Full-text processing: improving a practical NLP system based on surface information within the context

Authors:
Tetsuya Nasukawa
Affiliations:
IBM Research, Tokyo Research Laboratory, Kanagawa-ken, Japan
Venue:
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
Year:
1996

Citing 7
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Automatic Extraction of Rules for AnaphoraResolution of Japanese Zero Pronouns in Japanese–English Machine Translation from Aligned Sentence Pairs

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Automatic extraction of rules for anaphora resolution of Japanese zero pronouns from aligned sentence pairs

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An environment for extracting resolution rules of zero pronouns from corpora

Proceedings of the COLING-2000 Workshop on Semantic Annotation and Intelligent Content

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Abstract

Rich information for resolving ambiguities in sentence analysis, including various context-dependent problems, can be obtained by analyzing a simple set of parsed trees of each sentence in a text without constructing a precise model of the context through deep semantic analysis. Thus, processing a group of sentences together makes it possible to improve the accuracy of a practical natural language processing (NLP) system such as a machine translation system. In this paper, we describe a simple context model consisting of parsed trees of each sentence in a text, and its effectiveness for handling various problems in NLP such as the resolution of structural ambiguities, pronoun referents, and the focus of focusing subjects (e.g. also and only), as well as for adding supplementary phrases to some elliptical sentences.