%0 Conference Paper %1 QAst2008/PardinoEtAl %A Pardiño, M. %A Gómez, J.M. %A Llorens, H. %A Muñoz-Terol, R. %A Navarro-Colorado, B. %A Saquete, E. %A Martinez-Barco, P. %A Moreda, P. %A Palomar, M. %D 2008 %K QAst %T Adapting IBQAS to work with text transciptions in QAst Task: IBQAst %U http://www.clef-campaign.org/2008/working_notes/pardi%C3%B1o-paperCLEF2008.pdf %X This paper shows the results of adapting a modular domain English QA system (called IBQAS, whose initials correspond to Interchangeable Blocks Question Answering System) to work with both manual and automatic text transcriptions. This system provides a generic and modular framework using an approach based on the recognition of named entities as a method of extracting answers. The system architecture follows the general methodology of QA systems incorporating the modules detailed below: analysis of the question, information retrieval and extraction of the answer. In the analysis phase of the system, we extracted the type of question or type of answer expected, keywords and focus. Next, we used JIRS, a traditional Passage Retrieval system which is able to find structures in questions using n-gram models, for the information retrieval process. Finally, we selected the potential answers and those with higher scores were given as result.

@inproceedings{QAst2008/PardinoEtAl, abstract = {This paper shows the results of adapting a modular domain English QA system (called IBQAS, whose initials correspond to Interchangeable Blocks Question Answering System) to work with both manual and automatic text transcriptions. This system provides a generic and modular framework using an approach based on the recognition of named entities as a method of extracting answers. The system architecture follows the general methodology of QA systems incorporating the modules detailed below: analysis of the question, information retrieval and extraction of the answer. In the analysis phase of the system, we extracted the type of question or type of answer expected, keywords and focus. Next, we used JIRS, a traditional Passage Retrieval system which is able to find structures in questions using n-gram models, for the information retrieval process. Finally, we selected the potential answers and those with higher scores were given as result. }, added-at = {2009-03-03T16:05:21.000+0100}, author = {Pardiño, M. and Gómez, J.M. and Llorens, H. and Muñoz-Terol, R. and Navarro-Colorado, B. and Saquete, E. and Martinez-Barco, P. and Moreda, P. and Palomar, M.}, biburl = {https://www.bibsonomy.org/bibtex/2a355bc1750787c7616b63ec4f666da97/epsilon}, interhash = {b2c2762d5fad449bdb1f0e167d0fdf7e}, intrahash = {a355bc1750787c7616b63ec4f666da97}, keywords = {QAst}, timestamp = {2009-03-03T16:05:21.000+0100}, title = {Adapting IBQAS to work with text transciptions in QAst Task: IBQAst}, url = {http://www.clef-campaign.org/2008/working_notes/pardi%C3%B1o-paperCLEF2008.pdf}, year = 2008 }