The value of parsing as feature generation for gene mention recognition Export

@article{citeulike:4570176, abstract = {We measured the extent to which information surrounding a base noun phrase reflects the presence of a gene name, and evaluated seven different parsers in their ability to provide information for that purpose. Using the GENETAG corpus as a gold standard, we performed machine learning to recognize from its context when a base noun phrase contained a gene name. Starting with the best lexical features, we assessed the gain of adding dependency or dependency-like relations from a full sentence parse. Features derived from parsers improved performance in this partial gene mention recognition task by a small but statistically significant amount. There were virtually no differences between parsers in these experiments.}, author = {Smith, Larry H. and Wilbur, John W.}, citeulike-article-id = {4570176}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.jbi.2009.03.011}, citeulike-linkout-1 = {http://linkinghub.elsevier.com/retrieve/pii/S1532046409000501}, day = {02}, doi = {10.1016/j.jbi.2009.03.011}, issn = {15320464}, journal = {Journal of Biomedical Informatics}, keywords = {bikel-parser, break-even-point, charniak-lease-parser, charniak-parser, dependency-relations, enju, evaluation, extrinsic-evaluation, gene-mention, genetag, genetag-corpus, intrinsic-evaluation, lexical-features, machine-learning, minipar, named-entity-recognition, parsing, rasp, stanford-parser, svm, syntactic-features}, month = {April}, posted-at = {2009-06-09 14:58:16}, priority = {0}, title = {The value of parsing as feature generation for gene mention recognition}, url = {http://dx.doi.org/10.1016/j.jbi.2009.03.011}, year = {2009} }

TY - JOUR ID - citeulike:4570176 L3 - citeulike-article-id:4570176 N2 - We measured the extent to which information surrounding a base noun phrase reflects the presence of a gene name, and evaluated seven different parsers in their ability to provide information for that purpose. Using the GENETAG corpus as a gold standard, we performed machine learning to recognize from its context when a base noun phrase contained a gene name. Starting with the best lexical features, we assessed the gain of adding dependency or dependency-like relations from a full sentence parse. Features derived from parsers improved performance in this partial gene mention recognition task by a small but statistically significant amount. There were virtually no differences between parsers in these experiments. JF - Journal of Biomedical Informatics SN - 15320464 TI - The value of parsing as feature generation for gene mention recognition KW - bikel-parser KW - break-even-point KW - charniak-lease-parser KW - charniak-parser KW - dependency-relations KW - enju KW - evaluation KW - extrinsic-evaluation KW - gene-mention KW - genetag KW - genetag-corpus KW - intrinsic-evaluation KW - lexical-features KW - machine-learning KW - minipar KW - named-entity-recognition KW - parsing KW - rasp KW - stanford-parser KW - svm KW - syntactic-features AU - Smith, Larry AU - Wilbur, John PY - 2009/04/02/ UR - http://dx.doi.org/10.1016/j.jbi.2009.03.011 ER -