VOICE ACTIVATED RELATIONAL DATABASE SYSTEM PROVIDING USER ASSISTANCE

Abstract: A voice activated system for retrieving information from relational files in a database (element 24) incorporating a "help" mechanism that leads the user to relevant information. The apparatus and the method thereof provide the user with a message (element 4) that the spoken queries (element 8) are understood or not. If understood the system outputs the desired information (element 4). If not understood the system asks for key words from the user (element 4). The user may input phrases, voice new queries to the recognition system (element 8). The system responds with the desired information.

Description: VOICE ACTIVATED RELATIONAL DATABASE SYSTEM PROVIDING USER ASSISTANCE Addendums Four Addendums are attached to this application. Addendum 1 describes a preferred embodiment of a process for deriving meaning from words; Addendum #2 describes a preferred embodiment way of generating SQL rules; Addendum #3 describes a clause parsing tree used as part of a preferred grammar, and Addendum #4 describes a parsing tree for sentences also used in a preferred grammar. Technical Field This invention relates to the general field of retrieving information from related files within a relational database. More particularly, the present invention relates to helping the user retrieve information, from related files stored in a computer system or a network of computer systems, while staying within the vocabulary and semantic boundaries of the system. Background Art Computer applications that utilize a relational (related) database for storing and accessing information relevant to that application are known. The relational databases are typically designed such that information can be stored in one file that 'relates' to information stored in another file within the database structure. The database structure is used herein in a general sense where the databases may be comprised of files accessible over communication networks as well as locally. For example in such systems the database files may be those stored files accessible by any node on the network - the files need not be stored on the local memory of any particular hardware group. Information stored in related files permit rapid retrieval of more complex information than would otherwise be possible. Information stored in such a database provides a convenient method for users to retrieve information by typing in a query with knowledge of the underlying format. More often the user interfaces with a mouse-and-menu and completes a standard query without the user having any information on the underlying format. A limitation of the mouse-and-menu method is that the user must point, click, drag, and type to excess to retrieve information from a database. For example, if a database contained logistical information in multiple files, it can be difficult and time consuming to access and sort through the multiple files looking for related information. In particular, if the user wanted to determine the supply level of a particular type of missile at a particular location being maintained by a particular military unit (an example of information retrieval from three sources) using the mouse-and-menu method, the user would need to search each of the three separate database files or tables, the first file containing the name of the missile; the second containing quantities of the missile at the various locations; and the third containing the military units responsible for maintenance at the locations. More recently, spoken language recognition (voice activation) has been incorporated to make the searching easier to use, especially for complex databases. For example, the user no longer has to manually traverse multiple tables of an application database to find relevant information. The user could simply say, for example, "How many missiles are at the ammunition supply point for the air defense batteries?" However, a voice activated interface has a fixed vocabulary and a limited number of concepts (meaning); such a system does not have the full linguistic capabilities of a human. This limitation is broadened, with the incorporation of a large vocabulary recognition system into a voice activated application, where it is not obvious or intuitive to the user which queries an application may or may not accept, or where the system may not respond to in a meaningful fashion. Disclosure of Invention The limitations of the prior art are overcome in a method and apparatus, having a lexicon and grammar, for retrieving data from files in a relational database. The invention incorporates a help system that responds to the user's inputs by helping keep the user's inquiries within the prescribed vocabulary and semantic boundaries of the system. In a preferred embodiment, the user may input verbal or spoken queries to the system. In a preferred embodiment where the system accepts and digitizes spoken queries, the spoken words are digitized and input to a speech recognition module which determines the meaning of the digitized query. If the meaning can be ascertained, then the system provides information in accordance with that meaning, and if the meaning cannot be ascertained, then a message so indicating is output to the user. In response the user may input key words to the system. The system responds by outputting sample phrases incorporating those key words, wherein those phrases have been generated by the lexicon/grammar in the system. The user may select among the phrases output those relevant phrases for further inquires or the user may formulate new queries based on the phrases (herein phrases is defined to include clauses and sentences) supplied by the apparatus and input these new phrases as queries into the apparatus, via voice or keyboard, for meaning determination. The system apparatus will then return information in accordance with the meaning provided by the apparatus. If a phrase does not lead to further information, that phrase is automatically dropped from the list of phrases. In one aspect the apparatus and method of the present invention provides particular advantages when the vocabulary is comprised of thousands of words and/or when the words are technical in character. Brief Description of the Drawings FIG. 1 is a functional block diagram of the overall system; FIG. 2A is a functional block diagram/flow chart of the system that automatically generates sample questions; FIG. 2B is a functional block diagram/flow chart of the FILTER part of FIG. 2A; and FIG. 3 is a chart showing illustrating the helping function of the present invention. Best Mode for Carrying Out the Invention FIG. 1 shows, in part, an Interface Manager 2, with a display and keyboard 4, a CPU(a computer) 6 and a microphone 8. The microphone receives voiced (audio) signals which are sent to the Interface Manager. The analog audio signals are digitized, as known in the art, within the Interface Manager or the speech recognizer (S/R) 10. The Interface Manager contains software to prepare the S/R module for processing the digitized audio signals as is known in the art. The S/R module may be use the HARC software speech recognizer designed by Bolt Beranek and Newman Inc. (now GTE Internetworking Incorporated). The S/R module 10 utilizes information contained in a lexicon 12 and a language model, including a grammar, 14 and incorporating hidden Markov Models (HMM's) that have been previously trained as described in a paper by John Makhoul and Richard Schwartz entitled, "State of the Art in Continuous Speech Recognition" which is incorporated by reference herein. The output of the S/R is a string of words 16 that is input to a Natural Language Understanding (NLU) module 18. The process describing the workings of the NLU are described in the paper by Madeleine Bates et al. entitled, "The BBN/HARC Spoken Language Understanding System," Addendum 1 attached hereto. The NLU parses the word string resulting in a structure that represents meaning to the application. The meaning of the word string is passed to an Application Monitor 20 where it is translated into commands that the Application 20 can utilize to access relevant database information 24. The retrieved information from the database is passed to the Interface Manager for display 4. The grammars and the method of traversing these grammars to generate phrases utilized by the system to determine meaning of the input word strings is described below. If the NLU cannot parse the word string, the word string is meaningless to the application. A message is passed to the Response Generator 26 to notify the user, via the Interface Manager 2, that the system does not understand the user's input. Details of this foregoing operation are discussed below. Still referring to FIG. 1, the NLU outputs the meaning of the word string to the Applications Monitor 20 for translation into a form that can be process by the database module. A standard database retrieval language is SQL. The NLU module output of meaning, in a preferred embodiment, is translated into SQL, which is used to interrogate the database 34. Rules for this translation are described in Addendum #2 attached hereto. Still referring to FIG. 1, in the event that the user's query does not generate the desired response, the user can elect to invoke the User Assistance module 28 for help. When invoked, the user may enter selected key words that represent general topics (e.g. cities, supplies, cars, tanks, etc.) which when incorporated into a sentence would be understood by the system. The system responds with relevant information retrieved. The system responds to the key word input by either displaying a message that the word was not part of the lexicon 12, or displaying a listing of sample phrases that incorporate the key word(s) of interest and which phrases, when entered back into the system by the user, will generate some meaningful response. The user then reviews the list of displayed sample phrases/sentences to select those relevant to the information the user desires. The user may then ask a query based on the samples presented to the user. The user may input additional words wherein the system will generate meaningful phrases. The operation of the NLU 18 and the grammar 14 are described with respect to a preferred embodiment of a grammar with parsing as described in Addendums #3 and #4 attached hereto. However, since there are a large number of alternatives for the expansion of most non-terminal symbols in the grammar, combined with a large number of optional expansions, resulting in large, complex grammar rules that are difficult to write, debug, and maintain, two layers of software have been added over the recursive transition networks (RTN's) described below. These software layers can be considered as higher level languages for generating grammars. The RTN's are produced through the compilation of simple inputs. The first layer, called "S-rules", is a conventional context-free semantic grammar. S- rules look like RTN rules, but there can be more than one S-rule per non-terminal. A nonterminal appears on the left side of a rule and is changed into something else by the rule. This produces a less cluttered grammar, increases the convenience for the grammar developer, and produces a compact and efficient RTN. The second software layer, called Schema, are templates for producing S-rules which have variables that range over semantic classes and predicates which constrain the possible assignments. For example, a Schema dealing with prepositional phrases that can be post-modifiers of a known phrase is as follows: (Schema pp-postmod-rule x/np/postmod -> y/z/pp ; where (pp-relation? x y z)) where the variables, x, y, and z are constrained by the requirement that z be a preposition which can link an object of semantic class x with an object of semantic class y. A large number of S-rules are automatically produced from this Schema, including the following, for example: unit/np/postmod->supply/of-for/pp unit/np/postmod->point-location/at pp unit/np/postmod->unit under/pp The relation facts about the prepositions and allowable subjects and objects are stored in a separate table, which the instantiation process consults to determine which assignments satisfy the predicate. While Schemas can help the grammar developer to avoid writing s- rules, they do not prevent them from writing S-rules directly if the developer so chooses. If a given phenomenon does not fit any of the domain-independent Schemas available, one can step down to the S-rules level and cover them directly. FIG. 2 A shows more detail of the phrase generating portion of the NLU 18', Grammar 14, and lexicon 12 when the system fails to respond to the user's input in a meaningful way. As described above the user may enter key words relevant to the information the user desires. The system responds by displaying a listing of many phrases/sentences 30 containing the keywords understood by the system for a particular application. The listing of phrases/sentences is filtered 32 to remove all phrases that do not retrieve any information. The result is a listing of examples 34 that the system will understand and retrieve information relevant thereto. The filter module 32 is shown in FIG. 2B. The purpose of this module is to drop from the list of possible phrases those phrases that will not retrieve any information when used as a queries to the system. Each possible phrase 36, which is a word string, is input to the NLU module which converts the phrase into a representation of the meaning. The meaning is translated 40 into a command form that is passed to the database retrieval program 42. If no result is retrieved 44, the phrase is deleted from the list 46, and conversely if a result is found the phrase remains on the list 48. If the phrase was the last one being analyzed 50 the list is complete and the process ends 52. If not the last phrase 54, the system analyzes the next phrase on the list. FIG. 3 is an example of typical responses to the key word "howitzers" input from a user. In this particular example, with a lexicon and grammar such as described in the addendums, the list of meaningful phrases, where meaning to the system refers to where the lexicon and grammar determine that the phrase is a proper construction. Notice the key word "howitzers" is a military term, and this preferred embodiment system is built around military meanings. Such a system produces the phrase shown. The phrases are presented to the user for the user's selection designating those phrases which are useful, if any. The user can generate additional questions and/or key words for searching through the database or database files from the phrases returned. The user may add words to the lexicon which are incorporated with the grammar into new phrases. ADDENDUM #1 IEEE International Conference on Acoustics, Speech, and Signal Processing, Minneapolis, Minnesota, April 27-30, 1993, pp. 11-111-114. THE BBN/HARC SPOKEN LANGUAGE UNDERSTANDING SYSTEM Madeleine Bates, Robert Bobrow, Pascale Fung, Robert Ingria, Francis Kubala, John Makhoul, Long Nguyen, Richard Schwartz, David Stallard BBN Systems and Technologies Cambridge, MA 02138, USA ABSTRACT We describe the design and performance of a complete spoken language understanding system currently under development at BBN. The system, dubbed HARC (Hear And Respond to Continuous speech), successfully integrates state-of-the-art speech recognition and natural language understanding subsystems. The system has been tested extensively on a restricted airline travel information (ATIS) domain with a vocabulary of about 2000 words. HARC is implemented in portable, high-level software that runs in real time on today's workstations to support interactive online human- machine dialogs. No special purpose hardware is required other than an A D converter to digitize the speech. The system works well for any native speaker of American English and does not require any enrollment data from the users. We present results of formal DARPA tests in Feb. '92 and Nov. '92. 1. OVERVIEW The BBN HARC spoken language system weds two technologies, speech recognition and natural language understanding, into a deployable human- machine interface. The problem of understanding goal-directed spontaneous speech is harder than recognizing and understanding read text, due to greater variety in the speech and language produced. We have made minor modifications to our speech recognition and understanding methods to deal with these variabilities. The speech recognition uses a novel multipass search strategy that allows great flexibility and efficiency in the application of powerful knowledge sources. The natural language system is based on formal linguistic principles with extensions to deal with speech errors and to make it robust to natural variations in language. The result is a very usable system for domains of moderate complexity. While the techniques used here are general, the most complete test of the whole system thus far was made using the ATIS corpus, which is briefly described in Section 2. Section 3 describes the techniques used and the results obtained for speech recognition and Section 4 is devoted to natural language. The methods for combining speech recognition and language understanding, along with results for the combined system are given in Section 5. Finally, in Section 6, we describe a real-time implementation of the system that runs entirely in software on a single workstation. More details on the specific techniques, the ATIS corpus, and the results can be found in the papers presented at the 1992 and 1993 DARPA Workshop on Speech and Natural Language [1 , 2, 3, 4]. 2. THE ATIS DOMAIN AND CORPUS The Air Travel Information Service (ATIS) is a system for getting information about flights. The information contained in the database is similar to that found in the Official Airline Guide (OAG) but is for a small number of cities. The ATIS corpus consists of spoken queries by a large number of users who were trying to solve travel related problems. The ATIS2 training corpus consists of 12,214 spontaneous utterances from 349 subjects who were using simulated or real speech understanding systems in order to obtain realistic speech and language. The data originated from 5 collection sites using a variety of strategies for eliciting and capturing spontaneous queries from the subjects [4]. Each sentence in the corpus was classified as class A (self contained meaning), class D (referring to some previous sentence), or class X (impossible to answer for a variety of reasons). The speech recognition systems were tested on all three classes, although the results for classes A and D were given more importance. The natural language system and combined speech understanding systems were scored only on classes A and D, although they were presented with all of the test sentences in their original order. The Feb '92 and Nov '92 evaluation test sets had 971 and 967 sentences respectively from 37 and 35 speakers with an equal number of sentences from all 5 sites. For both test sets, about 43% of the sentences were class A, 27% were class D, and 30% were class X. The recognition mode was speaker-independent - the test speakers were not in the training set and every sentence was treated independently. 3. BYBLOS — SPEECH RECOGNITION BYBLOS is a state-of-the-art, phonetically-based, continuous speech recognition system that has been under development at BBN for over seven years. This system introduced an effective strategy for using context- dependent phonetic hidden Markov models (HMM) and demonstrated their feasibility for large vocabulary, continuous speech applications [5]. Over the years, the core algorithms have been refined primarily on artificial applications using read speech for training and testing. 3.1. New Extensions for Spontaneous Speech Spontaneous queries spoken in a problem-solving dialog exhibit a wide variety of disfluencies. There were three very frequent effects that we attempted to solve - excessively long segments of waveform with no speech, poorly transcribed training utterances, and a variety of nonspeech sounds produced by the user. We eliminated long periods of background with a heuristic energy- based speech detector. But typically, there are many untranscribed short segments of background silence remaining in the waveforms after truncating the long ones, which measurably degrade the performance gain usually derived from using crossword-boundary triphone HMMs. We mark the missing silence locations automatically by running the recognizer on the training data constrained to the correct word sequence, but allowing optional silence between words. Then we retrained the model using the output of the recognizer as corrected transcriptions. Spontaneous data from naive speakers has a large number and variety of nonspeech events, such as pause fillers (urn's and uh's), throat clearings, coughs, laughter, and heavy breath noise. We attempted to model a dozen broad classes of nonspeech sounds that were both prominent and numerous. However, when we allowed the decoder to find nonspeech models between words, there were more false detections than correct ones. Because our silence model had little difficulty dealing with breath noises, lip smacks, and other noises, our best results were achieved by making the nonspeech models very unlikely in the grammar. 3.2. Forward-Backward N-best Search Strategy The BYBLOS speech recognition system uses a novel multi-pass search strategy designed to use progressively more detailed models on a correspondingly reduced search space. It produces an ordered list of the N top-scoring hypotheses which is then reordered by several detailed knowledge sources. This N-best strategy [6, 7] permits the use of otherwise computationally prohibitive models by greatly reducing the search space to a few (N=20-100) word sequences. It has enabled us to use cross-word- boundary triphone models and trigram language models with ease. The N- best list is also a robust interface between speech and natural language that provides a way to recover from speech errors. We use a 4-pass approach to produce the N-best lists for natural language processing. 1 . A forward pass with a bigram grammar and discrete HMM models saves the top word-ending scores and times [8]. 2. A fast time-synchronous backward pass produces an initial N-best list using the Word-Dependent N-best algorithm [9]. 3. Each of the N hypotheses is rescored with cross-word boundary triphones and semi-continuous density HMMs. 4. The N-best list is rescored with a trigram grammar. Each utterance is quantized and decoded three times, once with each gender-dependent model and once with a gender-independent model. (In the Feb '92 test we did not use the gender-independent model.) For each utterance, the N-best list with the highest top-1 hypothesis score is chosen. The top choice in the final list constitutes the speech recognition results reported below. Then the entire list is passed to the language understanding component for interpretation. 3.3. Training Conditions Below, we provide separate statistics for the Feb (Nov) test as n1 (n2). We used speech data from the ATIS2 sub-corpus exclusively to train the parameters of the acoustic model. However, we filtered the training data for quality in several ways. We removed from the training any utterances that were marked as truncated, containing a word fragment, or containing rare nonspeech events. Our forward-backward training program also automatically rejects any input that fails to align properly, thereby discarding many sentences with incorrect transcriptions. These steps removed 1 ,200 (1 ,289) utterances from consideration. After holding out a development test set of 890 (971 ) sentences, we were left with a total of 7670 (10925) utterances for training the HMMs. The recognition lexicon contained 1881 (1830) words derived from the training corpus and all the words and natural extensions from the ATIS application database. We also added about 400 concatenated word tokens for commonly occurring sequences such as WASHINGTON_D_C, and D_C_TEN. Only 0.4% (0.6%) of the words in the test set were not in the lexicon. For statistical language model training we used all available 14,500 (17,313) sentence texts from ATIS0, ATIS1 , and ATIS2 (excluding the development test sentences from the language model training during the development phase). We estimated the parameters of our statistical bigram and trigram grammars using a new backing-off procedure [10]. The n-grams were computed on 1054 (1090) semantic word classes in order to share the very sparse training (most words remained singletons in their class). 3.4. Speech Recognition Results Table 1 shows the official results for BYBLOS on this evaluation, broken down by utterance class. We also show the average perplexity of the bigram and trigram language models as measured on the evaluation test sets (ignoring out-of-vocabulary words). Sentence Bigram Trigram Feb(Nov) Class Perplex Perplex % Word Errors A+D 17 12 6.2(4.3) A+D+X 20 15 9.4(7.6) A 15 10 5.8(4.0) D 20 14 7.0(4.8) X 35 28 17.2(14.5) Table 1 : Official SPREC results on Feb (Nov) '92 test sets. The word error rate in each category was lower than any other speech system reporting on this data. The recognition performance was well correlated with the measured perplexities. The trigram language model consistently, but rather modestly, reduced perplexity across all three classes. (However, we observed that word error was reduced by 40% on classes A+D with the trigram model.) The performance on the class X utterances (those which are unevaluable with respect to the database) is markedly worse than either class A or D utterances. Since these utterances are not evaluable by the natural language component, it does not seem profitable to try to improve the speech performance on these utterances for a spoken language system. 4. DELPHI — NATURAL LANGUAGE UNDERSTANDING The natural language (NL) component of HARC is the DELPHI system. DELPHI uses a definite clause grammar formalism, augmented by the use of constraint nodes [11] and a labeled argument formalism [3]. Our initial parser used a standard context-free parsing algorithm, extended to handle a unification-based grammar. It was then modified to integrate semantic processing with parsing, so that only semantically coherent structures would be placed in the syntactic chart. The speed and robustness were enhanced by switching to an agenda-based chart-parser, with scheduling depending on the measured statistical likelihood of grammatical 13 rules [12]. This greatly reduced the search space for the best parse. The most recent version of DELPHI includes changes to the syntactic and semantic components that maintain the tight syntactic/semantic coupling characteristic of earlier versions, while allowing the system to provide semantic inteφretations of input which has no valid global syntactic analysis. This included the development of a "fallback component" [2], in which statistical estimates play an important role. This component allows DELPHI to deal effectively with linguistically ill-formed inputs that are common in spontaneous speech, as well as with the word errors produced by the speech recognizer. 4.1. Parsing as Transduction — Grammatical Relations The DELPHI parser is not a device for constructing syntactic trees, but an information transducer. Semantic interpretation is a process operating on a set of messages characterizing local "grammatical relations" among phrases, rather than as a recursive tree walk over a globally complete and coherent parse tree. The grammar has been reoriented around local grammatical relations such as deep-structure subject and object, as well as other adjunct-like relations. The goal of the parser is to make these local grammatical relations (which are primarily encoded in ordering and constituency of phrases) readily available to the semantic inteφreter. From the point of view of a syntactic-semantic transducer, the key point of any grammatical relation is that it licenses a small number of semantic relations between the "meanings" of the related constituents. Sometimes the grammatical relation constrains the semantic relation in ways that cannot be predicted from the semantics of the constituents alone (e.g. Given "John", "Mary", and "kissed", only the grammatical relations or prior world knowledge determine who gave and who received). Other times the grammatical relation simply licenses the only plausible semantic relation (e.g., "John", "hamburger", and "ate"). Finally, in sentences like "John ate the fries but rejected the hamburger", semantics would allow the hamburger to be eaten, but syntax tells us that it was not. Grammatical relations are expressed in the grammar by giving each 14 element of the right hand side of a grammar rule a grammatical relation as a label. A typical rule, in schematic form, is: (NP ...) → :HEAD (NP ...) .PP--COMP (PP: PREP...) which says that a noun phrase followed by a prepositional phrase provides evidence for the relation PP-COMP between the PP and HEAD of the NP. One of the right-hand elements must be labeled the "head" of the rule, and is the initial source of information about the semantic and syntactic "binding state" which controls whether other elements of the right-hand side can "bind" to the head via their labeled relation. This view made it possible to both decrease the number of grammar rules (from 1143 to 453) and increase syntactic coverage. Most attachments can be modeled by simple binary adjunction, and since the details of the syntactic tree structure are not central to a transducer, each adjunct can be seen as being "logically attached" to the "head" of the constituent. This scheme allows the adjunction rules of the grammar to be combined together in novel ways, governed by the lexical semantics of individual words. The grammar writer does not need to foresee all possible combinations. 4.2. "Binding rules" — the Semantics of Grammatical Relations The interface between parsing and semantics is a dynamic process structured as two coroutines in a cascade. The input to the semantic interpreter is a sequence of messages, each requesting the semantic "binding" of some constituent to a head. A set of "binding rules" for each grammatical relation licenses the binding of a constituent to a head via that relation by specifying the semantic implications of binding. These rules specify features of the semantic structure of the head and bound constituent that must be true for binding to take place, and may also specify syntactic requirements. Rules may also allow certain semantic roles (such as time specification) to have multiple fillers, while other roles may allow just one filler. As adjuncts are added to a structure, the binding list is conditionally extended as long as semantic coherence is maintained. When a constituent is syntactically complete (i.e., no more adjuncts are to be added), DELPHI evaluates rules that check for semantic completeness and produce an 15 "inteφretation" of the constituent. 4.3. Robustness Based on Statistics and Semantics Unfortunately, simply having a transduction system with semantics based on grammatical relations does not deal directly with the key issue of robustness - the ability to make sense of an input even if it cannot be assigned a well-formed global syntactic analysis. In DELPHI we view standard global parsing as merely one way to obtain evidence for the existence of the grammatical relations in an input string. DELPHI'S strategy is based on two other sources of information. DELPHI applies semantic constraints incrementally during the parsing process, so that only semanti- cally coherent grammatical relations are considered. Additionally, DELPHI has statistical information on the likelihood of various word senses, grammatical rules, and grammatical-semantic transductions. Thus DELPHI can rule out many locally possible grammatical relations on the basis of semantic incoherence, and can rank alternative local structures on the basis of empirically measured probabilities. The net result is that even in the absence of a global parse, DELPHI can quickly and reliably produce the most probable local grammatical relations and semantic content of various fragments. DELPHI first attempts to obtain a complete syntactic analysis of its input, using its agenda-based best-first parsing algorithm. If it is unable to do this, it uses the parser in a fragment-production mode, which produces the most probable structure for an initial segment of the input, then restarts the parser in a top down mode on the first element of the unparsed string whose lexical category provides a reasonable anchor for top-down prediction. This process is repeated until the entire input is spanned with fragments. Experiments have shown that the combination of statistical evaluation and semantic constraints produces chunks of the input that are very useful for inteφretation by non-syntactically-driven strategies. 4.4. Advantages of This Approach The separation of syntactic grammar rules from semantic binding and completion rules greatly facilitates fragment parsing. While it allows syntax and semantics to be strongly coupled in terms of processing (parsing and 16 semantic inteφretation) it allows them to be essentially decoupled in terms of notation. This makes the grammar and the semantics considerably easier to modify and maintain. 5. COMBINED SPOKEN LANGUAGE SYSTEM The basic interface between BYBLOS and DELPHI in HARC is the N- best list. In the most basic strategy, we allowed the NL component to search arbitrarily far down the N-best list until it either found a hypothesis that produced a database retrieval or reached the end of the N-best list. However, we have noticed in the past that, while it was beneficial for NL to look beyond the first hypothesis in an N-best list, the answers obtained by NL from speech output tended to degrade the further down in the N-best list they were obtained. We optimized both the depth of the search that NL performed on the N-best output of speech and how we used the fall-back strategies for NL text processing [2]. We found that, given the current performance of all the components, the optimal number of hypotheses to consider was N=10. Furthermore, we found that rather than applying the fall-back mechanism to each of these hypotheses in turn, it was better to make one pass through the N-best hypotheses using the full parsing strategy, and then, if no sentences were accepted, make another pass using the fall-back strategy. In Tables 2 and 3 we show the official performance on the Feb and Nov '92 evaluation data. The percent correct and the weighted error rate is given for the DELPHI system operating on the transcribed text (NL) and for the combined HARC system (SLS). The weighted error measure weights incorrect answers twice as much as no answer. Corpus NL Cor NL WE SLS Cor SLS WE A+D 76.7 33.9 71.8 43.7 A 80.1 26.4 74.9 35.8 D 71.9 44.6 67.4 54.7 Table 2: % Correct and Weighted error on the Feb '92 test set. 17 Coφus NL Cor NL WE SLS Cor SLS WE A+D 85.0 22.0 81.0 30.6 A 88.8 15.7 84.6 23.7 D 78.6 32.8 74.9 42.5 Table 3: % Correct and Weighted error on the Nov '92 test set. The weighted error on context-dependent sentences (D) is about twice that on sentences that stand alone (A). First, it is often difficult to resolve references correctly and to know how much of the previous constraints are to be kept. Second, in order to understand a context-dependent sentence correctly, we must correctly understand at least two sentences. The weighted error from speech input is from 8%-10% higher than from text, which is lower than might be expected. Even though the BYBLOS system misrecognized at least one word in 25% of the utterances, the DELPHI system was able to recover from most of these errors through the use of the N-best list and fallback processing. The SLS weighted error was 30.6%, which represents a substantial improvement in performance over the weighted error during the previous (February '92) evaluation, which was 43.7%. Based on end-to-end tests with real users, the system is usable, given that subjects were able to accomplish their assigned tasks. 6. REAL-TIME IMPLEMENTATION A real-time demonstration of the entire spoken language system described above has been implemented. The speech recognition was performed using BBN HARK™, a commercially available product for continuous speech recognition of medium-sized vocabularies (about 1 ,000 words). HARK stands for High Accuracy Recognition Kit. HARK™ (not to be confused with HARC) has essentially the same recognition accuracy as BYBLOS but can run in real-time entirely in software on a workstation with a built-in A/D converter (e.g., SGI Indigo, SUN Sparc, or HP715) without any additional hardware. The speech recognition displays an initial answer as soon as the user 18 stops speaking, and a refined (rescored) answer within 1-2 seconds. The natural language system chooses one of the N-best answers, inteφrets it, and computes and displays the answers, along with a paraphrase of the query so the user can verify what question the system answered. The total response cycle is typically 3-4 seconds, making the system feel extremely responsive. The error rates for knowledgeable interactive users appears to be much lower than those reported above for naive noninteractive users. 7. SUMMARY We have described the HARC spoken language understanding system. HARC consists of a modular integration of the BYBLOS speech recognition system with the DELPHI natural language understanding system. The two components are integrated using the N-best paradigm, which is a modular and efficient way to combine multiple knowledge sources at all levels within the system. For the Class A+D subset of the November '92 DARPA test the official BYBLOS speech recognition results were 4.3% word error, the text understanding weighted error was 22.0%, and the speech understanding weighted error was 30.6%. Finally, the entire system has been implemented to run in real time on a standard workstation without the need for any additional hardware. Acknowledgment This work was supported by the Defense Advanced Research Projects Agency and monitored by the Office of Naval Research under Contract Nos. N00014-91 -C-0115, and N00014-92-C-0035. REFERENCES [1] F. Kubala, C. Barry, M. Bates, R. Bobrow, P. Fung, R. Ingria, J. Makhoul, L. Nguyen, R. Schwartz, D. Stallard, "BBN BYBLOS and HARC February 1992 ATIS Benchmark Results", Proc. of the DARPA Speech and Natural Language Workshop, Morgan Kaufmann Publishers, Feb. 1992. [2] Bobrow R., D. Stallard, "Fragment Processing in the DELPHI System", Proc. of the DARPA Speech and Natural Language Workshop, Morgan Kaufmann Pub., Feb. 1992. 19 [3] Bobrow, R., R. Ingria, and D. Stallard, '"Syntactic/Semantic Coupling in the DELPHI System", Proc. of the DARPA Speech and Natural Language Workshop, Morgan Kaufmann Pub., Feb. 1992. [4] MADCOW, "Multi-Site Data Collection for a Spoken Language Coφus", Proc. of the DARPA Speech and Natural Language Workshop, Morgan Kaufmann Pub., Feb. 1992. [5] Chow, Y., M. Dunham, O. Kimball, M. Krasner, G.F. Kubala, J. Makhoul, P. Price, S. Roucos, and R. Schwartz (1987) "BYBLOS: The BBN Continuous Speech Recognition System," IEEE ICASSP87, pp. 89- 92 [6] Chow, Y-L. and R.M. Schwartz, "The N-Best Algorithm: An Efficient Procedure for Finding Top N Sentence Hypotheses", ICASSP90, Albuquerque, NM S2.12, pp. 81 -84. [7] Schwartz, R., S. Austin, Kubala, F., and J. Makhoul, "New Uses for the N-Best Sentence Hypotheses Within the BYBLOS Speech Recognition System", ICASSP92, San Francisco, CA, pp. 1.1 -1.4. [8] Austin, S., Schwartz, R., and P. Placeway, "The Forward-Backward Search Algorithm", ICASSP91 , Toronto, Canada, pp. 697-700. [9] Schwartz, R. and S. Austin, "A Comparison Of Several Approximate Algorithms for Finding Multiple (N-Best) Sentence Hypotheses", ICASSP91 , Toronto, Canada, pp. 701 -704. [10] Placeway, P., Schwartz, R., Fung, P., and L Nguyen, "The Estimation of Powerful Language Models from Small and Large Corpora", To be presented al ICASSP93, Minneapolis, MN. [1 1] Stallard, D., "Unification-Based Semantic Interpretation in the BBN Spoken Language System", Proc. of the DARPA Speech and Natural Language Workshop, Morgan Kaufmann Pub., Oct. 1989, pp. 39-46. [12] Bobrow, R., "Statistical Agenda Parsing", Proc. of the DARPA Speech and Natural Language Workshop, Morgan Kaufmann Publishers, Feb. 1991 , pp. 222-224. 20 ADDENDUM # 2 The macro form "SQL" is used to enter SQL translation rules into the system, either from files or at the list prompt. A rule constructed with this macro has the following 5 BNF: 1) <RULE> :: = (SQL <PREDICATE-SYMBOL> (<VAR-TRANSLATION>+ ) <RELATION-SPEC> 0 <CONSTRAINT>*) 2) <VAR-TRANSLATION> :: = (<LF-VAR> <ATTRIBUTE-REF>+) 3) <LF-VAR :: = l y l z ... 4) <ATTR_BUTE-REF> :: = 5 <ATTRIBUTT_-NAME> <RELAΗON-NAME>.<ATTRBUTE-NAME> <TABLE-VAR>.<ATTRIBUTE-NAME> 5) <TABLE-VAR> :: = a I b I c ... 6) <RELATION-SPEC> : := <RELATION-NAME> ] (<TABLE- 0 VAR>.<RELATION-NAME>+) 7) <CONSTRAINT> :: = (<OP> <TERM> <TERM>) 8) <OP> :: + GT | GE | LT | LE | EQ | LIKE 9) <TERM> : := <ATTRIBUTE-REF> I <FUNCTION-TERM> I 5 <CONSTANT> 10) <FUNCTION-TERM> : := (<FUNCTION> <TERM>+ 1 ) 11) <FUNCTION> ::= + l - l * l / 12) <CONSTANT> : := <STRING> | <NUMBER> Here's a simple example of a rule: 0 (SQL SUPPLY ((X NSN)) NSN . DESCRIPTION) It defines the one-place predicate 'SUPPLY' in terms of the attribute NSN of the 21 table NSN_DESCRIPTION. Note that the argument places of the predicate are in a one-to-one correspondence with the entries in the <VAR-TRANSLATION> list. By convention, we use symbols like 'X' and Υ to make this correspondence clearer to read off. The following is an example of a translation rule for a two-place predicate: (sql nomenclature ((x nsn) (y nomenclature)) nsn_description) The predicate 'NOMENCLATURE' is a two-place predicate which relates SUPPLY-objects and their names. This rule defines that Predicate in terms of the NSN field of the table NSN_DESCRIPTION (the representation for the SUPPLY- object) and the NOMENCLATURE field of that table (the representation of the name). A quick aside about these higher-than-one-place Predicates. While in formulae, they often appear as functions instead of predicates, e.g.: { LIKE (NOMENCLATURE x) "TANK%") we do not have to worry about this distinction when writing SQL-rules. A function of N argument places is simply viewed as a predicate of N+ 1 argument places, and a SQL-rule written for it accordingly. Now to somewhat more complicated rules. Sometimes, a given predicate is translated in terms of more than one table. For example: (sql unit-supplies ((x a.uic) (y b.nsn)) (a-ue_stock_report b.nsn_description) (eq a.lin b.lin)} This rule says that the two-place relation between UNITs and their SUPPLYs is defined in by the UNIT'S having a value of LIN in its row of UE_STOCK_REPORT that is the same as the value of LEN in the SUPPLY'S row of NSN_DESCRIPTION. The hairer looking notation with all the 'a."s 'b."s is there to distinguish which attribute-references goes with which table. The rule defines 'A' as an instance of UE_STOCK_REPORT and 'B' as an instance of NSN_DESCRIPTION, and "A.LIN' as thus coming from that instance of UE_STOCK_REPORT and 'B.L_N' from that instance of NSN_DESCRIPTION. When we only have one relation, as in the previous rules, these distinctions don't matter, but when more than one relation is 22 present, we need this notational device. The linking 'EQ' in this rule is one example of a <CONSTRAINT>, but there are others possible. For example, suppose that the UE_STOCK_REPORT table has a 'QTY' field which can have the value 0, and we want to say that a UNIT has a SUPPLY-object only if it has more than QTY 0 of them. Then we would write the rule as: (sql unit-supplies ((x a.uic) (y b.nsn) } (a.ue_stock_report b.nsn_description) (eq a.lin b.lin) (gt a.qty 0)) We won't go into any other examples of constraints, except to point out that if there are N <RELATION-SPEC>s in a rule, there must be N-l linking EQ-type <CONSTRAINT>s connecting them. Note that a <VAR-TRANSLATION> can have more than <ATTRIBUTE-REF>. This is because objects in the logical form are sometimes represented by more than one field in the database. As an example, consider the rule: (sql shipment ((x manifest_id nsn)) manifest_detail)) This rule says that a 'SHIPMENT' in the database is indexed by a MAN-PEST ID - an id for some sort of master plan of shipments - and an NSN - the id for the type of thing being shipped. 23 ADDENDUM # 3 1 Parse Tree -> Clauses Once a parse tree is built, the next stage is to traverse the tree and to produce s set of clauses. Most of the clauses are generated according to a node rule; however, wheu the parse tree does not match any node rule in the database, a default mapping is used. 1.1 Node Rule The node rule has the following structure: node-rule <LABEL-TEST> required (<TEST-EXTRACT>*) optional (<TEST-EXTRACT>*) :result (<CLAUSE-PATTERN> +) where <LABEL-TEST> symbol or predicate <TEST-EXTRACT> (<TEST> <EXTRACT>) <TEST> symbol or predicate <EXTRACT> (INTERP <VAR>) or (CLAUSES <PATTERN>+) The <LABEL-TEST> constrains what type of tree match the rules. If the label test is a symbol, the semantic class of the tree must match the symbol in order for the tree to match the rule, If the label test is a predicate, then the predicate must be satisfied in order for a match. Some predicates are SUB-CLASS?, SYN-CLASS?, AND, OR, NOT, etc. The :RESULT keyword specifies a template clause list, with meta-variables that need to get filled in. The :REQUIRED and :OPTIONA.L slots coutain meta-variables whose values are assigned during the inteφretation of child trees. The values of these meta-variable are later substituted into the :RESULT template clause list. If <EXTRACT> is of the form INTERP, the logic variable d the matching child inteφretation is substituted for <VAR> in the :RESULT template, and the clauses of the inteφretation are appended to it. If it is of CLAUSES form, an additional pattern match is done on the <PATTERN>s, and the meta variables instantiated in those patterns are 24 substituted into the :RESULT template. These two slots also contain tests that the child trees have to pass in order to match the rule If the :REQUIRED slot is present, all the <TEST-EXTRACTS> in it have to match for the rule to succeed. If the :OPTIONAL keyword is present, the <TEST- EXTRACTS> are matched if possible. At least one of these two keywords has to be present, and both are allowed at the same time, Lets take the example, Show me the artillery units. This sentence produces the parse tree: <PARSE-TREE> := (LAD/UTT (SHOW/COMMAND-CLAUSE (SHOW/INFINITIVE SHOW ME) (MILITARY-UNΠVNP (DET/DET THE) (MILΓΓARY-UNIT/PREMOD (MILΓΓARY-UNIT/ADJ ARTILLERY)) (MILITARY-UNIT/NP/HEAD UNITS)))) Since this is a standard parse tree with LAD UTT as the root node, it is traversed by the subfunction INTERPRET-ORDINARY-TREE. This function gets recursively invoked to obtain the inteφretation of the child sub-trees. (SHOW/INFINITINE SHOW ME) -> T44 (gensymed variable) This sub-tree has no childrein it's a terminal string, where the terminal is just the cdr. <SUB-TREE> := (MILITARY-UΝIT/ΝP (DET/DET THE) (MILITARY-UNΠVPREMOD (MILΓΓARY-UNΓΓ/ADJ ARTILLERY)) (MILΓΓARY-UNΠVNP/HEAD UNITS)) (DET/DET THE -> DEFINITE T46) <SUB-TREE> := (M_L_TARY-UNIT/PREMOD (MILITARY-UNIT/ADJ ARTILLERY)) MILITARY-UNIT/PREMOD -> (MILITARY-UNIT T47) (MILΓΓARY-UNΓΓ/ADJ ARTILLERY) -> (T48 (LIKE (URI T48) "%aRΗLLERY")) The inteφreter looks up the word "ARTILLERY" as a semantic class MILITARY- UNIT in the lexicon and it finds: (LIKE (URI*) "%Artillery")) It substitutes the new variable for * and returns (T48 (LIKE (URI T48) "%aRTILLERY")). (MILITARY-UNIT/NP/HEAD UNITS) -> (T49 (MILITARY-UNIT T49)) At this stage, the system must combine the inteφretations of the subtrees with the 25 current parent. There are different methods for doing this, depending upon the relationship of the sub-tree and the current tree. In the this case, the relationship is simple: the children trees and the current tree have the same semantic class - MILITARY-UNIT. So the inteφretations are merged. For instance, (MILITARY-UNIT/NP/HEAD UNIT) has the inteφretation (T49 (MILITARY-UNIT T49)) and (MILITARY-UNIT/ADJ ARTILLERY) has the inteφretation (T48 (LIKE (URI T48) "%Artillery*)). A new variable, for instance T45, substitutes T48 and T49 and all the lists are merged together. The final result being: (T45 ((MILITARY-UNIT T45) (DEFINITE T45) (MILITARY-UNIT T45) (LIKE (URI T45) "% Artillery") (MILITARY-UNIT T45))) 26 ADDENDUM #4 1 Sentence -> Parse Tree After a speech recognition event, VALAD parses the sentence to form a parse tree. This section describes the modules needed to specify a grammar. VALAD's grammar specifying mechanism is compromised of five modules (see Figure 1): • schemas - the syntactic rule schemata that produce s-rules from known semantic classes and modifier rules • s-rules - the semantic grammer rules thst produce the RTN networks • lexicons - the system's vocabulary • RTN networks - the recursive Transition networks • LISP Code - the Lisp code the networks are compiled into. 1.1 Schemas The structure of a schema is as follows: <NAME> <LHS> -> <RHS> : where <CONSTRAINTS> where <NAME> = symbol <LHS> = label <R/IS> = label or list containing :OPTs, :ORs and :BIGRAM-ORs <CONSTRAINTS> = predicates (ex. (premodifier x y) (object x y) transitive? x)) The <LHS>' and <KHS> of a schema contain variables, which are distinguished symbols, (X, Y, Z, Q, T, S, T, U, V, W} . In many cases, there can be multiple expansion definitions for a single <LHS> label. An s-rule is produced by 1 Labels are symbols having the structure SEMANTIC-CLASS/SYNTACTIC-CLASS. Sometime there are extra subdivision on the SYNTACTIC-CLASS, and hence more slashes. 27 substituting the same semantic class for all instances of a variable, for instance "X". Part of compilation is to perform this substitution for all declared semantic classes in the system. However, expanding schemas for each semantic class produces an overgeneration of useless s-rules that contain unexpandable constituents. To prevent this overgenerstion problem, if an s-rule contains an unexpandable constituent, it is pruned out. In addition, schemas can optionally contain a set of constraints on label variables. Only when these constraints are met, the s-rules are generated. Lets take for example the command-clause-rule schema for the semantic class SHOW: (schema command-clause-rule x/command-clause x/infinitive (:or y/np y/spec) (:opt (:bigram-or x/vp/postmod)) : where (object x y) (strict-sub-class? x 'command) ) In VALAD's domain model, SHOW is a subclass of the class COMMAND; therefore the constraint (strict-sub-class? x 'command) is satisfied for x = SHOW. All instances of x are replaced by SHOW. The constraint (object x y) reduces the set of y to y e {the classes that are direct objects of X } . The SHOW class has the following object modifiers 2 ; 1. (modifier object show displayable-item SHOW-ADD-REMOVE- DISPLAYABLE-1TEM-OF), 2. (modifier object show map-area show-area), 3. (modifier object show glad-screen go-to-glad-screen-of). For X = SHOW, y e {displayable-item map-area glad-screen}. The s-rule produced is 2 Grammar relations in VALAD are specified in the following ray: (modifier grammer-relation class 1 class2 semantic-relation) (Refer to Section 1.2.2). show/command-clause -> (SHOW/INFINITIVE (:OR(DISPLAYABLE-ITEM/NP (:OPT (:BIGRAM-OR SHOW/NP/POSTMOD))) (MAP-AREA/ΝP (:OPT (:BIGRAM-OR SHOW/VP/POSTMOD))) (GLAD-SCREEΝ/ΝP (:OPT (:BIGRAM-OR SHOW/VP/POSTMOD))))) 1.2 S-Rule The s-rule structure is as follows: <RULE-ΝAME> <LHS> <RHS> The variable types are the same as those in the schemas. Approximately 80 % of VALAD's s-rules are automatically generated via schemas; however, there are some cases where schemas cannot generate a s-rule needed to represent the grammar. In this case, the programmer directly inputs a s-rule into the system. Lets take, for example, how the command 'ZOOM IN' is specified in VALAD. Two s-rules specified: 1. (s-rule lad/utt-1 1 lad/utt (:bias zoom clause 3.0)) 2. (s-rule zoom-rule zoom/clause (zoom/* zoom-in-or-out/lex (:opt (:or lots/spec (by number-spec/lex))))) The first s-rule adds an extra expansion definition to the root node lad/utt. The second s-rule describes the further expansion of the zoom clause. 1.2.1 Modifiers Modifiers specifies the grammatical and syntactical relations between two classes. These mistions constrain which s-rules are generated from schemas. Take, for instance, the schema: X/CLAUSE - > Y/NP X/VP :where (subject X Y) 29 Due to the constraint, only the s-rules where the class Y is a subject of the class X are generated. The structure of a modifier is (<GRAMMAR-RELATION> <CLASS1> <CLASS2> <SYNTACTIC- RELATIONS, where <GRAMMAR-RELATION> :: = {object, subject, np- postmod, premodifier, time}. 1.2.2 Possessive/PP Modifiers Possessive and PP modifiers are slightly different from the modifiers described in the previous section. When this type of modifier is loaded into the system, an s-rule is generated, installed in the s-rule database and placed in *s-rule-labels-for- recompile* to be compiled later. <GRAMMAR-RELATION> is the concatenated list of linking words separated by hyphens. The s-rule generated from a pp modifier is <RULE-NAME> = PP-MODIFIER <LHS> = <CLASS2>/<GRAMMAR-RELATION>/PP <RHS> = ((:OR (LIST OF GRAMMAR RELATION WORDS)) (<CLASS2>/NP)) The s-rule generated from a possessive modifier is <RULE-NAME> = PP-MODIFIER <LHS> = <CLASS2>/<GRAMMAR-RELATION>/PP <RHS> = (possessive/lex <CLASS2>/NP)) 1.3 Lexicons The scheme for a lexicon is as follows: (WORD <LABEL> ( < ANNOTATED-SPELLING > + ) <SEMANTICS>) <LABEL> is the class to which the word belongs. <ANNOTATED-SPELLING> can be of the form: • <WORD-TOKEN>+ or • <WORD-TOKEN>+ > <PHONETIC-TOKEN>+ Word tokens are just units of spelling, like "TANK", "TANKS", "SAN" and "FRANCISCO". The delimiter ">", if present, indicates the end d the word tokens and the beginning of phonetic tokens that are their pronunciations. Phonetic tokens, 30 if they appear, must be in one-to-one correspondence with the word tokens they follow, and are the expressions allowed by the HARK phonetic notation system, plus a distinguished token "?": Examples: ((spotter plane > sp-aw-t-r pl-ayun)) The distinguished token "?" is inteφreted as a request by the writer of the WORD form for the system to provide the pronounclarion: ((KWANJU COMPOUND > K-W-AA-N-JH-UW ?)) The word-token "COMPOUND" is a common one, and likely to be present in the HARK phonetic dictionary. We do not want to re-iterate its pronounciation here, so we use the special Symbol "?", which means variously "I don't know the pronounciation" or "I don't feel like typing it". Of course, the absence of the phonetic delimiter ">" in & spelling is equivalent to a matching number of "?"- instances for the orthographic word-tokens. Sometimes a word-token can have more than one pronounciation. In this case, its matching phonetic token is simply a list: ((TAEGU > (T-EY-G,-UW T-AY-G-UW))) Multiple pronunications can occur in collocations as well, in the obvious way. If a word has multiple spellings, they are simply listed: ((AREA SUPPORT GROUP) (AREA SUPPORT GROUPS)) Pronounciation for word-tokens present in both spellings need be indicated only once: ((AREA SUPPORT GROUP > EI-R-IY-AX S-AX-P-AO-R-T G-R-UW-P) (AREA SUPPORT GROUP > ? ? G-R-UW-P-S)) Indeed, a pronouncation can come from another WORD entry in the file altogether, or even from a different file if multiple lexicon files are used. <SEMANTICS>* is an optional slot value. It specifies the semantic meaning of the word. It is a mapping from the word to how that word gets represented in the clauses. 3 <SEMATICS> can be: • a string _ "west" 3 In VALAD, *words-to-semantics* is a hash table where <WORD> is the key and <SEMANTICS> is the value. 31 • a list (similar to SQL notation), - (EQ (NAME-OF *) "ADRG-JNC") • a list of lists consed by an AND or OR - (OR (LIKE (NOMENCLATURE *) *%CGO%") (LIKE (NOMENCLATURE*) "%CARGO%"))) 1.4 RTN Network & Lisp Code The RTN network contains a compressed network of expansions for a single LHS label . At compile time a compilation program produces this network from the complete database of s-rules. For example, suppose the following are s-rules for an LHS label: • lad/utt -> (show/command-clause (:OPT theater/pp) • lad/utt-> (display/when-clause) • lad/utt -> (show/command-clause (:opt (:bigram-or show/vp/postmod)) The resulting compressed network is lad/utt -> (:OR (show/command-clause (:OPT theater/pp (:bigram-or slow/vp/postmod))) (display/when-clause)) At this stage, & lisp function called <LABEL>-PARSE is generated and compiled into the system. Its function is to parse the <LHS> label part of the RTN network. Its arguments are a list of words to be parsed and what it returns is the constituents, words that were not used during the parse and alternative parse paths.

Claims: 32 Claims 1. An improved method for retrieving data from files in a relational database, including a system having a grammar and a lexicon, comprising the steps of: accepting key words from a user, selecting phrases incoφorating those key words, said phrases being generated using said lexicon and grammar, and outputting the phrases for the user to review 2. The method as defined in claim 1 further comprising: accepting speech inputs from said user. 3. The method as defined in claim 1 further comprising the steps of: reviewing the phrases, selecting one or more of said phrases forming a group of phrases, inputting said group of phrases back into said system as queries, attaching meanings to said queries, and outputting information to the user in accordance with said meanings. 4. The method as defined in claim 3, further comprising the steps of: inputting new key words, in response thereto, using said lexicon and grammar, automatically generating new phrases using said new key words. 5. Apparatus for retrieving data from files in a relational database, including a system having a grammar and a lexicon comprising: means for accepting key words from a user , means for selecting phrases incoφorating those key words, said phrases being generated by using said lexicon and grammar, means for outputting the phrases for the user to review, and if no phrases exist, means for inputting new key words, in response thereto, means for automatically generating new phrases containing said new key words by using said lexicon and grammar. 33 6. A computer-readable medium having stored thereon sequences of instructions which, when executed by a processing system, cause the processing system to perform the steps of accepting key words from a user , selecting phrases incoφorating those key words, said phrases being generated by said lexicon and grammar, outputting the phrases for the user to review. 7. The computer-readable medium as defined in claim 6, wherein the processing system performs the additional step of: reviewing the phrases, selecting one or more of said phrases forming a group of phrases, inputting said group back into said system as queries, attaching meanings to said queries, and outputting information to the user in accordance with said meanings. 8. A computer system for retrieving data from files in a relational database by voice comprising: a computer system constructed with an accessible, stored database of files, wherein the content of said files are related to each other, microphone arranged for accepting a spoken query, means for digitizing the query, a speech recognition module, means for inputting the digitized query into the speech recognition module, wherein said speech recognition module determines if the meaning of the digitized query can be ascertained or not, if the meaning can be ascertained, then means for providing information in accordance with said meaning, and if the meaning cannot be ascertained, then means for messaging the user that the meaning cannot be ascertained, in response to said messaging, means for accepting and key word inputs from the user, 34 means for transferring said key word inputs into said speech recognition module, wherein said speech recognition module determines if the meaning of the key word inputs can be ascertained, and in response thereto, means for attaching meanings to said phrases, and means for outputting information in accordance with said meanings. 9. The apparatus as defined in claim 8 further comprising: means for formulating new queries based on the phrases, and means for inputting these new queries, attaching second meanings to these new queries, means for outputting information in accordance with said second meanings, 10. The apparatus as defined in claim 8 further comprising: means for reviewing the phrases, means for selecting a group of said phrases, inputting said group back into said system, attaching second meanings to said phrases, and means for outputting information in accordance with said second meanings.

Inventors: BATES MADELEINE, BOBROW ROBERT J, COLBATH SEAN C, PHAM THUY T, BATES, MADELEINE, BOBROW, ROBERT, J., COLBATH, SEAN, C., PHAM, THUY, T.
Applicants: GTE INTERNETWORKING INC, GTE INTERNETWORKING INCORPORATED
Application year: 1999
Main classification: G06F17
IPC codes: G06F9, G06F17, G10L15