Paper: Extracting Regulatory Gene Expression Networks From Pubmed

ACL ID P04-1025
Title Extracting Regulatory Gene Expression Networks From Pubmed
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2004
Authors

We present an approach using syntacto- semantic rules for the extraction of rela- tional information from biomedical ab- stracts. The results show that by over- coming the hurdle of technical termi- nology, high precision results can be achieved. From abstracts related to baker’s yeast, we manage to extract a regulatory network comprised of 441 pairwise relations from 58,664 abstracts with an accuracy of 83–90%. To achieve this, we made use of a resource of gene/protein names considerably larger than those used in most other biology re- lated information extraction approaches. This list of names was included in the lexicon of our retrained part-of-speech tagger for use on molecular biology ab- stracts. For the domain in question an accuracy of 93.6–97.7% was attained on POS-tags. The...