Reducing parsing complexity by intra-sentence segmentation based on maximum entropy model

Authors:
Sung Dong Kim;Byoung-Tak Zhang;Yung Taek Kim
Affiliations:
Seoul National University, Korea;Seoul National University, Korea;Seoul National University, Korea
Venue:
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
Year:
2000

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Abstract

Long sentence analysis has been a critical problem because of high complexity. This paper addresses the reduction of parsing complexity by intra-sentence segmentation, and presents maximum entropy model for determining segmentation positions. The model features lexical contexts of segmentation positions, giving a probability to each potential position. Segmentation coverage and accuracy of the proposed method are 96% and 88% respectively. The parsing efficiency is improved by 77% in time and 71% in space.