COMET - Conditional Maximum Entropy Estimation from Truncated Data

A Machine Learning Tool for Statistical Disambiguation in Constraint-Based Parsing


Research Goal

Highly precise disambiguation of analyses is a key problem and prerequiste for real-world applications for broad-coverage parsing. The goal of the COMET project is to apply statistical machine learning techniques to induce disambiguation routines for broad-coverage constraint-based parsers automatically from data. The grammar and parsing tools used in this project are developed in the Pargram project for the XLE parsing system. Current work on statistical estimation and disambigation can be summarized under the name COMET - Conditional Maximum-Entropy Estimation from Truncated Data.

Estimation and Disambiguation Tools

Depending on the availability of data (fully labeled, partially labeled, unlabeled) and the complexity of the ambiguity space of the grammars different estimators have been invented, implemented, and evaluated.
For more information and references, visit the NLTT page.
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