLEMS IN SPEECH APPLICATIONS�, which is hereby incorporated by reference in its entirety.
BACKGROUND OF THE INVENTION The present invention generally pertains to speech recognition applications and systems. More specifically, the present invention pertains to methods of identifying and analyzing performance problems during development of speech recognition applications.
Although great progress has been made in speech recognition technology during the past two decades, adoption of speech technology has not gone as smoothly as might be desired. This is evidenced by the relatively limited deployment of speech applications. The main barrier to wider adoption of speech recognition technology stems from the cost incurred in developing the speech applications. A typical speech application includes application logic, dialog, one or more grammars, a speech recognition engine, etc. Since it is unlikely that the system will be available right at the very beginning (i.e., without significant development), constructing a quality speech application usually involves four steps, namely, (1) design; (2) prototyping; (3) evaluation and tuning; and (4) deployment. Typically, the prototyping step and the evaluation and tuning step are performed iteratively, fine tuning the speech application through each iteration to improve performance.
Among these four steps, evaluation and tuning during test and pilot stages is one of the most important phases and usually costs most. The tuning phase can take many months and requires a team of developers, testers, and speech technology experts.
Significant efforts have been directed toward the goal of reducing the total cost needed to develop and deploy speech applications. For example, a series of development tools have recently been released by companies working in speech recognition. The development tools significantly improved the development process. However, some critical functionality is not available in these tools. For instance, speech application authors usually don't know what to do when the success rate of their application is not satisfactory, even though they have access to large quantities of application logs. It would therefore be very valuable to automatically (or semi-automatically) determine what callers are struggling with, or which parts of their application need the most work.
SUMMARY OF THE INVENTION A method of identifying problems in a speech recognition application is provided and includes the step of obtaining a speech application call log containing log data on question-answer (QA) states and transitions. Then, in accordance with the method, for each of a multiple transitions between states, a parameter is generated which is indicative of a gain in a success rate of the speech recognition application if all calls passing through the transition passed instead through other transitions. In exemplary embodiments, the parameter is an Arc Cut Gain in Success Rate (ACGSR) parameter. Methods of generating the ACGSR, as well as systems and tools for aiding developers are also disclosed.
FIG. 2 is a block diagram illustrating an algorithm for constructing a call transition diagram (CTD) from a call log.
FIG. 3 is a diagrammatic illustration of a tree type call-state graph (CSG).
FIG. 4 is a diagrammatic illustration of a CSG with loops and merges.
FIG. 5-1 is a block diagram illustrating a system or tool in accordance with an embodiment of the present invention.
FIG. 5-2 is a flow diagram illustrating a method in accordance with the present invention.
FIG. 6-1 is a diagrammatic illustration of an example of an initial interactive clustering (IC) step in which all calls are clustered into one unlabelled class.
FIG. 6-2 is a diagrammatic illustration of an example of a later interactive clustering (IC) step in which, after six calls are labeled, calls are automatically clustered into labeled classes and an unlabeled class.
FIG. 7 is a diagrammatic illustration of a call flow graph.
FIG. 8 is a screen shot of a user interface (UI) of a system or tool of the present invention.
FIG. 9 includes two tables illustrating performance evaluation data.
DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS 1. Exemplary Environments
Various aspects of the present invention pertain to methods and apparatus for identifying problems in speech recognition applications. The methods and apparatus are useful in reducing the speech recognition application development time and to enhance performance of the applications. Both of these results can lead to reduced development costs and wider use of speech recognition technology.
Examples of well-known computing systems, environments, and/or configurations that may be suitable for use with the invention include, but are not in any way limited to, personal computers, server computers, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, and set top boxes. Embodiments of the present invention can be implemented in the development of a wide variety of speech recognition applications, for example including voice-dialing systems, call routing systems, voice messaging systems, order management systems, or any application using dialog turns (i.e., question-answer or �QA� states and transitions) and a speech recognition engine to recognize speech from a user to guide the user to a desired result. These are simply examples of systems with which the present invention can be used to improve the development process.
It should be noted that the present invention can be carried out on a computer system such as that described with respect to FIG. 1. However, the present invention can be alternatively carried out on a server, a computer devoted to message handling, a distributed system in which different portions of the present invention are carried out on different parts of the distributed computing system, etc.
2. Speech Application Problem Identification Methods and System
The present invention includes methods, apparatus and systems which can be used to reduce the time and efforts needed by developers to identify problems in their applications during the evaluation and tuning phase of speech application development. It is assumed that developers have access to the speech application logs containing data on Question Answer (QA) states and transitions. The present invention can then be used to aid developers by mining and analyzing the log data. While in embodiments of the present invention the logs are referred to as �call logs�, inferring for example that the speech recognition application can be part of a telephone based system (for example a voice dialing or call center application), the invention is not limited to this type of application or to use with this type of logs. Use of the phrase �call logs� is intended to represent logs pertaining to telephone calls interacting with the speech recognition application, as well as any logs of interaction (�calls�) between a user and a speech recognition application, whether telephone based or not.
The approach used in some embodiments of the invention includes two primary steps. In the first step, calls are clustered based on QA states and transitions, and important QA states and transitions are identified based on a new criterion referred to here as Arc Cut Gain in Success Rate (ACGSR). The invention also includes an algorithm or method to estimate the ACGSR by analyzing the Call Transition Diagram (CTD) automatically inferred from the speech application log data. In the second step, calls associated with important QA transitions are clustered through a new approach referred to here as Interactive Clustering (IC). The purpose of IC is to automatically cluster calls that are similar to those labeled by the users, so that users can focus on checking those unlabeled calls and identifying new problems. Experiments on an auto-attendant application named �MS Connect� show that the approaches of the present invention can dramatically reduce the time and efforts needed to identify problems in speech applications.
The above concepts and methods are described below as follows: First, the step or method of clustering calls based on QA states and transitions, and ranking QA transitions, is described. Specifically, the concept of ACGSR for measuring the importance of the QA transitions is introduced, and an algorithm to estimate ACGSR is provided. Then, the step or method of identifying problems in the calls associated with those important QA transitions is illustrated. The concept of IC is described, and semi-supervised mixture model based algorithms for it are derived/disclosed. Finally, the performance evaluation of the algorithms on MS Connect log is reported. It will be shown that the present invention does significantly reduce the time and efforts needed by developers to identify problems in their applications.
II. Cluster and Rank QA Transitions
In this section, the step or method of clustering calls based on QA states and transitions, and ranking QA transitions based on ACGSR, is described. First, the concept of a call transition diagram (CTD) is introduced, as is a way to automatically construct a CTD from speech application logs. The concept of ACGSR is then described, and an ACGSR estimation algorithm is derived/disclosed. After that, issues related to presenting the ranking results are discussed. It is emphasized that the ultimate goal of performance evaluation and tuning is to improve the success rate of a speech application.
Definition 1: The success rate r is defined as the percentage of successful calls, i.e., calls that fulfilled their tasks, over all calls placed: r=S/N.
Here S is the number of calls which fulfilled their tasks and N is the total number of calls placed. Fulfillment of a task is defined by the corresponding speech application(s), and for example can be calls transferred in an auto-attendant application, or balance checked in an automatic support application.
A. Call Transition Diagram and its Construction
Definition 2: A Call Transition Diagram (CTD) is a 3-tuple CTD=(Q, A, T), where:
Q is a finite set of states Q={qi|i=1, . . . , I}; A is a finite set of arcs that A={aij}⊂(Q�Q); T is the set of calls passing through the arcs: T:A→SC, where C is the set of all calls placed C={cn|n=1, . . . , N}and SC is the superset of the set C.
Note that the CTD differentiates itself from the traditional Call Flow Diagram (CFD) in that the CTD contains the set of the calls passing through each arc. The CTD is also different from the traditional Markov Model description of the Call states. In the Traditional Markov Model description, the number of times a state and/or arc is visited determines the model parameters, while in the CTD the number of distinguished calls visiting each arc is measured. No matter how many times the same call has visited the same arc, it's counted as one call in the CTD. The reason for this difference is that more interest is placed in the successful calls rather than the events.
To make the following discussion easier, Tij is used to represent the set of calls associated with the arc aij, and tij=|Tij| is used to represent the number of calls in the set. Similarly, Sij is used to represent the set of successful calls associated with the arc aij, and sij=|Sij| is used to represent the number of successful calls associated with the arc.
The construction of the CTD from the application logs is straight forward. Table I shown in FIG. 2 summarizes an algorithm for performing such a construction. Note that, during the CTD construction process, calls are clustered based on the QA states and transitions they have visited.
B. ACGSR and Its Estimation
Once the CTD is obtained, those QA states and transitions that need the most attention from developers must be identified. One method of identifying these QA states is to list the QA states and transitions based on the local unsuccessful rate. This approach, however, is not optimal since the improvement in the local success rate does not necessary translate into the improvement in the overall success rate. For example, a QA state at the later stage with a high unsuccessful rate may not be worth working on since the total gain achieved by improving this QA state might be very limited. This suggests that a good criterion is critical to the effectiveness of the ranking. As mentioned previously, the criterion proposed here is referred to as ACGSR.
Definition 3: The Arc Cut Gain in Success Rate (ACGSR) is the change of success rate if all calls originally passing through an arc are distributed to other arcs with the same start state (as if the arc is cut from the CTD) given that all other system parameters are unchanged.
ACGSR essentially measures how important it is to reduce the number of calls passing through a specific arc. The higher the ACGSR, the more important it is. Unfortunately, ACGSR is not directly available from the CTD (or logs) and needs to be estimated. In accordance with one embodiment, to estimate ACGSR, it is assumed that calls redirected to other arcs have the same success rate as the calls originally passing through those arcs. If the CTD is a tree (with no loop and no merge) as represented in the call-state graph (CSG) 300 shown in FIG. 3, ACGSR of arc aij (designated by reference number 302) between states qi and qj (designated by reference numbers 304 and 306, respectively) can be estimated using Equation 1:
ACGSR  ( a ij ) = [ ∑ k ≠ j  s ik ∑ k ≠ j  t ik � t ij - s ij ] / N Equation   1 Defining ti as the total number of calls passing through the state qi, and si as the total number of success calls passing through the state qi, i.e.,
t i = ∑ k  t ik Equation   2 s i = ∑ k  s ik Equation   3 Equation 1 thus becomes Equation 4:
ACGSR  ( a ij ) = [ s i - s ij t i - t ij � t ij - s ij ] / N = ( s i � t ij - s ij � t ij - t i � s ij + t ij � s ij ) / [ N  ( t i - t ij ) ] = ( s i � t ij - t i � s ij ) / [ N  ( t i - t ij ) ] . Equation   4 To summarize, for the tree type CTD represented by CSG 300 illustrated in FIG. 3, estimation of the ACGSR of arc aij is based upon the scenario where calls originally passing through of arc aij are now redirected to other arcs (such as the arcs designated by reference numbers 308, 310, 312 and 314) whose start state is qi.
When the CTD is not a tree, however, Equation 4 does not hold any more due to the fact that the same call may flow out of a state several times through different arcs. Consider as an example the CTD represented by CSG 325 shown in FIG. 4 having incoming calls 326 entering state qi (represented by reference number 333) and passing through arcs aik, aij and aii (designated by reference numbers 327, 329 and/or 331) in route to states qk and qj (designated by reference numbers 335 and 337). This CSG includes loops and merges. Assume that calls 1, 2, and 3 passing through arc aij are failed and calls 4 and 5 passing through arc aik are successful. It can be seen that the success rate does not change if the arc aii is cut since all calls passing though aii have already been counted in the arcs aij and aik. However, if the arc aij is cut, all calls originally failed are now turned into successful calls and a big gain in the success rate is achieved.
The example illustrated in FIG. 4 suggests that only the calls flowing out of the start state 333 (no looping back) through the arc should be considered when estimating the ACGSR.
Definition 4: A call loops back to the start state qi of the arc aij from the end state qj if there is a list of arcs {ax k y k |k=1, . . . , K} such that x0=i, xk+1=yk, yK=j, and the call passes through them sequentially.
Definition 5: A sunken call of the arc aij is a call that eventually passes through arc aij and does not loop back to the start state qi again.
Theorem 1: A call c passing through the non-stop state qi is a sunken call of one and only one arc whose start state is qi.
Proof: Since the state is a non-stop state, the call must go out of the state qi and ends at another state. This means that c is a sunken call of at least one arc whose start state is qi. For the same reason, the call can't be a sunken call of multiple arcs with the same start state.
Definition 6: A timestamp is a number associated with each event. Later events have higher timestamps than the earlier ones. A timestamp can be real time or any number (e.g., event ID) that increases over time. In an example embodiment, time(c, aij) is used to indicate the timestamps of the event that the call c passes through the arc aij. Since each call may pass through the same arc several times, max(time(c, aij)) is used to indicate the latest time c passes through aij.
Theorem 2: A call c associated with arij is a sunken call of aij if and only if max(time(c, aij))>max(time(c, aki))∀k
Proof: If call c is a sunken call, then the call eventually flows out through arc aij and does not loop back again. This means that later (higher timestamp) events that are associated with call c do not occur in all arcs whose end state is qi.
Contradictory proof: If call c is not a sunken call, then there is a list of arcs {ax k y k |k=1, . . . , K} such that x0=i, xk+1=yk, yK=j, and the call passes through them sequentially. Referring to Equation 5, this means:
max  ( time  ( c , a ij ) ) <  time  ( c , a jy 1 ) <  time  ( c , a y 1  y 2 ) <  � <  time  ( c , a y K  i ) ≤  max  ( time  ( c , a y K  i ) ) .  Equation   5 The final algorithm of ACGSR estimation can be obtained by redefining Tij (recall from above that tij=|Tij|) as the set of sunken calls of the arc aij in Equation 5. Then, Equation 4 still holds for calculation of ACGSR.
C. Present Ranking Results to the Users
To present the result to developers, arcs are sorted on the estimated ACGSR in descending order. The higher the ACGSR for an arc or transition, the larger the gain in success rate which can be achieved by improving the QA state and transition. In addition, two other issues should be considered:
First, developers are interested in knowing how they may improve the system. For this reason, those arcs with negative ACGSR are not informative to them and should not be displayed.
Second, developers cannot manage to reduce the number of calls passing through some arcs even though those arcs may have large ACGSR values. For example, an application may have a menu for the callers to select from. Transitions to any state associated with an item in the menu should not be cut. Two solutions to this problem are provided: Developers can either mark these arcs in the CSG before running the tool or method of the present invention, or select to hide the transition in the displayed report.
The ACGSR estimation algorithm is not only useful in determining the most important QA states and/or transitions for developers to work on, but is also useful in many other areas. For example, by changing the grammar used in a QA state, the call flow would diverge from the original path. The ACGSR estimation algorithm can be used to estimate the total success rate gain after the grammar change without running the updated application.
D. General System and Method
FIG. 5-1 is a block diagram illustrating general system 500 configured to implement the methods of the present invention described above. System 500 includes an ACGSR generating component 504 configured to implement one or more of the above-described methods to generate the ACGSR for transitions or arcs using data in call log 502 pertaining to question-answer (QA) states and transitions. In some embodiments, component 504 can also generate call transition diagrams using, for example, the algorithm shown in FIG. 2. Also, in other embodiments, component 504 generates parameters other than the ACGSR as described above. However, in exemplary embodiments, at least one of these other parameters would still be indicative of a gain in the success rate of the speech recognition application if all calls passing through a transition passed instead through others of a plurality of transitions.
In system 500 illustrated in FIG. 5-1, a ranking component receives the ACGSR scores or other parameters, and ranks the transitions based on these scores or parameters. Then, graphical user interface (GUI) generating component 508 generates a GUI to display the transitions to a user as a function of rank. An example of such a GUI is described below with reference to FIG. 8.
FIG. 5-2 is a flow diagram 520 illustrating in more general terms a method as described above. As can be seen at block 522, the method includes obtaining a speech application call log containing log data on QA states and transitions. At block 524, the method is shown to include the step of generating a parameter (e.g., the ACGSR) for each desired transition which is indicative of the gain in success rate of the application if all calls passing through the transition passed instead through other transitions. Then, the method optionally includes the steps shown at blocks 526 and 528 of ranking or sorting the transitions as a function of their generated parameters, and displaying the transitions as a function of their rank. These steps are optional in some embodiments because it is possible that the generated ACGSRs or other parameters could be used in an automated fashion to correct identified problems without displaying the ranked transitions to a user.
III. Identify Problems through Interactive Clustering
Once the most important QA states and/or transitions are identified, it is desirable to group calls passing through these arcs into clusters based on the cause of problems. Ideally, the whole process should be automatic. However, there are several difficulties of doing so. First, it is impossible to know all possible causes of the problems, and it is therefore impossible to train classifiers for them before hand. Second, different applications may log different information and it is the additional information that separates one type of problem from another. Third, the parameters in different applications are different and so it's difficult to generate a set of universal classifiers.
For these reasons, the present invention addresses the problem from a different perspective, aiming to reduce the time and efforts needed for the developers to identify problems through Interactive Clustering (IC).
Definition 7: Interactive Clustering (IC) is a clustering process based on semi-supervised clustering technology. During initialization, an IC method or component clusters calls based on prior knowledge such as pre-trained classifiers. If no prior knowledge is available, IC keeps all calls in one cluster or groups them with unsupervised clustering. The developer checks unlabeled calls and labels them (with the cause of the problem, for example). The developer's interaction is used as supervision and/or feedback for IC to adjust and label other calls. This process goes on until all calls are labeled and the developer is satisfied with the labels.
The benefit of IC is clear. With a brute force approach, the developer needs to check, for example, 400 calls associated with a special QA transition to identify most of the problems. During this process, the developer frequently will become frustrated since he/she is checking calls with the same causes again and again. A slightly better approach is random sampling in which it is hard to determine the number of samples needed. A large number of samples typically requires lots of redundant work, and a small number of samples typically results in a low probability of finding all problems.
With IC, things are different. Once the developer labels a call, all similar calls are grouped together and labeled. The developer only needs to focus on those unlabeled calls which are usually associated with new causes. The number of calls the developer needs to check overall is thus greatly reduced.
FIGS. 6-1 and 6-2 illustrate an example of the IC method. Originally, all calls 350 (represented as circles) are unlabeled and clustered into one unlabeled class 352 as indicated in FIG. 6-1. After six calls are labeled (represented as darkened circles 350-1 through 350-6), calls are clustered into three classes, two of which are labeled (designated by reference numbers 354 and 356) as indicated in FIG. 6-2. The next call the developer needs to check is from the unlabeled class 352. The main component of IC is a semi-supervised, finite mixture distribution model based, clustering algorithm.
A. Membership Determination
The goal is to find the best classification for each call in which instances similar to labeled data are labeled accordingly the same or similarly, given a large set of unlabeled data instances and a small set of labeled data instances. For the sake of easy discussion, a variable is denoted by a capitalized token (e.g., Xi), and the value of the variable by that same token in lower case (e.g., xi). We use p(x|y) to denote the probability that X=x given Y=y or a probability distribution for X given Y.
The disclosed approach to cluster the whole data set X={xi|i=1 . . . , I} into J clusters C={cj|j=1, . . . , J} is mixture distribution model based. Given the model parameters λ, the probability that the instance xi belongs to class cj is:
p  ( c j ❘ x i , λ ) = p  ( c j ❘ λ )  p  ( x i ❘ c j , λ ) ∑ j ′ = 1 J  p  ( c j ′ ❘ λ )  p  ( x i ❘ c j ′ , λ ) , Equation   6 where p(xi|cj, λ) is the probability that the instance xi is generated by the class cj and p(cj|λ) is the prior of different classes. It is assumed that the prior follows Bernoulli distribution, i.e., without knowing the value of the data instance, the probability that it belongs to class cj is:
p(c j|λ)=πj� Equation 7
p  ( c j ❘ x i , λ ) = π j  p  ( x i ❘ c j , λ ) ∑ j ′ = 1 J  π j ′  p  ( x i ❘ c j ′ , λ ) Equation   8 The probabilities p(cj|xi, λ) are called membership probability. Once these probabilities have been computed, the data instance can either be assigned to the cluster with the highest probability (a hard assignment), or the data instance can be assigned fractionally to the set of clusters according to this distribution (a soft assignment). In exemplary embodiments, when the result is presented to the user, hard assignment is used.
B. Model Parameter Learning
The model parameters are estimated with an Expectation-Maximization (EM) algorithm. The EM algorithm starts with a set of initial values of the parameters, and then iterates between an Expectation (or E) step and a Maximization (or M) step until the parameter values converge to stable values. Given the model parameters λ, the probability that the instance xi is generated by the model is:
p  ( x i ❘ λ ) = ∑ j = 1 J  p  ( c j ❘ λ )  p  ( x i ❘ c j , λ ) = ∑ j = 1 J  π j  p  ( x i ❘ c j , λ ) . Equation   9 The probability that all data instances are generated by the model is:
p  ( X ❘ λ ) = ∏ i = 1 I  p  ( x i ❘ λ ) = ∏ i = 1 I  ∑ j = 1 J  π j  p  ( x i ❘ c j , λ ) . Equation   10 The posterior probability of model parameter λ is:
p  ( λ ❘ X ) = p  ( X ❘ λ )  p  ( λ ) p  ( X ) , Equation   11 where p(λ) is the prior distribution of λ. The maximum a posteriori (MAP) parameter is defined as:
λ MAP = arg   max λ  p  ( λ ❘ X ) = arg   max λ  p  ( X ❘ λ )  p  ( λ ) = arg   max λ  ( log   p  ( X ❘ λ ) + log   p  ( λ ) ) . Equation   12 When used in conjunction with vague or non-informative priors, MAP estimates are smoothed versions of Maximum Likelihood (ML) estimates. In one embodiment, Dirichlet distribution is chosen as the prior for the multinomial distribution with parameters φ=(φl, . . . , φN)
p  ( ϕ 1 , ... , ϕ N ❘ α 1 , ... , α N ) = Γ  ( ∑ n = 1 N  α n ) ∏ n = 1 N  Γ  ( α n )  ∏ n = 1 N  ϕ n α n - 1 Equation   13 In one embodiment, αl= . . . =αN=l/N is chosen.
Given the current parameters λ, the new parameters λ′ is thus estimated to maximize the following function:
Q  ( λ ′ , λ ) = E  ( p  ( λ ′ ❘ X ) ) = ∑ i = 1 I  ∑ j = 1 J  p  ( c j ❘ x i , λ )  [ log   p  ( X ❘ λ ′ ) + log   p  ( λ ′ ) ] . Equation   14 Assume that each data instance is an ordered vector of K attribute values xi={xik|k=1, . . . K}. It is further assumed that attributes are independent with each other, and therefore:
p  ( x i ❘ c j , λ ) = ∏ k = 1 K  p  ( x ik ❘ c j , λ ) Equation   15 Each attribute can have either nominal values or real values. Multinomial value attributes are modeled with Bernoulli distribution: λik={qjkl, . . . , qjkl k }. Real value attributes are modeled with Gaussian distribution: λjk={μjk, σjk}.
Maximizing the Q function with respect to each subset of parameters, the algorithms to update parameters can be obtained.
For prior of class distribution:
π ′ j = ∑ i = 1 I  p  ( c j ❘ x i , λ ) + 1 / J ∑ j ′ = 1 J  ∑ i = 1 I  p  ( c j ′ ❘ x i , λ ) + 1 Equation   16 For multinomial (Bernoulli distribution) parameters:
q ′ jkl = ∑ i = 1 I  p  ( c j ❘ x i , λ )  δ  ( x ik , l ) + 1 / L k ∑ j ′ = 1 J  ∑ i = 1 I  p  ( c j ′ ❘ x i , λ )  δ  ( x ik , l ) + 1   where : Equation   17 δ  ( x , v ) = { 1 , if   x = v 0 , otherwise Equation   18 For Gaussian parameters:
μ jk ′ = ∑ i = 1 I  p  ( c j ❘ x i , λ )  x ik ∑ j ′ = 1 J  ∑ i = 1 I  p  ( c j ′ ❘ x i , λ )  x ik Equation   19 σ jk ′ = ∑ i = 1 I  p  ( c j ❘ x i , λ )  ( x ik - μ jk ′ ) ∑ j ′ = 1 J  ∑ i = 1 I  p  ( c j ′ ❘ x i , λ ) + 1 Equation   20 At the initialization step, random probabilities are assigned to p(cj|xi, λ) which is used to estimate the initial model.
C. Supervision As Constraint and Feedback
Thus far, the clustering algorithm has been discussed without any labeled data. To use the data instances labeled by users, in some embodiments, the importance of the data instance is artificially boosted to the estimation of the model parameters:
p(c j |x i, λ)=B�δ(j, Lab(i)) Equation 21
where Lab(i) is the label of data point xi, and B is the boost factor. Boost factor B is originally set to 1 and doubles its value every time the converged result does not have labeled data correctly classified. It is straight forward to see that the labeled data is used both as constraint and as feedback in this approach.
Since it is a goal to help users to focus on calls that are not similar to those already labeled, the number of clusters in the clustering algorithm is set to one plus the number of known classes. In other words, J is set to 1 initially and increases by 1 each time a new class is discovered by the user. The IC method or algorithm stops when the number of instances in the unlabeled cluster is zero and the user is satisfied with the labels.
IV. Experiment and Evaluation
The present invention also includes a tool or component, which implements the above-described approach, for use by developers. This tool has been evaluated on the MS Connect application. MS Connect is an auto-attendant application based on the Microsoft Speech Server platform. It allows users to speak the name of the person they wish to contact and provides them with a variety of messaging options (transfer to office, transfer to cell, or leave a voice recording as an email attachment). When used to evaluate the tool, the MS Connect Service had 33 QA states and 137 transitions, and serves to transfer calls to more than fifty thousand employees of Microsoft Corporation. FIG. 7 illustrates part of the Call Flow Graph 400.
For performance monitoring and tuning purposes, all calls are logged until they are either transferred or hanged up. In the experiments used to evaluate the tool of the present invention, log data collected in June 2004 was used.
FIG. 8 is a screenshot or snapshot illustrating a graphical user interface (GUI) 550 of the tool. In this particular configuration of GUI 550, pane 560 is included, showing QA transitions sorted based on ACGSR scores (from highest ACGSR at the top to lowest ACGSR at the bottom). Each transition shown is followed by two sets of numbers. The first set of numbers indicates the absolute and percentage gain of ACGSR. The second set of numbers indicates the absolute and percentage of calls passing through the arc. The user can select a transition and QA in pane 560 using an input (such as a mouse click) from an input device. In the illustrated example, transition 562 is selected as represented by its highlighting in the display. In pane 570, calls associated with selected transition (in this case transition 562) are displayed. Again, the user can select a call in pane 570. In the illustrated example, call 572 (having Call ID 18830 is selected). In pane 580, the Call QA Summary for a selected call 572 is illustrated.
From FIG. 8, it can be seen that the transition which needs most work (having the highest ACGSR is the transition 564 from Phase1AskQA to FarEnd (hanged up by the caller). From pane 560, it can be seen that more than 10% (or 222) of all calls pass through this transition. As can also be seen in pane 560, if the calls flowing through this transition can be eliminated completely, an absolute 8.5% (or 189 calls) improvement in the success rate can be achieved. This information clearly indicates to the developer that this transition should be worked on, as it will have a significant impact on the performance of the system.
Another interesting finding is that 1.5% of all calls pass through the transition 566 from SayCallingContact to NearEnd (hanged up by the system) which was unexpected. Fixing problems in this transition results in a 1.5% gain in success rate. As can be seen from this experimental data, ACGSR clearly identified those QA states and transitions that developers should pay most attention to.
Evaluating the effectiveness of IC methods requires additional work. Traditionally, clustering algorithms are evaluated with criteria such as confusion matrix, precision, recall, F1 measure, balancing, purity, entropy, and mutual information. These criteria do not fit here since the purpose of IC is to identify all problems as fast as possible. Two obvious criteria are:
Criterion 1: The PPI is the Percentage of Problems Identified over all problems existing under the assumption that the developer can label each call he/she has checked. The best PPI possible is 100%.
Criterion 2: The ANCL (Average Number of Calls Labeled) is the total number of calls labeled manually over all problems identified. The best possible ANCL is 1.
Another criterion is:
Criterion 3: The Problem Distribution Accuracy (PDA) is the accuracy of the number of calls in each class. It is defined as the K-L (Kullback-Leibler) distance between the problem distribution obtained from IC and the true problem distribution:
PDA = ∑ i  p ^ i  log  ( p ^ i / p i ) , Equation   22 where {circumflex over (p)}i is the estimated percentage of calls in class i, and pi is the true percentage of calls in the class i. The best possible PDA is 0. See S. Kullback & R. Leibler, �On Information and Sufficiency�, Annals of Mathematical Statistics 22, 79-86, 1951.
PDA is related to the final goal of the analysis since it tells developers the order of problems they should work on and the estimated success rate gain after resolving each problem.
We evaluated IC using two sets of data generated from the ACGSR ranking and clustering step. The first set of data contains 459 calls classified into four types of problems with distributions 338, 36, 53, and 32. The second set of data contains 896 calls in four clusters each of which has 706, 69, 67, and 54 calls. The feature set we used in this experiment includes promptID, prompt barged in status, DtmfID, repetition of passing, duration, and engine confidence scores. We ran four independent experiments for each data set and compared IC with the brute force and random sampling approach. Tables 2 and 3 shown in FIG. 9 illustrate the results.
The brute force approach guarantees the correct identification of all problems and distributions. However, it requires checking of 115 calls in data set one and 224 calls in data set two for each problem identified. The random sampling approach gives better results. To compare apples to apples with IC, two sampling configurations were conducted. The first approach is to fix the sampling size (equivalent to ANL fix) and the second approach is to fix the percentage of problems identified (PPI) on average. The result shows that with the same ANL, IC can detect 93.8% of problems in both data sets and the sampling approach can only detect 62.5% of problems. To detect 93.8% of problems, IC requires checking of only 1.63 calls per problem in both data sets, while the sampling approach requires checking 14 calls per problem in data set one and 21 calls per problem in data set two. Furthermore, ANL increases very slowly using IC and much faster using the sampling approach with larger data sets. In both configurations, IC provides more accurate distribution than the sampling approach.
In conclusion, the methods of the present invention have been described in the context of log analysis and its application to speech application problem identification. It has been shown that using ACGSR as the criterion to rank the QA state and transitions, and using IC to help developers find problems quicker, can significantly shorten and improve the development process. Specifically, it has been demonstrated that the present invention can significantly reduce the time and effort required of developers to identify problems in their speech applications.
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D., "Interpreting and Extending Classical Agglomerative Clustering Algorithms using a Model-Based Approach", Proceedings of the Nineteenth International Conference on Machine Learning (ICML-2002), 2002.14Nigam et al., K., "Text Classification from Labeled and Unlabeled Documents using EM" Machine Learning, vol. 39, Nos. 2/3, pp. 103-134, May/Jun. 2000.15Smyth, P., "Probabilistic Model-Based Clustering of Multivariate and Sequential Data", In Proceedings of Seventh International Workshop on Artificial Intelligence and Statistics, San Francisco, California, pp. 299-304, 1999.16Wagstaff et al., K., "Constrained K-means Clustering with Background Knowledge", Proceedings of the Eighteenth International Conferences on Machine Learning (ICML-2001), pp. 577-584, 2001.17Zeng et al., H., "CBC: Clustering Based Text Classification Requiring Minimal Labeled Data", Proceedings of IEEE International Conference on Data Mining, Melbourne, Florida, 2003.18Zhong, S., "Probabilistic Model-based Clustering of Complex Data", doctoral dissertation, Department of Electrical and Computer Engineering, The University of Texas at Austin, Aug. 2003.19Zhong, S., "Semi-Supervised Model-based Document Clustering: A Comparative Study", Department of Computer Science and Engineering, Florida Atlantic University, 2004.Referenced byCiting PatentFiling datePublication dateApplicantTitleUS8099279Feb 9, 2005Jan 17, 2012Microsoft CorporationInteractive clustering method for identifying problems in speech applicationsUS20070168190 *Oct 30, 2006Jul 19, 2007Konica Minolta Business Technologies, Inc.Information processing apparatus with speech recognition capability, and speech command executing program and method executed in information processing apparatus* Cited by examinerClassifications U.S. Classification704/251, 704/246, 704/254, 704/9International ClassificationG10L15/00Cooperative ClassificationG10L15/083, G10L15/01European ClassificationG10L15/01, G10L15/08NLegal EventsDateCodeEventDescriptionMar 18, 2013FPAYFee paymentYear of fee payment: 4Mar 29, 2005ASAssignmentOwner name: MICROSOFT CORPORATION, WASHINGTONFree format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:ACERO, ALEJANDRO;YU, DONG;REEL/FRAME:015833/0462Effective date: 20050207RotateOriginal ImageGoogle Home - Sitemap - USPTO Bulk Downloads - Privacy Policy - Terms of Service - About Google Patents - Send FeedbackData provided by IFI CLAIMS Patent Services©2012 Google

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text:
a: Altered Book: A Sense of Place - Progress
All of my altered book spreads got finished Friday evening and Saturday, except the sign-in page. The cover, just masking taped to death, then coated with white gesso. This is just a base prep; I won't do the cover until after it comes back to me with the other 3 women's spreads. On the left is the tag that goes into the sewn triangular pocket. This one is my favorite layout. These are the back sides of 2 vintage envelopes, which I stamped then painted in. The tags inserted into the vintage envelopes. The backs of the tags -- I mean it when I say I'm trying to lighten up myself, be more fanciful! This one is Double BB's favorite layout. Posted by
Oh TThis is stunning. So, beautiful you really did a great job. I love everything about it and the pockets? wow? This is jut out of this world. I love everything about it.
Love, love, love all your pages. If it's okay with you I'd love to CASE the one with the turn page with the laced ribbon. I totally saw an idea for a card as soon as I saw it...simply beautiful! The tags are gorgeous as well!
absolutely beautiful I don´t know what like more because I love everyone, so soft and so melancolic. Great work with the envelopes ,it´s another way to " play" with imagination.
April 2, 2008 at 5:19 AM

type: global

value: Blogger is an American online content management system (CMS) which enables multi-user blogs with time-stamped entries. Pyra Labs developed it before being acquired by Google in 2003.


text:
Book HI Chicago, Chicago, Illinois - Hotels.comEnglish (United States)Book online or call 800-246-8357This call is free. 24 hours a day; 7 days a week.HotelsHotel DealsPackages & FlightsGroupsGift CardsHelpWebsite feedbackSign in & AccountSign inCreate an accountYour bookingsReview a hotelHotels.com® RewardsYour bookingsJump toOverviewRoom choicesMapReviewsHotel informationLowest price: $26Check availability HotelsUnited States of America HotelsIllinois HotelsChicago HotelsHI ChicagoAll hotels in Chicago, Illinois, United States of America1 room, 2 adultsDestination, hotel, landmark or addressCheck inCheck out Rooms123456789+Room 1:Adults12345678Aged 18+Children01230-17Age at check in:Child 1:-?-<11234567891011121314151617SearchYou can collect Hotels.com® Rewards nights hereHI Chicago, Chicago1.5 stars1.5 stars24 East Congress Parkway, Chicago, IL, 60605, United States of AmericaLowest price$30$26Price GuaranteePrice Guarantee on hotel bookings. 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Free WiFi in roomsTransportationParkingDiscounted offsite parkingSee mapOther informationSmoke-free propertyAlso known asHostelling International Chicago Chicago HostelHostelling International Chicago Summer Hotel ChicagoHostelling International Chicago HostelHostelling International ChicagoHostel HI ChicagoPoliciesThis hostel's policy is to accept bookings only from non-local guests. Guests whose residence is within 45 miles of the property will not be allowed to check in. Guests can contact the property for additional information using the number on the reservation confirmation received after booking.
Only registered guests are allowed in the guestrooms. The name on the credit card used at check-in to pay for incidentals must be the primary name on the guestroom reservation. Mandatory feesYou’ll be asked to pay the following charges at check in or check out:Guests who do not present a Hostelling International membership card will be charged $3 at check-in, payable to the hostel.Club Card: USD 3 per person, per nightOptional extrasHigh-speed Internet access (wired) is available in public areas for an extra chargeNearby One Star Chicago hotelsChinatown Hotel Chicago1.5 starsFair2.3 / 5From $51Holiday Jones1.5 starsExcellent4.2 / 5From $99Chicago Getaway Hostel1.5 starsExcellent4.1 / 5From $16Urban Holiday Lofts1.5 starsGood3.7 / 5From $48See more One Star Chicago hotelsIn the hotelFood and drinkFree continental breakfast dailyCoffee shop/café Things to doBicycle rentals nearbyHiking/biking trails nearbyArcade/game roomBilliards or pool tableWorking awayMeeting rooms 2Conference space size (feet) - 600Conference space size (meters) - 56Computer stationServices24-hour front deskConcierge servicesTours/ticket assistanceLaundry facilitiesLuggage storageMultilingual staffFacilitiesNumber of buildings/towers - 1Year Built 1886Elevator/liftATM/bankingSafe-deposit box at front deskLibraryTelevision in common areasAccessibilityAccessible bathroomIn-room accessibilityRoll-in showerIn the roomHome comfortsAir conditioningIron/ironing boardFreshen upShared bathroomShower onlyHair dryerStay connectedFree WiFiMoreDaily housekeepingTop Chicago hotelsThe Sono Chicago3.5 starsOutstanding5.0 / 5From $119Chicago Premier Suites4 starsOutstanding5.0 / 5Furnished Suites in South Loop Chicago3.5 starsOutstanding5.0 / 5From $106$85The Robey3.5 starsOutstanding5.0 / 5Recently reviewed hotels in ChicagoHotel Lincoln4 starsExcellent4.3 / 5From $99"I stayed for 6 nights. During this time everything was excellent. The room was awesome, spacious and clean."Jan 18, 2017Harvey House Bed & Breakfast4 starsOutstanding4.8 / 5"Harvey House provided warm and inviting accommodation as I arrived on a chilly night in January to attend a nearby class for two days. The breakfast Beth provided was delicious…"Jan 17, 2017Courtyard by Marriott Chicago Downtown River North3.5 starsExcellent4.3 / 5From $119"Staff was very nice, lobby was clean. Room was spacious and clean. Bed was comfortable and bathroom was clean."Jan 17, 2017Club Quarters Hotel, Wacker at Michigan4 starsExcellent4.1 / 5From $94$80"Perfect location for me @ Chicago business and leisure on the riverfront and Michigan Avenue amenities. "Jan 17, 2017Nearby hotelsTravelodge Hotel Downtown2 starsGood3.3 / 5From $99$84Congress Plaza Hotel3 starsGood3.3 / 5From $95$86Chicago Old Colony3.5 starsGood3.3 / 5From $75Central 3BR in the Chicago Loop by Sonder4 starsNo guest ratings yetAlso popular with travelers from the USASee all hotels in ChicagoList viewMap viewSee all hotels in ChicagoHI Chicago, Chicago’s small printAlso known asHostelling International Chicago Chicago HostelHostelling International Chicago Summer Hotel ChicagoHostelling International Chicago HostelHostelling International ChicagoHostel HI ChicagoPoliciesThis hostel's policy is to accept bookings only from non-local guests. Guests whose residence is within 45 miles of the property will not be allowed to check in. Guests can contact the property for additional information using the number on the reservation confirmation received after booking.
Only registered guests are allowed in the guestrooms. The name on the credit card used at check-in to pay for incidentals must be the primary name on the guestroom reservation. Mandatory feesYou’ll be asked to pay the following charges at check in or check out:Guests who do not present a Hostelling International membership card will be charged $3 at check-in, payable to the hostel.Club Card: USD 3 per person, per nightOptional extrasHigh-speed Internet access (wired) is available in public areas for an extra chargeWe have included all charges provided to us by this hotel. However, charges can vary, for example, based on length of stay or the room you book.Recent reviewsExcellent 4.3 /5 from 533 reviewsHI Chicago, ChicagoOutstanding5.0 / 5Super!I enjoyed my stay here. One of the best hostels I have been.Jan 24, 2017Genuine Hotels.com guest reviewJenni, us2 night tripHI Chicago, ChicagoGood3.0 / 5A Large and impersonal dormitoryFelt very college dorm-esque. Big long hallways, minimal decorations, not a lot of personality. A decent sized common areas with a ping pong table and two pool tables. Overall an average stay, nothing particularly outstanding. There are better options in Chicago as far as hostels go. Try the Freehand instead.Jan 4, 2017Genuine Hotels.com guest reviewMatthew, us4 night business tripHI Chicago, ChicagoFair2.0 / 5There was hair everywhere. The bathroom was filthy.Dec 30, 2016Genuine Hotels.com guest reviewA Traveler, us3 night trip with friendsHI Chicago, ChicagoOutstanding5.0 / 5Convenient accommodation-Chicago MarathonCentrally located, HI Chicago provides for convenient and affordable accommodation for those running the Chicago Marathon. (The start and finish lines are within walking distance from HI)
The hostel staff was very friendly and had arranged for a special pre-marathon pasta dinner, as well as making breakfast available to marathon participants starting 5 AM on race day.
I stayed here in 2016 and am booked to be here for the 2017 edition of the race as well!Oct 8, 2016Genuine Hotels.com guest reviewHarish, us1 night tripHI Chicago, ChicagoOutstanding5.0 / 5One of the best hostels I've ever beenAbsolutely perfect. The hostel is huge and the people at the attention disk are too kindDec 8, 2016Genuine Hotels.com guest reviewA Traveler, us1 night tripSee all reviewsHI Chicago, ChicagoIs the description of this hotel not correct? Tell usChoose one of the following to unlock Secret Prices and pay less on select hotels.EmailSign up for our latest offersHotels.com appDownload the app and book on the goHotels.com® RewardsJoin now! Collect 10 nights, get 1 free*Top DestinationsHotels in New York, New YorkHotels in Las Vegas, NevadaHotels in Chicago, IllinoisHotels in San Francisco, CaliforniaHotels in Washington, District of ColumbiaHotels in Boston, MassachusettsHotels in San Diego, CaliforniaHotels in Orlando, FloridaHotels in London, United KingdomHotels in New Orleans, LouisianaHotels in Los Angeles, CaliforniaHotels in San Antonio, TexasHotels in Paris, FranceHotels in Seattle, WashingtonHotels in Atlanta, GeorgiaHotels in Philadelphia, PennsylvaniaHotels in Austin, TexasHotels in Atlantic City, New JerseyHotels in Rome, ItalyHotels in Myrtle Beach, South CarolinaHotels in Dallas, TexasHotels in Houston, TexasHotels in Denver, ColoradoHotels in Miami, FloridaSupport & FAQsYour bookingsFAQsContact usWebsite feedbackReview a hotelFor Suppliers, Affiliates and the MediaAdd HotelAffiliate with usPrivate LabelPromote with UsTravel AgentsPress OfficeUser Terms and Privacy PolicyTerms & ConditionsPrivacy PolicyCookie PolicyAbout our adsAbout UsJobsTravel GuidesTop 50 CitiesAll countriesAll hotelsSite index© 2017 Hotels.com, L.P. All rights reserved.Hotels.com and the Hotels.com logo are trademarks or registered trademarks of Hotels.com, LP in the U.S. and/or other countries. All other trademarks are property of their respective owners.* Learn more about Hotels.com® RewardsABOUT SSL CERTIFICATES

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Michael Blatz Chairman of The Supervisory Board, Member of Personnel Committee and Member of Nominating Committee, Loewe AGAgeTotal Calculated CompensationThis person is connected to 1 Board Members in 1 different organizations across 0 different industries.See Board Relationships----
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February 2016 – Ask Aladdin
February 4, 2016 by Ask Aladdin
Some world famous tourist places in Egypt.
If you are planning to go for a vacation and you are looking for a good option to travel, then Egypt is one of the most excellent options. This country is the home to some of the ancient Pharaohs, along with the River Nile, Egypt has a number of travel destinations which will leave you enthralled and amused by their beauty. A person who has a special space for history in his heart, the ancient civilization of Egypt is the best thing to explore. This is the reason why tourists from all over the world come to explore this magnificent country.
There are some of the top Egypt Tourist Attractions which will make you fall in love with the ancient civilization of Egypt. There are some of the monuments which still stand strong and there are also the ruins of some.
Let us have a look at the Egypt Tourist Attractions which you cannot afford to miss while visiting this beautiful and grandeur country:
Abu Simbel: This is one of the most famous places in Egypt. Abu Simbel is a great temple where you will find statues of guards standing outside and the interiors of the temple are full of wall paintings. This place is also well-known for an incredible feat, where the temple got moved from its original place.
Egyptian Museum: This is one of the most popular and must visit place in Egypt. As Egypt is all about history, this museum has a lot to offer to you which will make you know more and more about this magnificent country. In the Egypt museum, the royal mummies are also exhibited there.
Aswan: If you want to spend some quality time with your partner or your family, then Aswan is the place for you. Aswan is also known as one of the most tranquil places in Egypt. There are many numbers of historic sites at this location and you will also find many temples near to this location, the biggest highlight of Aswan is sitting and watching the river. If you want some tranquil atmosphere then this place is a must go for you.
Pyramids of Gaza: We all know that Egypt is the home for Pyramids. One of the Seven Wonders of the World is Pyramids of Gaza, so you cannot actually afford to miss this place while visiting Egypt. You will find tourist from all over the world who just come to Egypt to visit Pyramids of Gaza.
So, what are you thinking about? The next vacation is around the corner and Egypt is waiting for you to make dwell in the most ancient history of the world. You can get in touch with Ask Aladdin as they are one of the most popular and reliable tour operator for Egypt. The best part about the ask Aladdin is there prices and the different tour packages they offer. Contact them today to get best and exciting offers.
Choose Your Egypt Nile River Cruise on 2019

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Team Leeds Magic: Tournament Report - Win a Mox Pearl in Fanboy3 html body .region-inner { min-width: 0; max-width: 100%; width: auto; } a:link { text-decoration:none; color: #cfe2f3; } a:visited { text-decoration:none; color: #cfe2f3; } a:hover { text-decoration:underline; color: #cfe2f3; } .body-fauxcolumn-outer .fauxcolumn-inner { background: transparent none repeat scroll top left; _background-image: none; } .body-fauxcolumn-outer .cap-top { position: absolute; z-index: 1; height: 400px; width: 100%; background: #111111 none repeat scroll top left; } .body-fauxcolumn-outer .cap-top .cap-left { width: 100%; background: transparent none repeat-x scroll top left; _background-image: none; } .content-outer { -moz-box-shadow: 0 0 40px rgba(0, 0, 0, .15); -webkit-box-shadow: 0 0 5px rgba(0, 0, 0, .15); -goog-ms-box-shadow: 0 0 10px #333333; box-shadow: 0 0 40px rgba(0, 0, 0, .15); margin-bottom: 1px; } .content-inner { padding: 10px 10px; } .content-inner { background-color: #000000; } /* Header ----------------------------------------------- */ .header-outer { background: #111111 none repeat-x scroll 0 -400px; 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Hi all! I come fresh from another foray into Lejacy Magic at the always-fun Fanboy3 in Manchester. 37 players came with their favourite deck sleeved up, resulting in 6 rounds before top 8. The prizes? A very good looking Mox Pearl to the winner, with store credit to the top 4 and 5 boosters for each of the losing quarter finalists. Legacy has undergone a lot of changes recently with the StarCityGames Open Series forcing people to make better decks than the classic Team America Control, Goblins and so on. The buzz was that the format would be Zoo, Merfolk, Goblins and New Horizons this weekend, so I picked a deck which would be good against those 4 and forsaked my usual protection against silly combo decks, since with a 9 round tournament I might only have to face it once or twice and I can afford one loss in the swiss easily. I brewed up the following, borrowing many cards from the always-excellent Rik Powell:
Which is similar to my Madrid deck but has subbed the Mindbreak Traps for the mirror tech of Baneslayers :D. The Karakas was a last-minute addition I borrowed from Matt Light in lieu of the presence of many Emrakuls in the room.
Round 1 vs Phil Smith with Bridge-Naught
His deck is one of the new Emrakul ones. It is based on the old Stifle-Naught decks which used Stifle to cheat Phyrexian Dreadnaught into play. Using Mosswort Bridge they can hide away a good card and with the Dreadnaught in play and trigger on the stack can activate the Bridge to cast something good. They also have Show and Tell to directly cheat stuff into play. I have only a few answers for this sort of deck but I have a lot of aggression so it might not be too terrible. Game 1 he chose to not counter my Knight of the Reliquary as he wasn't sure if Zoo was playing Karakas still (most don't) so when he Show and Telled Emrakul into play I was able to tutor up my land to bounce the Spaghetti Monster. Game 2 he countered my Knight but I'd already drawn the Karakas. In any event his countering my Knight put enough card types into graveyards to power up my Goyfs for a lethal attack so it was Catch 22 really.
Round 2 vs Tomas Sukaitis with Bant
Tom brought Bant but had given up on Counterbalance-Top for a straightforward aggro-control version of the deck. My Baneslayers came in for this creature mirror and it was mostly back and forth with life totals only he has War Monks. Game 1 I played around Stifle (quite common in current Bant Decks) only to get blown out by Wasteland (far less common). Game 3 had an unusual situation: on 4 life with a Jitte versus War Monk and Qasali Pridemage I rip a Baneslayer Angel. He rips Noble Hierarch and the Exalted means I actually have to chump block :o.
Round 3 vs Thomas Robinson with Goblins
I may be mixing up rounds 3 and 4 here as I don't have any notes to go on but I definitely played these 2 matches. Goblins is a good matchup for Zoo as they lean fairly heavily on turn 1 Goblin Lackey (how lackey!) or Aether Vial, but Lackey is a 1/1 versus my 21 1 mana answers and Vial doesn't affect the board very quickly. I draw a bit of gash while he nuts off a Ringleader but his deck is set up to do that so fair enough. Game 2 I establish control of the board but only draw 3 creatures the entire game, 2 Guides and a Nacatl - Jitte helps a lot with this. Game 3 he didn't have a very explosive start and played out 2 Vials in the first two turns before Edicting me when I had a Nacatl and a Pridemage in play. I sacced the Nacatl as I was fairly sure his hand had something like Goblin Warchief, Ringleader, Siege-Gang so I kept the Pridemage to kill his 2 counter vial in his next upkeep with the trigger on the stack. This turned out to Time Walk him just enough for my guys and burn to get in before he could develope a board presence.
Round 4 vs Jason Christie with Enchantress
Another good matchup for Zoo. I was lucky enough to know what my opponents were playing every round today - having good friends at tournaments has a lot of benefits and you should always help out the people you know if you can because they often return the favour. I kept my hand despite being slow because it had a Qasali Pridemage in. I got some damage in and kept him off the right cards until he raw-dogged a Moat when he was going to tutor for it with the Sterling Grove he had in play. I'd rather he'd tutored for it as I'd been sandbagging my Pridemage in hand. I ran it out to kill the Grove as he had a card which prevented players from casting spells and activating abilities in opponents turns and had to hope to draw another Pridemage to win. Luckily I did and was able to attack in in time. My hand for game 2 had no 1 drop or 2 drop again but 2 Krosan Grips so I kept. My draws were Wild Nacatl, Gaddock Teeg, Qasali Pridemage, Qasali Pridemage; proving that it is, in fact, very nice being me.
Round 5 vs Alex Shoemark with Iggy-Pop
This is not a good matchup for me as I have to have one of my Teegs or Canonists to win, but at least if he's leaning on Ill-Gotten Gains rather than Ad Nauseam my sideboard graveyard hate has room to do something. Game 1 I mull a hand without Teeg into another one without Teeg but some aggression. I cast 2 spells before I lose (Goblin Guide and Lightning Helix) and start sideboarding. Games 2 and 3 I get a hate bear and some aggression plus a Tormod's Crypt so I am able to put him under pressure. Luckily for me his draws are terrible and I sneak the victory. Game 3 his keep was because he had Lotus Petal, Crystal Vein and Dark Confidant, which seems fair but he failed to draw any more lands. I'm not convinced Bob comes in against Zoo because he just gives me extra Lightning Bolts on my opponent but perhaps his testing revealed that he wanted more cards more than he wanted his life total - I think Bob is more for mirror matches. Ah well, no complaints as I can ID into the top 8 with Matt Light in round 6.
4-1-1, 4th after the Swiss
Quarterfinals vs Matt Light with Hypergenesis
Matt is playing another one of those Emrakul decks, only using Hypergenesis alongside Show and Tell to put Kul and the gang into play. I literally have only my main deck Karakas and my sideboard Ethersworn Canonist to interact and sure enough Matt kills me with gigantic monsters. 1 at a time, a la Reanimator I can deal with but 5 or 6 at once is too much. He kept a dog of a hand game 2, with 1 land, a cascade spell and 5 monsters on the draw, but he only needs 2 mana sources to plain win the game, against a deck with Goblin Guide, up a game and in a match where he is the heavy favourite so I think it's defensible.
I pack wars my 5 M11 boosters and manage to get a Fauna Shaman and am fairly happy with top 8 when I hadn't played Legacy in a while and couldn't list peoples' decklists by heart any more. I've got a new version of Zoo brewed up with a bit more metagaming involved but I'm saving it for Rik or myself to run in the Legacy event at Nats in a few weeks time - probably not me, I'll be busy being National Champion obv.
Posted by Wagz at 10:48
Labels: Legacy, Rob Wagner, Tournament Report
PhanTom 2 August 2010 at 11:59
Was fun playing you :)
The decklist needs editing, is rather hard to read, would suggest removing the spaces between lines. also SB is not marked as such.
Wagz 2 August 2010 at 12:04
Ah yes I knew I'd forgotten something, thanks Tom. It used to not write the cards on different lines without tags but that seems to be fixed now so I won't use them in future, good catch.
yankeedave 2 August 2010 at 12:33
Good report mate! I will interested to see what you have brewing for Nats! I wonder if its similar to Caleb Durward's Vengival list from GP Columbus...


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value: Freelance (sometimes spelled free-lance or free lance), freelancer, or freelance worker, are terms commonly used for a person who is self-employed and not necessarily committed to a particular employer long-term. Freelance workers are sometimes represented by a company or a temporary agency that resells freelance labor to clients; others work independently or use professional associations or websites to get work.


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Deez Nailz: ♥ Billie 'Classy Booze' Collection ♥
......Just kidding, this is actually called "Classic Red High Gloss" Collection. These are all very pretty reds, named after Classy Booze.... 2 coats on the swatches, no top Coat. It was very difficult to get a good photo of the swatch: the last three polish's look very similar swatched, but they are all slightly different from each other. Did I need all 4 of these polish's? - Heck yes, These dry sooooo fast, and they are .99 cents in my town, and they are very, very pretty. I saw these in spring-time and passed on them because I had ( and still have ) like, 40 polish's to wear/swatch, but this time I had to buy them.
if you happen to live in Canada and have access to a Dollar Giant ( in B.C., Alberta, Ontario.... I think that's it...... ) I am curious if Billie had a yellow nail polish, there is an itemized list on their website that has 'summer' names, but no photos yet......hmmmmm.
Posted by Deez Nailz - Canadas most fabulous hand model at 10:30 AM
Labels: Brand - Billie, Colors - Red, SCORE - .99 cent store
Wow! I couldn't even begin to pick a favorite. They're all such pretty shades of red. That brand seems fantastic...I never get tired of looking at your pictures of them :)
I'm with Mary, I get all excited about your Billie pictures. These are no exception, I can see that they are all really really pretty reds! For 99 cents, you can't beat it, might as well have them all!
Those Billie polishes are amazing. Such beautiful colors. Outside they all looks the same. They are all quite different. Can't wait to see them on your nails.


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Melissa Pearce | Parasite Eve Wiki | Fandom
Characters, Parasite Eve characters, Deceased characters,
32 (born 1965)
Green (as Eve)
“ I don't care if I die, I just want to get through this show.
—Melissa in her diary
Melissa Pearce was an actress and opera singer who became the second Mitochondria Eve, possessed by an ancient biological evil.
On December 23, 1977, when she was 12 years old, Melissa had a kidney transplant due to kidney failure. This kidney belonged to Maya Brea, and Maya's mitochondria would unknowingly lay dormant inside Melissa for 20 years. This operation was at St. Francis Hospital in New York City and it was successful. After six months of hospitalization, Melissa was released, although she still needed medication from the hospital.
Years later, she became ill, and believing that it was her kidney being rejected, she began to take more immunosuppressants drugs than usual. Melissa blamed the medication for her illness, but at the same time, it was weakening her immune system from her sentient mitochondria. The drugs served to act as a catalyst for her neo-mitochondria and Mitochondria Eve.
Melissa visited the American Museum of Natural History at night on several occasions, presumably to meet with Hans Klamp, and with Eve as the dominant persona. Eve and Klamp collaborated and planned to eradicate humanity in order to begin a new world with a higher species of ultimate beings. Although the game does not mention it, it is also possible that the two had a relationship and Eve may have seduced Klamp.
In 1997, Melissa wanted to perform as the lead role in an opera at Carnegie Hall, but was instead double cast (two actors share the role and take turns playing it each day) with another very talented actress named Suzanne. During this time she began to overdose on immunosuppressants. Soon after, Suzanne died in an apartment fire, giving Melissa the sole lead role. She felt much guilt and stress over this, which increased her drug usage. Despite feeling sick, Melissa avoided going to a doctor and mentioning her illness to others in fear that it would cause her to give up her lead role.
Melissa was going to have a concert at Central Park on Christmas Day, 1997.
Melissa portraying Eva.
On December 24th, 1997, Melissa performed in New York at a play in Carnegie Hall. Aya Brea, who also has her twin sister's mitochondria, attended Melissa's performance. Melissa played a "witch" called Eva and Eva's lover, Edward, begged his father the king for permission to marry Eva. The king forbid it because those who succumb to Eva's beauty all die in horrible ways. Edward claims his father doesn't understand and that Eva is the one who suffered after all the deaths. The king claimed Eva is evil and asked his guard to grab her and burn her at the stake. Edward said if Eva was to be sentenced to death, he would prefer to die too. At this moment, Eva whispered Edward's name, stepped forward, and began an opera performance.
Eve, possessing Melissa, killing hundreds by immolation on Carnegie Hall's stage.
While performing, Melissa's mitochondria strongly respond to Aya's, causing Melissa's mutated mitochondria to fully take over her and awaken as Eve. Her mitochondria communicates with the audience's and orders them to produce excessive levels of ATP, causing spontaneous human combustion to all except Aya and her unnamed date. Aya gives chase as Melissa runs off, or more accurately, levitates back stage.
Behind a locked door at the end of the hall in the basement, Melissa is last seen playing the titular "Main Theme" on the grand piano before smashing it and evolving into a more advanced form, Mitochondria Eve.
Melissa Pearce's name is mentioned as one of the answers for Fred Bowman's computer.
"I took all that medication to get here. I HAVE to get the lead part! I'll even sell my soul to the devil if I have to."
"I think I'm overdoing it. My body is getting hotter than ever for some reason. I'd better take more medicine."
"I collapsed today. I lost consciousness after my body got hot. I don't care if I die. I just want to get through this show."
"Suzanne was burned in an apartment fire. Is it because I wanted the part so bad? God, forgive me..."
"It looks like I'll be the lead. I'd better take more medication and work it."
Eve's final form resembling Melissa.
The BradyGames guide claims she is 5'11" (180cm) and 114 lbs (52kg), which would be disturbingly underweight for her tall height. This is an unnatural height and weight, meaning that it's possible Melissa was starving herself, possibly to appear thin for an actress role. One could also theorize that Eve made Melissa lose her appetite in order to deprive Melissa of energy and weaken Melissa's body, making Melissa easier to be controlled and overpowered so that Eve could become dominant.
Ironically, after Eve's multiple freakish forms and evolution, at the end of the game, her final form returns to one that resembles Melissa more.
Melissa's red dress resembles to Ifalna's, Aeris Gainsborough's biologically mother from Final Fantasy VII
Melissa and Aya.
Melissa on the Statue of Liberty.
Eve awakens.
Melissa singing.
Melissa staring at Aya.
Melissa/Eve setting actors on fire.
Melissa/Eve singing as the audience burns.
Eve on the stage.
Melissa on the burned stage.
Melissa/Eve floating away.
Melissa transforming into Eve.
Eve and Aya in Carnegie Hall.
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