Improving name tagging by reference resolution and relation detection

Published 1 January 2005Open access

Heng Ji, Ralph Grishman

DOI10.3115/1219840.1219891

Citations36

TL;DR

This work uses an N-best approach to generate multiple hypotheses and have them re-ranked by subsequent stages of processing to reduce the errors produced by a Chinese name tagger.

Abstract

Information extraction systems incorporate multiple stages of linguistic analysis. Although errors are typically compounded from stage to stage, it is possible to reduce the errors in one stage by harnessing the results of the other stages. We demonstrate this by using the results of coreference analysis and relation extraction to reduce the errors produced by a Chinese name tagger. We use an N-best approach to generate multiple hypotheses and have them re-ranked by subsequent stages of processing. We obtained thereby a reduction of 24% in spurious and incorrect name tags, and a reduction of 14% in missed tags.

Keywords

Computer Science

Nymble

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Lecture notes in computer scienceActive Hidden Markov Models for Information Extraction

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