A Maximum-Entropy Partial Parser for Unrestricted Text

TL;DR

This paper introduces a maximum-entropy-based partial parser capable of recognizing complex syntactic structures in unrestricted text by combining various knowledge sources, demonstrating promising accuracy in different applications.

Contribution

It presents a novel partial parsing approach using maximum entropy estimation to integrate hierarchical, part-of-speech, and phrasal information.

Findings

Achieves high accuracy in syntactic recognition

Effectively combines multiple knowledge sources

Handles complex structures beyond simple bracketing

Abstract

This paper describes a partial parser that assigns syntactic structures to sequences of part-of-speech tags. The program uses the maximum entropy parameter estimation method, which allows a flexible combination of different knowledge sources: the hierarchical structure, parts of speech and phrasal categories. In effect, the parser goes beyond simple bracketing and recognises even fairly complex structures. We give accuracy figures for different applications of the parser.