Accomplishments and challenges in literature data mining for biology.
We review recent results in literature data mining for biology and discuss the need and the steps for a challenge evaluation for this field. Literature data mining has progressed from simple recognition of terms to extraction of interaction relationships from complex sentences, and has broadened from recognition of protein interactions to a range of problems such as improving homology search, identifying cellular location, and so on. To encourage participation and accelerate progress in this expanding field, we propose creating challenge evaluations, and we describe two specific applications in this context.
Hirschman
Lynette
L
The MITRE Corporation, USA. lynette@mitre.org
Park
Jong C
JC
Tsujii
Junichi
J
Wong
Limsoon
L
Wu
Cathy H
CH
D019985:Benchmarking / Q000379:methods / Q000592:standards; D001695:Biology / Q000379:methods; D019295:Computational Biology / Q000379:methods; D003628:Database Management Systems; D016206:Databases, Bibliographic; D005069:Evaluation Studies as Topic; D016247:Information Storage and Retrieval / Q000379:methods* / Q000639:trends; D009323:Natural Language Processing; D009862:Online Systems; D011786:Quality Control