Abstract
Question Answering research has long recognised that the identification of the type of answer being requested is a fundamental step in the interpretation of a question as a whole. Previous strategies have ranged from trivial keyword matches, to statistical analyses, to well-defined algorithms based on shallow syntactic parses with userinteraction for ambiguity resolution. A novel strategy combining deep NLP on both syntactic and dependency parses with supervised learning is introduced and results that improve on extant alternatives reported. The impact of the strategy on QALD is also evaluated with a proprietary Question Answering system and its positive results analysed.
Original language | English |
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Title of host publication | Semantic Technology |
Subtitle of host publication | 5th Joint International Conference, JIST 2015, Yichang, China, November 11-13, 2015, Revised Selected Papers |
Editors | Guilin Qi, Kouji Kozaki, Jeff Z Pan, Siwei Yu |
Publisher | Springer-Verlag |
Pages | 235-251 |
Number of pages | 17 |
Volume | 9544 |
ISBN (Electronic) | 978-3-319-31676-5 |
ISBN (Print) | 978-3-319-31675-8 |
DOIs | |
Publication status | Published - Mar 2016 |
Event | 5th Joint International Conference on Semantic Technology, JIST 2015 - Yichang, China Duration: 11 Nov 2015 → 13 Nov 2015 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 9544 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 5th Joint International Conference on Semantic Technology, JIST 2015 |
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Country/Territory | China |
City | Yichang |
Period | 11/11/15 → 13/11/15 |
Bibliographical note
AcknowledgementThis research has been partly funded by the European Commission within the 7th Framework Programme/Marie Curie Industry-Academia Partnerships and Pathways schema/PEOPLE Work Programme 2011 project K-Drive number 286348 (cf. http://www.kdrive-project.eu).