Doyle et al., 2016 - Google Patents
Data-driven learning of symbolic constraints for a log-linear model in a phonological settingDoyle et al., 2016
View PDF- Document ID
- 1409876547497137035
- Author
- Doyle G
- Levy R
- Publication year
- Publication venue
- Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
External Links
Snippet
We propose a non-parametric Bayesian model for learning and weighting symbolically- defined constraints to populate a log-linear model. The model jointly infers a vector of binary constraint values for each candidate output and likely definitions for these constraints …
- 239000011159 matrix material 0 description 8
Classifications
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- G06F17/2705—Parsing
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/08—Speech classification or search
- G10L15/18—Speech classification or search using natural language modelling
- G10L15/1822—Parsing for meaning understanding
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- G06F17/22—Manipulating or registering by use of codes, e.g. in sequence of text characters
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- G06F17/30946—Information retrieval; Database structures therefor; File system structures therefor details of database functions independent of the retrieved data type indexing structures
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
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- G10L15/06—Creation of reference templates; Training of speech recognition systems, e.g. adaptation to the characteristics of the speaker's voice
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