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EVENT EXTRACTION FROM BIOMEDICAL PAPERS USING A FULL PARSER

Yakushiji, Akane, Tateisi, Yuka, Miyao, Yusuke, Tsujii, Jun-ichi·2000·Biocomputing 2001

Abstract

We have designed and implemented an information extraction system using a full parser to investigate the plausibility of full analysis of text using general-purpose parser and grammar applied to biomedical domain. We partially solved the problems of full parsing of inefficiency, ambiguity, and low coverage by introducing the preprocessors, and proposed the use of modules that handles partial results of parsing for further improvement. Our approach makes it possible to modularize the system, so that the IE system as a whole becomes easy to be tuned to specific domains, and easy to be maintained and improved by incorporating various techniques of disambiguation, speed up, etc. In preliminary experiment, from 133 argument structures that should be extracted from 97 sentences, we obtained 23% uniquely and 24% with ambiguity. And 20% are extractable from not complete but partial results of full parsing.

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Metadata

Authors
Yakushiji, A., Tateisi, Y., Miyao, Y., & Tsujii, J.-I.
Venue
Biocomputing 2001
Published
2000-12
Pages
408-419
OpenAlex
W1999117234
Cited by
224
References
0
FWCI
35.14
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: EVENT EXTRACTION FROM BIOMEDICAL PAPERS USING A FULL PARSER. https://app.cochat.ai/papers/10.1142/9789814447362_0040
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Yakushiji, A., Tateisi, Y., Miyao, Y., & Tsujii, J.-I. (2000). EVENT EXTRACTION FROM BIOMEDICAL PAPERS USING A FULL PARSER. Biocomputing 2001. https://doi.org/10.1142/9789814447362_0040

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.