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Construction of natural language sentence acceptors by a supervised-learning technique
1Centre de Recherche en Informatique de Nancy, Ecole des Mines (INPL), Nancy, France.
IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
Summary
This study develops an automatic natural language sentence acceptor using machine learning. It defines a learning criterion to measure the quality of acceptors compatible with given sentence samples.
Area of Science:
- Computer Science
- Artificial Intelligence
- Computational Linguistics
Background:
- Automated processing of natural language sentences is crucial for applications like computer-assisted instruction and database interrogation.
- Existing methods for building sentence acceptors often require extensive manual input or lack robust learning capabilities.
Purpose of the Study:
- To automatically construct an acceptor for natural language sentences based on provided examples.
- To define a quantifiable learning criterion for evaluating the performance of these acceptors.
Main Methods:
- The study focuses on defining a learning criterion as a quality measure.
- This criterion is applied to a set of acceptors that are compatible with a given sample of sentences.
Main Results:
- A method for automatically generating sentence acceptors has been proposed.
- A novel learning criterion has been defined to assess the quality of these acceptors.
Conclusions:
- The defined learning criterion provides a framework for selecting optimal acceptors in natural language processing tasks.
- This approach facilitates the development of more efficient computer-assisted instruction and database interrogation systems.
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