Methods and systems for natural language understanding using human knowledge and collected data
First Claim
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1. A method of natural language understanding, comprising:
- developing a statistical model for a natural language understanding application using human knowledge exclusive of any data that is collected during execution of said application; and
during execution of said application receiving a sequence of words and assigning a sequence of tags to said received sequence of words by using said developed model.
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Abstract
Disclosed herein are systems and methods to incorporate human knowledge when developing and using statistical models for natural language understanding. The disclosed systems and methods embrace a data-driven approach to natural language understanding which progresses seamlessly along the continuum of availability of annotated collected data, from when there is no available annotated collected data to when there is any amount of annotated collected data.
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Citations
20 Claims
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1. A method of natural language understanding, comprising:
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developing a statistical model for a natural language understanding application using human knowledge exclusive of any data that is collected during execution of said application; and
during execution of said application receiving a sequence of words and assigning a sequence of tags to said received sequence of words by using said developed model. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A system for natural language understanding, comprising:
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means for receiving sequences of words;
means for developing a statistical model for natural language understanding using human knowledge and optionally using data previously received by said receiving means and subsequently annotated; and
means, using said developed statistical model, for assigning sequences of tags to sequences of words received by said receiving means. - View Dependent Claims (8, 9, 10, 11, 12, 13, 14, 15, 16)
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17. A system for natural language understanding, comprising:
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a language model building tool configured to use tag-related phrases to build at least one n-gram language model, wherein said phrases are obtained from at least one selected from a group consisting of;
human knowledge and annotated collected data;
a statistical classifier training tool configured to train a classifier model using a body of annotated collected data to model the dependency of a tag for a word on at least one feature of said word and on at least one tag of at least one previous word; and
a model executor configured in run time to output a sequence of tags for an inputted sequence of words by using said statistical classifier model and said at least one language model in accordance with predetermined proportions. - View Dependent Claims (18, 19, 20)
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Specification