Method and system for creating a named entity language model
AssigneeNone recorded
InventorsAllen Louis Gorin, Frederic Bechet, Jeremy Huntley Wright, Dilek Z. Hakkani-Tur
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AssigneeNone recorded
InventorsAllen Louis Gorin, Frederic Bechet, Jeremy Huntley Wright, Dilek Z. Hakkani-Tur
Estimating the term…
The invention concerns a method and system for creating a named entity language model. The method may include recognizing input communications from a training corpus, parsing the training corpus, tagging the parsed training corpus, aligning the recognized training corpus with the tagged training corpus, and creating a named entity language model from the aligned corpus.
A method of creating a named entity language model, comprising:
A system that creates a named entity language model, comprising:
A method of creating a named entity language model, comprising:
Dependencies are inferred from the claim text.
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Phrasal analysis, e.g. finite state techniques or chunking: named entity recognition
Grammatical context, e.g. disambiguation of the recognition hypotheses based on word sequence rules: formal grammars, e.g. finite state automata,…
Speech classification or search: word spotting
Computing and data processing
Term end not estimated
The invention relates to automated systems for communication recognition and understanding.
Examples of conventional automated dialogue systems can be found in U.S. Pat. Nos. 5,675,707, 5,860,063, 6,021,384, 6,044,337, 6,173,261 and 6,192,110, which are incorporated herein by reference in their entireties. Interactive spoken dialogue systems are now employed in a wide range of applications, such as directory assistance or customer care on a very large scale. Dealing with a large population of non-expert users results in a great variability in the spontaneous communications being processed. This variability requires a very high robustness from every part of dialogue system.
In addition, the large amount of real data collected through these dialogue systems raises many new dialogue system training issues and makes possible the use of even more automatic learning and corpus-based methods at each step of the dialogue process. One of the issues that these developments have raised is the role of the dialogue system in determining, firstly what kind of query the database is going to be asked and secondly with which parameters.
The invention is described in detail with reference to the following drawings wherein like numerals reference like elements, and wherein:
FIG. 1 is a block diagram of an exemplary communication recognition and understanding system;
FIG. 2 is a detailed block diagram of an exemplary named entity training unit;
A dialogue system can be viewed as an interface between a user and a database. The role of the system is to determine first, what kind of query the database is going to be asked, and second, with which parameters. For example, in the How May I Help You?SM, TM (HMIHY) customer care corpus, if a user wants his or her account balance, the query concerns accessing the account balance field of the database with the customer identification number as the parameter.
Such database queries are denoted by task-type (or in this example, call-type) and their parameters are the information items that are contained in the user's request. They are often called “named entities”. The most general definition of a named entity is a sequence of words, symbols, etc. that refers to a unique identifier. For example, a named entity may refer to:
proper name identifiers, like organization, person or location names;