Method and system for automatically building natural language understanding models
First Claim
1. A method for building a language model representation comprising the steps of:
- categorizing a natural language understanding (NLU) application;
classifying a corpus for producing a classified corpus; and
training at least one language model in view of said classified corpus, wherein said classifying partitions said corpus in view of said categorizing, and said training produces a configuration of language models based on said classification.
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Abstract
The invention disclosed herein concerns a system (100) and method (600) for building a language model representation of an NLU application. The method 500 can include categorizing an NLU application domain (602), classifying a corpus in view of the categorization (604), and training at least one language model in view of the classification (606). The categorization produces a hierarchical tree of categories, sub-categories and end targets across one or more features for interpreting one or more natural language input requests. During development of an NLU application, a developer assigns sentences of the NLU application to categories, sub-categories or end targets across one or more features for associating each sentence with desire interpretations. A language model builder (140) iteratively builds multiple language models for this sentence data, and iteratively evaluating them against a test corpus, partitioning the data based on the categorization and rebuilding models, so as to produce an optimal configuration of language models to interpret and respond to language input requests for the NLU application.
139 Citations
20 Claims
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1. A method for building a language model representation comprising the steps of:
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categorizing a natural language understanding (NLU) application;
classifying a corpus for producing a classified corpus; and
training at least one language model in view of said classified corpus, wherein said classifying partitions said corpus in view of said categorizing, and said training produces a configuration of language models based on said classification. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
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14. A natural language understanding (NLU) system comprising:
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an application categorization for categorizing an NLU application;
a classifier for classifying an NLU database corpus; and
a language model builder for creating a language model for each said partitioning. - View Dependent Claims (15, 16, 17, 18, 19)
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20. A natural language understanding (NLU) system comprising:
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an application categorization for visually categorizing an NLU application;
a classifier for classifying an NLU database corpus based on said application categorization; and
a language model builder for creating a language model for each partition of said classified NLU database corpus, wherein a language input request is processed through at least one language model configuration of said language model representation for yielding a classification result, wherein said classifier further partitions said NLU database corpus based on said application categorization in view of said classification result, wherein if no further partitioning is possible, then of the previous partitions, the model configuration that yielded the best performance accuracy on the test data is considered as the optimum model configuration.
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Specification