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 |
|---|---|
| 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 - 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) |
|---|---|
| Volume | 9544 |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Conference
| Conference | 5th Joint International Conference on Semantic Technology, JIST 2015 |
|---|---|
| Country | China |
| City | Yichang |
| Period | 11/11/15 → 13/11/15 |
Fingerprint
ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
Cite this
Answer type identification for question answering : Supervised learning of dependency graph patterns from natural language questions. / Walker, Andrew D.; Alexopoulos, Panos; Starkey, Andrew; Pan, Jeff Z.; Gómez-Pérez, José Manuel; Siddharthan, Advaith.
Semantic Technology: 5th Joint International Conference, JIST 2015, Yichang, China, November 11-13, 2015, Revised Selected Papers. ed. / Guilin Qi; Kouji Kozaki; Jeff Z Pan; Siwei Yu. Vol. 9544 Springer-Verlag, 2016. p. 235-251 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9544).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Answer type identification for question answering
T2 - Supervised learning of dependency graph patterns from natural language questions
AU - Walker, Andrew D.
AU - Alexopoulos, Panos
AU - Starkey, Andrew
AU - Pan, Jeff Z.
AU - Gómez-Pérez, José Manuel
AU - Siddharthan, Advaith
N1 - Acknowledgement This 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).
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84961675218&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-31676-5_17
DO - 10.1007/978-3-319-31676-5_17
M3 - Conference contribution
SN - 978-3-319-31675-8
VL - 9544
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 235
EP - 251
BT - Semantic Technology
A2 - Qi, Guilin
A2 - Kozaki, Kouji
A2 - Pan, Jeff Z
A2 - Yu, Siwei
PB - Springer-Verlag
ER -