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Tata Consultancy Services Linguistic Data Consortium for Indian Languages (LDC-IL)
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In: http://www.aclweb.org/anthology/W/W12/W12-60.pdf (2012)
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Learning to rank for quantity consensus queries
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In: http://www.cc.gatech.edu/~zha/CSE8801/learning-to-rank/p243-banerjee.pdf (2009)
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Passage scoring for question answering via bayesian inference on lexical relations
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In: http://trec.nist.gov/pubs/trec12/papers/iit.qa.pdf (2004)
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P.: A gloss-centered algorithm for disambiguation
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In: http://www.cse.iitb.ac.in/~ganesh/papers/acl2004.pdf (2004)
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Is Question Answering an Acquired Skill?
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In: http://http.cs.berkeley.edu/~soumen/doc/www2004/p179-chakrabarti.pdf (2004)
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Passage Scoring for Question answering via Bayesian inference on lexical
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In: http://trec.nist.gov/pubs/trec12/./papers/iit.qa.ps (2004)
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Is Question Answering an Acquired Skill?
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In: http://www.www2004.org/proceedings/docs/1p111.pdf (2004)
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Question answering via Bayesian inference on lexical relations
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In: http://acl.ldc.upenn.edu/W/W03/W03-1201.pdf (2003)
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| 9 |
Question answering via bayesian inference on lexical relations
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In: http://acl.ldc.upenn.edu/acl2003/mlsum/pdfs/Ramakrishnan.pdf (2003)
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| 10 |
IIT Bombay
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In: http://www.cse.iitb.ac.in/~soumen/doc/www2004/p179-chakrabarti.pdf
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Abstract:
We present a question answering (QA) system which learns how to detect and rank answer passages by analyzing questions and their answers (QA pairs) provided as training data. We built our system in only a few person-months using offthe-shelf components: a part-of-speech tagger, a shallow parser, a lexical network, and a few well-known supervised learning algorithms. In contrast, many of the top TREC QA systems are large group efforts, using customized ontologies, question classifiers, and highly tuned ranking functions. Our ease of deployment arises from using generic, trainable algorithms that exploit simple feature extractors on QA pairs. With TREC QA data, our system achieves mean reciprocal rank (MRR) that compares favorably with the best scores in recent years, and generalizes from one corpus to another. Our key technique is to recover, from the question, fragments of what might have been posed as a structured query, had a suitable schema been available. One fragment comprises selectors: tokens that are likely to appear (almost) unchanged in an answer passage. The other fragment contains question tokens which give clues about the answer type, and are expected to be replaced in the answer passage by tokens which specialize or instantiate the desired answer type. Selectors are like constants in where-clauses in relational queries, and answer types are like column names. We present new algorithms for locating selectors and answer type clues and using them in scoring passages with respect to a question.
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Keyword:
Algorithms; Experimentation. Keywords; General terms; machine learning; Question answering
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URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.117.6300
http://www.cse.iitb.ac.in/~soumen/doc/www2004/p179-chakrabarti.pdf
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Learning to Rank for Quantity Consensus Queries
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In: http://www.cse.iitb.ac.in/~soumen/doc/sigir2009q/QCQ.pdf
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| 12 |
Word sense disambiguation using ilp
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In: http://www.dcs.shef.ac.uk/~lucia/publications/Speciaetal_ILP-2006.pdf
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| 13 |
Building Multilingual Search Index using open
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In: http://www.aclweb.org/anthology/W/W12/W12-5018.pdf
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