Concept Recognition, Information Retrieval, and Machine Learning in Genomics Question-Answering Export

@inproceedings{citeulike:972770, abstract = {TREC Genomics 2006 presented a genomics question-answering challenge with questions on twenty-seven topics, and a corpus of 162,259 full-text biomedical journal articles from which to derive answers. Questions were formulated from actual information needs of biomedical researchers, and performance was based on human evaluation of the answers. The University of Colorado approach to this task involved three key components: semantic analysis, document zoning, and a promiscuous retrieval approach followed by pruning by classifiers trained to identify near-misses. We began by parsing the document HTML, splitting it into paragraph-length passages and classifying each passage with respect to a model of the sections (zones) of scientific publications. We filtered out certain sections, and built a search index for these passages using the Lemur system. Next, for each query, we semi-automatically created a set of expansions using ontological resources, including MeSH and the Gene Ontology. This expansion included not only synonyms, but terms related to concepts that were both more specific and (in some cases) more general than the query. We searched the passage collection for these expanded queries using the Indri search engine from the Lemur package, with pseudo-relevance feedback. We also tried expanding the retrieved passages by adding passages that had a small cosine distance to the initial retrievals in an LSA-defined vector space. Our final step was to filter this expanded retrieval set with document classifiers whose input features included word stems and recognized concepts. Three separate runs were constructed using varying components of the above set, allowing us to explore the utility of each. The system produced the best result for at least one query in each of the three evaluations (document, passage and aspect diversity).}, author = {Caporaso, Gregory J. and Baumgartner, William A. and Kim, Hyunmin and Lu, Zhiyong and Johnson, Helen L. and Medvedeva, Olga and Lindemann, Anna and Fox, Lynne M. and White, Elizabeth K. and Cohen, Bretonnel K. and Hunter, Lawrence}, booktitle = {TREC Notebook}, citeulike-article-id = {972770}, institution = {NIST}, keywords = {biomedical, data-mining, hunter-lab, nlp}, posted-at = {2006-12-04 05:00:33}, priority = {2}, title = {Concept Recognition, Information Retrieval, and Machine Learning in Genomics Question-Answering}, year = {2006} }

TY - CONF ID - citeulike:972770 L3 - citeulike-article-id:972770 N2 - TREC Genomics 2006 presented a genomics question-answering challenge with questions on twenty-seven topics, and a corpus of 162,259 full-text biomedical journal articles from which to derive answers. Questions were formulated from actual information needs of biomedical researchers, and performance was based on human evaluation of the answers. The University of Colorado approach to this task involved three key components: semantic analysis, document zoning, and a promiscuous retrieval approach followed by pruning by classifiers trained to identify near-misses. We began by parsing the document HTML, splitting it into paragraph-length passages and classifying each passage with respect to a model of the sections (zones) of scientific publications. We filtered out certain sections, and built a search index for these passages using the Lemur system. Next, for each query, we semi-automatically created a set of expansions using ontological resources, including MeSH and the Gene Ontology. This expansion included not only synonyms, but terms related to concepts that were both more specific and (in some cases) more general than the query. We searched the passage collection for these expanded queries using the Indri search engine from the Lemur package, with pseudo-relevance feedback. We also tried expanding the retrieved passages by adding passages that had a small cosine distance to the initial retrievals in an LSA-defined vector space. Our final step was to filter this expanded retrieval set with document classifiers whose input features included word stems and recognized concepts. Three separate runs were constructed using varying components of the above set, allowing us to explore the utility of each. The system produced the best result for at least one query in each of the three evaluations (document, passage and aspect diversity). TI - Concept Recognition, Information Retrieval, and Machine Learning in Genomics Question-Answering T2 - TREC Notebook KW - biomedical KW - data-mining KW - hunter-lab KW - nlp AU - Caporaso, Gregory AU - Baumgartner, William AU - Kim, Hyunmin AU - Lu, Zhiyong AU - Johnson, Helen AU - Medvedeva, Olga AU - Lindemann, Anna AU - Fox, Lynne AU - White, Elizabeth AU - Cohen, Bretonnel AU - Hunter, Lawrence PY - 2006/// ER -