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Grammatical Inference: Introduction and Survey-Part II.
IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
Summary
This study explores inferring high-dimensional grammars, including tree and stochastic grammars. Techniques are presented for modeling information sources with stochastic finite-state and context-free grammars.
Area of Science:
- Computer Science
- Computational Linguistics
- Machine Learning
Background:
- High-dimensional grammars are complex structures used in various fields.
- Inferring grammars from data is crucial for understanding underlying processes.
- Stochastic grammars are essential for modeling probabilistic information sources.
Purpose of the Study:
- To discuss techniques for inferring high-dimensional grammars.
- To present methods for inferring tree grammars.
- To introduce the inference of stochastic grammars for information source modeling.
Main Methods:
- Brief presentation of techniques for inferring tree grammars.
- Introduction to inferring stochastic grammars.
- Presentation of inference techniques for stochastic finite-state and context-free grammars.
Main Results:
- Techniques for inferring tree grammars are outlined.
- Methods for inferring stochastic grammars are presented.
- The inference process for specific classes of stochastic grammars is detailed.
Conclusions:
- The study provides methods for inferring complex grammatical structures.
- Inference techniques are applicable to stochastic finite-state and context-free grammars.
- Practical applications of grammar inference are illustrated through examples.
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