Answer type identification for question answering: Supervised learning of dependency graph patterns from natural language questions

Andrew D. Walker; Panos Alexopoulos; Andrew Starkey; Jeff Z. Pan; José Manuel Gómez-Pérez; Advaith Siddharthan

doi:10.1007/978-3-319-31676-5_17

Answer type identification for question answering: Supervised learning of dependency graph patterns from natural language questions

Andrew D. Walker^*, Panos Alexopoulos, Andrew Starkey, Jeff Z. Pan, José Manuel Gómez-Pérez, Advaith Siddharthan

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

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	https://doi.org/10.1007/978-3-319-31676-5_17
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)
Volume	9544
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	5th Joint International Conference on Semantic Technology, JIST 2015
Country	China
City	Yichang
Period	11/11/15 → 13/11/15

Access to Document

10.1007/978-3-319-31676-5_17Licence: Unspecified

Link to publication in Scopus

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Andrew Starkey
- Engineering, Engineering - Senior Lecturer
- Engineering (Research Theme)
- Centre for Applied Dynamics Research (CADR)
Person: Academic

Cite this

Walker, A. D., Alexopoulos, P., Starkey, A., Pan, J. Z., Gómez-Pérez, J. M., & Siddharthan, A. (2016). Answer type identification for question answering: Supervised learning of dependency graph patterns from natural language questions. In G. Qi, K. Kozaki, J. Z. Pan, & S. Yu (Eds.), Semantic Technology: 5th Joint International Conference, JIST 2015, Yichang, China, November 11-13, 2015, Revised Selected Papers (Vol. 9544, pp. 235-251). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9544). Springer-Verlag. https://doi.org/10.1007/978-3-319-31676-5_17

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

Walker, AD, Alexopoulos, P, Starkey, A, Pan, JZ, Gómez-Pérez, JM & Siddharthan, A 2016, Answer type identification for question answering: Supervised learning of dependency graph patterns from natural language questions. in G Qi, K Kozaki, JZ Pan & S Yu (eds), Semantic Technology: 5th Joint International Conference, JIST 2015, Yichang, China, November 11-13, 2015, Revised Selected Papers. vol. 9544, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9544, Springer-Verlag, pp. 235-251, 5th Joint International Conference on Semantic Technology, JIST 2015, Yichang, China, 11/11/15. https://doi.org/10.1007/978-3-319-31676-5_17

Walker AD, Alexopoulos P, Starkey A, Pan JZ, Gómez-Pérez JM, Siddharthan A. Answer type identification for question answering: Supervised learning of dependency graph patterns from natural language questions. In Qi G, Kozaki K, Pan JZ, Yu S, editors, Semantic Technology: 5th Joint International Conference, JIST 2015, Yichang, China, November 11-13, 2015, Revised Selected Papers. 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)). https://doi.org/10.1007/978-3-319-31676-5_17

Walker, Andrew D. ; Alexopoulos, Panos ; Starkey, Andrew ; Pan, Jeff Z. ; Gómez-Pérez, José Manuel ; Siddharthan, Advaith. / Answer type identification for question answering : Supervised learning of dependency graph patterns from natural language questions. Semantic Technology: 5th Joint International Conference, JIST 2015, Yichang, China, November 11-13, 2015, Revised Selected Papers. editor / Guilin Qi ; Kouji Kozaki ; Jeff Z Pan ; Siwei Yu. Vol. 9544 Springer-Verlag, 2016. pp. 235-251 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

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