Automating curation using a natural language processing pipeline

被引：0

作者：

Alex B. ^{[1
]}

Grover C. ^{[1
]}

Haddow B. ^{[1
]}

Kabadjov M. ^{[1
]}

Klein E. ^{[1
]}

Matthews M. ^{[1
]}

Tobin R. ^{[1
]}

Wang X. ^{[1
]}

机构：

[1] School of Informatics, University of Edinburgh, Edinburgh EH8 9AB

来源：

Genome Biology | / 9卷 / Suppl 2期

关键词：

Natural Language Processing; Name Entity Recognition; Relation Extraction; Gene Mention; Gene Lexicon;

D O I：

10.1186/gb-2008-9-s2-s10

中图分类号：

学科分类号：

摘要：

Background: The tasks in BioCreative II were designed to approximate some of the laborious work involved in curating biomedical research papers. The approach to these tasks taken by the University of Edinburgh team was to adapt and extend the existing natural language processing (NLP) system that we have developed as part of a commercial curation assistant. Although this paper concentrates on using NLP to assist with curation, the system can be equally employed to extract types of information from the literature that is immediately relevant to biologists in general. Results: Our system was among the highest performing on the interaction subtasks, and competitive performance on the gene mention task was achieved with minimal development effort. For the gene normalization task, a string matching technique that can be quickly applied to new domains was shown to perform close to average. Conclusion: The technologies being developed were shown to be readily adapted to the BioCreative II tasks. Although high performance may be obtained on individual tasks such as gene mention recognition and normalization, and document classification, tasks in which a number of components must be combined, such as detection and normalization of interacting protein pairs, are still challenging for NLP systems. © 2008 Alex et al; licensee BioMed Central Ltd.

引用

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