This article is part of the supplement: The Third BioCreative Critical Assessment of Information Extraction in Biology Challenge
Soft tagging of overlapping high confidence gene mention variants for cross-species full-text gene normalization
1 Institute of Information Science, Academia Sinica, Taipei 115, Taiwan
2 School of Chemical and Life Sciences, Singapore Polytechnic, Republic of Singapore
3 Department of Zoology, The University of Melbourne, Parkville, Victoria, Australia
4 Information Sciences Institute, University of Southern California, Marina del Rey, California, USA
BMC Bioinformatics 2011, 12(Suppl 8):S6 doi:10.1186/1471-2105-12-S8-S6Published: 3 October 2011
Previously, gene normalization (GN) systems are mostly focused on disambiguation using contextual information. An effective gene mention tagger is deemed unnecessary because the subsequent steps will filter out false positives and high recall is sufficient. However, unlike similar tasks in the past BioCreative challenges, the BioCreative III GN task is particularly challenging because it is not species-specific. Required to process full-length articles, an ineffective gene mention tagger may produce a huge number of ambiguous false positives that overwhelm subsequent filtering steps while still missing many true positives.
We present our GN system participated in the BioCreative III GN task. Our system applies a typical 2-stage approach to GN but features a soft tagging gene mention tagger that generates a set of overlapping gene mention variants with a nearly perfect recall. The overlapping gene mention variants increase the chance of precise match in the dictionary and alleviate the need of disambiguation. Our GN system achieved a precision of 0.9 (F-score 0.63) on the BioCreative III GN test corpus with the silver annotation of 507 articles. Its TAP-k scores are competitive to the best results among all participants.
We show that despite the lack of clever disambiguation in our gene normalization system, effective soft tagging of gene mention variants can indeed contribute to performance in cross-species and full-text gene normalization.