Abstract:
There is disclosed a system and method for automatically performing semantic categorization. In one embodiment at least one text description pertaining to a category set is accepted along with words that are anticipated to be uttered by a user pertaining to that category set; lexical chaining confidence score is attached to each pair matched between the anticipated words and the accepted text description. These confidence scores are used subsequently by a categorization circuit that accepts a text phrase utterance from an input source along with a category set pertaining to the accepted utterance. The categorization circuit, in one embodiment, creates word pairs matched between the accepted text phrase utterance and the accepted category set. From these word scores, the category pertaining to the utterance is determined based, at least in part, on the assigned lexical chaining confidence scores as previously determined.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     The present application is related to commonly assigned U.S. patent application Ser. No. 11/522,107, filed Sep. 14, 2006, entitled “AUTOMATIC GENERATION OF STATISTICAL LANGUAGE MODELS FOR INTERACTIVE VOICE RESPONSE APPLICATIONS”, the disclosure of which is hereby incorporated herein by reference. 
     TECHNICAL FIELD 
     This invention relates to semantic categorization and more to a system and method for categorizing text phrases or sentences into specific pre-defined categories. 
     BACKGROUND OF THE INVENTION 
     A semantic categorizer accepts text phrases or sentences as input, analyzes them and places each input text in a specific category. In some cases, a specific input text phrase can be placed in one or more categories, with confidence scores for each placement. Semantic categorization is a key component in most dialog systems. For example, Interactive Voice Response (IVR) systems must interpret a user&#39;s spoken response to a prompt in order to then complete an action based on the response. 
     Currently, in fixed-grammar directed-dialog systems, semantic categorization is performed using a set of manually defined rules. A dialog developer pre-defines those utterances that the system should be capable of “understanding”. These pre-defined utterances are called “grammars”. Each predefined utterance is assigned to a semantic category, and that semantic category is indicated by including a semantic tag with the grammar definition. Thus semantic categorization is labor intensive and requires significant manual involvement to develop grammars and define semantic tags for each new application or prompt. Using existing approaches, dialogs are fairly restrictive, since they must always remain within the scope of the pre-defined responses. 
     In open ended (non-directed) applications, that use prompts such as, for example, of the type, “How may I help you?”, users speak utterances intended to select one of a list of the tasks that are available in the application. Often these task choices are not pre-identified (directed) to the speaker so a user can say almost anything in response to the prompt. Automatic speech recognizers (ASRs) use Statistical Language Models (SLM) to transcribe the user&#39;s utterance into a text message. This transcribed text is then passed to a categorization engine to extract the semantic choice that the user is requesting. The above-identified patent application is directed to the automatic generation of SLMs, for example, for use with an ASR to generate text transcriptions of a user&#39;s utterance. 
     After a text transcription is available, the next task is to make that text understood by a machine. For example, if the user says, “I want my bank balance”, the ASR in the IVR would use the SLM created by the above-identified patent application to generate text that says, “I want my bank balance”. The text of the utterance then needs to be understood by the machine and mapped to a semantic category “bank_balance”. 
     By restricting the scope of a dialog to a specific domain such as “banking”, the accuracy and speed of generating the text transcription of spoken utterances is greatly improved. For this reason, many IVR applications assume that all user utterances will fall within the domain of that application. Utterances that have nothing to do with the application will not be transcribed accurately and will be assigned a low confidence score. For example, if a user calls a bank and says, “I want flight information to California,” an SLM system will transcribe that to some nonsensical sentence with a very low confidence level, because that question is an improper domain for a banking application and the SLM could not handle words out of its domain. The low confidence score level indicates that the utterance is probably not transcribed correctly, and further clarification is required. Therefore, normally, the proper domain must be known by the user or selected as a starting point. In a typical application, the overall domain is known, since if the user is calling, for example, a bank, it would be a banking domain. 
     Within a specific domain there are a number of category sets or available tasks that can be performed by the application. There are many ways a user can invoke a task. A task can be requested by a command: “Tell me how much I have in my checking account” or a question, “How much money do I have in my account?” There are typically a large number of utterances that a user can use to invoke any specific task in an application. 
     The job of a semantic categorizer is to discover the specific task that a user is requesting, no matter how it is requested. This process is typically done in two steps, with the first step transcribing the user&#39;s utterance into text. An improved method for this transcription process is described in the above-identified application. 
     Once the user&#39;s utterance is successfully transcribed, the text transcription must be analyzed to determine the user&#39;s intentions. One aspect of this process is discussed in a paper published in 2005 in the AAAI SLU workshop entitled “ Higher Level Phonetic and Linguistic Knowledge to Improve ASR Accuracy and its Relevance in Interactive Voice Response System ,” which is incorporated by reference herein. 
     BRIEF SUMMARY OF THE INVENTION 
     There is disclosed a system and method for automatically performing semantic categorization. In one embodiment at least one text description pertaining to each category in a category set is accepted; lexical chaining confidence score is attached to each word in the category text description being semantically paired with another word which is at most “n” semantic relations away in WordNet. For example, if “bank” is a word in the category description for the category “Account Balance” and “n” is equal to 3, we extract all the words which are at most 3 semantic relations away from the word “bank” in WordNet and associate a lexical chaining confidence score between “bank” and each of these extracted words. This confidence scores database is used subsequently by a categorization algorithm that accepts a user text utterance from an input source along with a category set and their corresponding text descriptions pertaining to the IVR dialog state. The categorization algorithm, in one embodiment, extracts word pairs matched between the input user text utterance and the IVR dialog state category set descriptions using the lexical chain confidence scores database. From these word pairs, the category pertaining to the user utterance is determined based, at least in part, using the collected lexical chaining confidence scores as previously determined. 
     The foregoing has outlined rather broadly the features and technical advantages of the present invention in order that the Detailed Description of the Invention that follows may be better understood. Additional features and advantages of the invention will be described hereinafter which form the subject of the claims of the invention. It should be appreciated by those skilled in the art that the conception and specific embodiment disclosed may be readily utilized as a basis for modifying or designing other structures for carrying out the same purposes of the present invention. It should also be realized by those skilled in the art that such equivalent constructions do not depart from the spirit and scope of the invention as set forth in the appended claims. The novel features which are believed to be characteristic of the invention, both as to its organization and method of operation, together with farther objects and advantages will be better understood from the following description when considered in connection with the accompanying figures. It is to be expressly understood, however, that each of the figures is provided for the purpose of illustration and description only and is not intended as a definition of the limits of the present invention. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       For a more complete understanding of the present invention, reference is now made to the following descriptions taken in conjunction with the accompanying drawing, in which: 
         FIG. 1  shows one embodiment of an architecture in accordance with the present invention; 
         FIG. 2  shows one embodiment of a categorizer for use in the embodiment of  FIG. 1 ; and 
         FIGS. 3 and 4  illustrate one embodiment of a process for performing the method of the invention. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
       FIG. 1  shows one embodiment  10  of an architecture for performing the concepts of the invention. Note that the semantic categorizer shown in  FIG. 1  is divided into two sets of processes. One set is the off-line processes, which includes everything to the left of database  14 , and the other set which includes the on-line processes, everything to the right of database  14 . The off-line processes are applied before the application is started, and may run for a lengthy period. The goal of the off-line processes is to generate data (word pairs and lexical chain confidence scores) to be stored in database  14  in a format that will allow fast categorization during a real-time dialog process. Once the database has been created, the on-line processes must work in near-real-time on user utterances, to prevent delays in the user&#39;s dialog with the system. Generally, the ASR and semantic categorizer must yield an answer to what task the user has requested, within a few tenths of a second, to make the dialog flow smoothly. 
     Shown in FIG. I is category sets  11  which consists of a plurality ( 11   a - 11   n ) of category sets, each set containing one or more text descriptions for each category or text within a category set. For example, let&#39;s say we have a prompt, “Do you want your account balance, cleared checks or transfer money?” The category set for this particular prompt contains three categories, a category for “account balance”, one for “cleared checks”, and one for “transfer money”. Each category has at least one text description of that category, which the semantic categorizer will use as the information to categorize the various possible answer utterances. In order to create each category description, the speech application designer formulates a text description of each category in the set into a file in sentence format (Process  301 ,  FIG. 3 ). Thus for each dialog state/prompt in the IVR call-flow, that speech application designer will create a category set and at least one description for each semantic category in the category set. All these category sets defined by the speech application designer for a particular IVR application are incorporated into category sets  11  ( 11   a - 11   n ), are then presented to configurator  12  along with a lexical data source like WordNet. For example, a small banking IVR might contain three different dialog states/prompts. State  1  “Check Account Balance, Check Cleared Checks, Transfer Money”; State  2  “Savings Account, Checking Account”; and State  3  “Electronic Transfer, Money Order”. Each of these states/prompts has a well-defined semantic category set which is created by the speech application designer. Once the designer defines the semantic category set for each dialog state/prompt, the designer writes at least one description for each of the semantic categories in each of the IVR category sets. For a category, such as “Transfer Money”, the designer would write a description of the money transfer process, defining the task related to moving money from one place to another. For “Check Cleared Checks”, the designer might write that the user can check the number of cleared checks in the past week. Other sentences would also be written pertaining to a description of this activity (process  301 ,  FIG. 3 ). The category sets from the IVR dialog prompt/states  1 ,  2 , and  3  form category sets  11  ( 11   a ,  11   b , and  11   c ). 
     Configurator  12  accepts two inputs, one from the designer and another from a lexical database, such as, for example, WordNet 13 (process  302 ,  FIG. 3 ). WordNet, such as described by C. Fellbaum in the MIT Press 1998, of which is incorporated herein by reference, is a well known database containing English words along with information about each word such as the synonyms for each one of the words (concepts) and the relations of those concepts to other concepts in the database. A description for each concept is also included and this information attached to each concept is called a “gloss”. WordNet contains open class words like nouns, verbs, adverbs and adjectives grouped into synonym sets. Each synonym set or WordNet synset represents a particular lexical concept WordNet also defines various relationships that exist between lexical concepts using WordNet semantic relations. For example, the lexical database will yield for “cellular phone”, as a concept, the different synonyms for cellular phone, such as cell, cellphone, mobile phone, and cellular telephone. It also has a gloss which states that the “cellular phone” concept is “a hand-held mobile radiotelephone for use in an area divided into small sections, each with its own short-range transmitter/receiver”. WordNet also has a hierarchy. For example, it knows that “cellular phone” is related to the concept “radiotelephone radiophone, wireless telephone—a telephone that communicates by radio waves rather than along cables” so “radiotelephone, radiophone, wireless telephone” concept is a parent of “cellular phone”. Going one more level up, we can see that “cellular phone” is related to the concept “telephone, phone, telephone set—electronic equipment that converts sound into electrical signals that can be transmitted over distances and then converts received signals back into sounds”, so “telephone, phone, telephone set” concept is the grand-parent of “cellular phone”. In this manner, all of the words in English are connected together by their semantic relations. Configurator  12  takes each category set from  11   a  through  11   n  and for each word in the designer written description of each category, it extracts a set of words/concepts that are at most “n” semantic relations away from that particular category description word in WordNet. A confidence score is also associated with each of these extracted word pairs. D. Moldovan and A. Novishi in their paper entitled, “ Lexical Chains for Question Answering” , published in Proceeding of Coling, 2002 (hereinafter “Moldovan”), of which is incorporated herein by reference, present a methodology for finding topically related words by increasing the connectivity between WordNet synsets (synonym set for each concept present in WordNet) using the information from WordNet glosses (definition present in WordNet for each synset). Thus we can determine if pairs of words are closely related by not only looking at the WordNet synsets but also by finding semantic relation paths between the word pair using the WordNet synsets and glosses. Configurator  12  uses such a lexical chain process to find all words which are related to a category description word by less than “n+l” semantic relations. A confidence score is also associated with a related word pair by the lexical chain process based on the distance of the words from each other. This set of word pairs, their corresponding relationship chains, and lexical chain confidence scores is sent to Natural Language Processing (NLP) database  14  (process  304 ,  FIG. 3 ). 
     Database  14  could store the relationship in any know data format. This could be a flat file or it could be an RDBMS in a database management system. The NLP database contains a set of word concept pairs along with the lexical chain weight associated with each concept pair. For example, for the pair of words such as “balance” and “account” the weight might be 50 or 60. The score indicates a high degree of semantic similarity between the words. This is in contrast to a word pair such as “eat” and “shoe” which have a much lower score and hence have a low semantic similarity between them. 
     When it is desired to translate an utterance into a particular semantic category or task, categorizer  204  (process  403 ,  FIG. 4 ) accepts as input the text, such as text  15  (process  402 ,  FIG. 4 ) as recognized by ASR  101 , and using the known category set (process  401 ,  FIG. 4 ) (such as category set  11   a ) for the IVR dialog state identifies the matching word pairings (words in the user utterance text against words in each category descriptions from  11   a ). The previously generated (off-line) NLP database  14  is used to find if a word pair is semantically related and also to identify the confidence score for such a match. 
     Database  14  then provides a listing of all the required word pairs (along with the lexical chain scores previously calculated for each such word pair) for the given category sets. Every word in the input user text  15  is paired with every word in the description(s) for a particular semantic category in  11   a , the categorizer  204  then associates a categorization confidence score for such a (user text, semantic category) pair by summing up the lexical chain confidence score associated with every valid word pair (using the NLP database to not only detect the validity of a particular word pair but also to find the corresponding semantic similarity score). The assumption then is that the highest categorization confidence score (total lexical confidence score normalized with the number of valid word pair numbers) for a particular category (given all the words in the transcribed utterance) indicates the proper category for that user utterance. Process  404  checks to see that all word pairs (concepts) have been given a score and when they have, checks if all the categories in category set  11   a  have been assigned a categorization confidence score for that particular user text utterance. Process  405  determines if it has enough separation (based on categorization confidence score for each category in set  11   a ) and if the scoring is high enough, to declare a winner. If not, process  406  returns a “no match” condition. Depending on the dialog system, this could result in the system asking the user for clarification, or various other dialog error-handling processes can be invoked. If a “winner” is determined, process  407  provides that information. 
     For example, assume a particular prompt which asks, “How may I help you?” with the available tasks “checking your account balance”, “checking cleared checks”, or “transferring money”. If we also assume that the user utters, “I want to check my account balance”. The text from the ASR for that particular user utterance would say, “I want to check my account balance”. We need to match this utterance transcription against each one of the available categories. Of course, the best match is the task of getting the account balance. Therefore the category tag coming out of the categorizer would be “account balance”. Now let&#39;s take an example where the utterance of the user does not completely match the category description or the category set. For example, in response to the prompt of this example, the user says, “I want the total in my account”. This utterance does not exactly match with any of the semantic activity descriptions completely but the categorization confidence score would be higher for the tag (or category) of “account balance” than it would be for the category of “money transfer” or “cleared checks”. This is due to the high semantic similarity between user utterance transcription and the category description for the task “account balances” than for the category of “money transfer” or “cleared checks”. Hence the category tag coming out of the categorizer would be “account balance”. 
     Now let&#39;s take an example where the utterance of the user does not match any of the category description or the category set. For example, in response to the prompt of this example, the user says, “I want to cancel my credit card”. This utterance does not match with any of the semantic activity descriptions. This is due to the low semantic similarity between user utterance transcription and all the category descriptions. Hence the category tag coming out of the categorizer would be “no match”. 
       FIG. 2  shows one embodiment  20  of a categorizer without the use of the NLP database. As discussed above, a text version of the user&#39;s utterance is presented to part of speech (PO S) tagger  202 . In some cases, the ASR cannot transcribe the utterance with perfect accuracy, so it will provide a list of 5 or 6 or 10 transcriptions, for each user spoken utterance that are close based upon the SLMs. These are called the N-best utterances. These utterances are provided to part of speech (POS) tagger  202 . POS  202  determines the part of speech for each word in the utterance text(s). For example, POS  202  determines if a word is a preposition or a noun or any other part of speech. This helps determine how well the word pairs are related. For example, when the user says, “bank”, in a banking domain, the word could be noun like as a “financial institution” in “bank account”, or it could mean a verb like “deposit: in “bank my checks”. When “bank” is used in a power catalog domain, it would mean “bank of batteries”. Thus, it is important that the system determine for each word which part of speech and which sense it denotes. This then helps lexical chain  203  find the relationship between a pair of words. The output from POS tagger goes to lexical chain application  203  which uses WordNet to help determine the actual relationship between the word pairs. This then, for example, says that “account” and “balance” are related and gives the score. For example, “account” the noun for an account has word sense No.  1  is related to the noun form of “balance” with word sense No.  2 . These are the scores which are actually put into the NLP database as discussed in  FIG. 1 . 
     Phrase categorizer  204  receives a large number of word paths and their scores. For example when the user says, “I want to check the total in my account,” categorizer  204  receives for each content word (which can be a noun, verb, adjective or adverb) a lexical chain with each word in the description given by the system designer. These descriptions, as discussed above, are present in each category set. Categorizer  204  finds the score between each one of these words and picks the best lexical chain. 
     In one embodiment, the best lexical chain is determined by the maximum confidence associated by the lexical chain program with the word pairs. For example, as between the words {“total” (the utterance), “balance”} and {“total”, “transfer”} the score is highest for the first pair and thus that lexical chain is selected, yielding a tag (or category) N=check balance. This mapping is performed for all pairs to select the right (highest score) semantic. 
     In case the input to the categorizer (process  21  in  FIG. 1 , or process  403  in  FIG. 4 ) is an n-best list of transcriptions (process  15  in  FIGS. 1 and 2 , or process  402  in  FIG. 4 ) for a particular user utterance, a majority voting algorithm is invoked to determine the best semantic category for that user utterance. In some cases the ASR cannot transcribe the utterance with perfect accuracy, so using a list of 5 or 6 or 10 transcriptions for each user spoken utterance that are close based upon the ASR language and acoustic models, can give a better categorization result than using just the first best ASR transcription for categorization. The process of categorization, described above for matching a single user input text to a particular category from a set of predefined categories, is repeated for each one of the transcriptions in the n-best list provided by the ASR. This results in a pre-defined category or “no-match” category being assigned to each one of the ASR n-best list transcriptions for a particular user utterance. We then pick the category assigned to the majority of the ASR n-best list transcriptions as the semantic category for that particular user utterance. 
     Note that the processes discussed herein could run, for example, on processor  102 ,  FIG. 1 , contained in IVR  101  or separate processors (not shown) can be used for each process. For example, categorizer  21  and configurator  12  could use the same processor or they could use separate processors as desired. One configuration could be as shown in the above-identified patent application where the configuration process (process  30 ,  FIG. 3 ) could be combined with the sum generation process. 
     The paper entitled “Higher Level Phonetic and Linguistic Knowledge to Improve ASR Accuracy and its Relevance in Interactive Voice Response Systems” (hereinafter “AutoCFGTuning”) published in 2005 at the AAA1 SLU workshop, which is incorporated by reference herein, described a semantic categorizer for the purpose of automatically tuning IVR grammars. Unlike the present invention, which relies only on the category descriptions to perform semantic categorization, the “AutoCFGTuning” semantic categorization process used the information present in the IVR grammars (to be tuned) for categorizing user utterances into the semantic categories. Thus, the “AutoCFGTuning” semantic categorization process has more information at its disposal to perform categorization and hence the semantic categorization algorithm is stricter (requires each word in the user utterance to map to at least one word from the grammar entry for a particular semantic category). The semantic categorization process in the present invention relies only on a single sentence (and sometimes more than once) description provided by the system designer to perform the categorization and hence semantic categorization algorithm is less strict (relying on the lexical chain semantic similarity score thresholds rather than having strict rules on the number of valid lexical chain based word mappings required.) 
     Also, the “AutoCFGTuning” semantic categorization process is an offline process (since the overall purpose is IVR grammar tuning and this can be done in an offline manner). Thus, speed of categorization is not an issue in the “AutoCFGTuning” semantic categorization process and hence is not addressed. Semantic categorization is a key component in most dialog systems. The IVR systems must interpret a user&#39;s spoken response to a prompt and then complete an action based on the response with the minimum of delays. Hence, the semantic categorization process described in the present invention needs to be used in an online process and the user&#39;s spoken response to a prompt needs to be categorized into one of the predefined semantic categories with high speed. The use of the configurator (process  12 ) to create the NLP database  14  with all the required information (the calculation of the word pair similarity score is the bottleneck and takes the majority of the categorization processing time) and takes care of the speed issue in the semantic categorizer (process  21 ) for calculating the similarity measure between the words in the description and in the user utterance. 
     Although the present invention and its advantages have been described in detail, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims. Moreover, the scope of the present application is not intended to be limited to the particular embodiments of the process, machine, manufacture, composition of matter, means, methods and steps described in the specification. As one of ordinary skill in the art will readily appreciate from the disclosure of the present invention, processes, machines, manufacture, compositions of matter, means, methods, or steps, presently existing or later to be developed that perform substantially the same function or achieve substantially the same result as the corresponding embodiments described herein may be utilized according to the present invention. Accordingly, the appended claims are intended to include within their scope such processes, machines, manufacture, compositions of matter, means, methods, or steps.