Summary of the paper
| Title | Learning the Species of Biomedical Named Entities from Annotated Corpora |
|---|---|
| Authors | Xinglong Wang and Claire Grover |
| Abstract | In biomedical articles, terms with the same surface forms are often used to refer to different entities across a number of model organisms, in which case determining the species becomes crucial to term identification systems that ground terms to specific database identifiers. This paper describes a rule-based system that extracts “species indicating words”, such as human or murine, which can be used to decide the species of the nearby entity terms, and a machine-learning species disambiguation system that was developed on manually species-annotated corpora. Performance of both systems were evaluated on gold-standard datasets, where the machine-learning system yielded better overall results. |
| Language | Single language |
| Topics | Information Extraction, Information Retrieval, Corpus (creation, annotation, etc.), Text mining |
Full paper |
Learning the Species of Biomedical Named Entities from Annotated Corpora |
Slides |
Learning the Species of Biomedical Named Entities from Annotated Corpora |
| Bibtex | @InProceedings{WANG08.460, author = {Xinglong Wang and Claire Grover}, title = {Learning the Species of Biomedical Named Entities from Annotated Corpora}, booktitle = {Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)}, year = {2008}, month = {may}, date = {28-30}, address = {Marrakech, Morocco}, editor = {Nicoletta Calzolari (Conference Chair), Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odijk, Stelios Piperidis, Daniel Tapias}, publisher = {European Language Resources Association (ELRA)}, isbn = {2-9517408-4-0}, note = {http://www.lrec-conf.org/proceedings/lrec2008/}, language = {english} } |