Abstract
Background: Text-mining can assist biomedical researchers in reducing information overload by extracting useful knowledge from large collections of text. We developed a novel text-mining method based on analyzing the network structure created by symbol co-occurrences as a way to extend the capabilities of knowledge extraction. The method was applied to the task of automatic gene and protein name synonym extraction. Results: Performance was measured on a test set consisting of about 50,000 abstracts from one year of MEDLINE. Synonyms retrieved from curated genomics databases were used as a gold standard. The system obtained a maximum F-score of 22.21% (23.18% precision and 21.36% recall), with high efficiency in the use of seed pairs. Conclusion: The method performs comparably with other studied methods, does not rely on sophisticated named-entity recognition, and requires little initial seed knowledge.
Original language | English (US) |
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Article number | 103 |
Journal | BMC bioinformatics |
Volume | 6 |
DOIs | |
State | Published - Apr 22 2005 |
ASJC Scopus subject areas
- Structural Biology
- Biochemistry
- Molecular Biology
- Computer Science Applications
- Applied Mathematics