Parsing and question classification for question answering Export

@inproceedings{citeulike:6088525, abstract = {This paper describes machine learning based parsing and question classification for question answering. We demonstrate that for this type of application, parse trees have to be semantically richer and structurally more oriented towards semantics than what most treebanks offer. We empirically show how question parsing dramatically improves when augmenting a semantically enriched Penn treebank training corpus with an additional question treebank.}, address = {Morristown, NJ, USA}, author = {Hermjakob, Ulf}, booktitle = {Proceedings of the workshop on Open-domain question answering}, citeulike-article-id = {6088525}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1117856.1117859}, citeulike-linkout-1 = {http://dx.doi.org/10.3115/1117856.1117859}, comment = {Compares syntactically parsing [stanford + treebank] with semantically motivated parsing using CONTEXT. The enhanced the stanford treebank with their semantic annotation and added \~{}2.000 tagged questions from the TREC collection, sadly in english. Sadly Hermjakob is the developer of CONTEXT and potentially subjective :]. [danielp]}, doi = {10.3115/1117856.1117859}, keywords = {context-parser, en, parsing, question-answering, semantic}, location = {Toulouse, France}, pages = {1--6}, posted-at = {2009-11-09 14:47:22}, priority = {4}, publisher = {Association for Computational Linguistics}, title = {Parsing and question classification for question answering}, url = {http://dx.doi.org/10.3115/1117856.1117859}, year = {2001} }

TY - CONF ID - citeulike:6088525 L3 - citeulike-article-id:6088525 N2 - This paper describes machine learning based parsing and question classification for question answering. We demonstrate that for this type of application, parse trees have to be semantically richer and structurally more oriented towards semantics than what most treebanks offer. We empirically show how question parsing dramatically improves when augmenting a semantically enriched Penn treebank training corpus with an additional question treebank. CY - Toulouse, France AD - Morristown, NJ, USA EP - 6 TI - Parsing and question classification for question answering PB - Association for Computational Linguistics T2 - Proceedings of the workshop on Open-domain question answering SP - 1 KW - context-parser KW - en KW - parsing KW - question-answering KW - semantic N1 - Compares syntactically parsing [stanford + treebank] with semantically motivated parsing using CONTEXT. The enhanced the stanford treebank with their semantic annotation and added ~2.000 tagged questions from the TREC collection, sadly in english. Sadly Hermjakob is the developer of CONTEXT and potentially subjective :]. [danielp] AU - Hermjakob, Ulf PY - 2001/// UR - http://dx.doi.org/10.3115/1117856.1117859 ER -