Improving Question Retrieval in cQA Services Using a Dependency Parser
Summary :
The translation based language model (TRLM) is state-of-the-art method to solve the lexical gap problem of the question retrieval in the community-based question answering (cQA). Some researchers tried to find methods for solving the lexical gap and improving the TRLM. In this paper, we propose a new dependency based model (DM) for the question retrieval. We explore how to utilize the results of a dependency parser for cQA. Dependency bigrams are extracted from the dependency parser and the language model is transformed using the dependency bigrams as bigram features. As a result, we obtain the significant improved performances when TRLM and DM approaches are effectively combined.
- Publication
- IEICE TRANSACTIONS on Information Vol.E100-D No.4 pp.807-810
- Publication Date
- 2017/04/01
- Publicized
- 2017/01/17
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2016DAL0001
- Type of Manuscript
- Special Section LETTER (Special Section on Data Engineering and Information Management)
- Category
Authors
Kyoungman BAE
Dong-A University
Youngjoong KO
Dong-A University
Keyword
Latest Issue
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Kyoungman BAE, Youngjoong KO, "Improving Question Retrieval in cQA Services Using a Dependency Parser" in IEICE TRANSACTIONS on Information,
vol. E100-D, no. 4, pp. 807-810, April 2017, doi: 10.1587/transinf.2016DAL0001.
Abstract: The translation based language model (TRLM) is state-of-the-art method to solve the lexical gap problem of the question retrieval in the community-based question answering (cQA). Some researchers tried to find methods for solving the lexical gap and improving the TRLM. In this paper, we propose a new dependency based model (DM) for the question retrieval. We explore how to utilize the results of a dependency parser for cQA. Dependency bigrams are extracted from the dependency parser and the language model is transformed using the dependency bigrams as bigram features. As a result, we obtain the significant improved performances when TRLM and DM approaches are effectively combined.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2016DAL0001/_p
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@ARTICLE{e100-d_4_807,
author={Kyoungman BAE, Youngjoong KO, },
journal={IEICE TRANSACTIONS on Information},
title={Improving Question Retrieval in cQA Services Using a Dependency Parser},
year={2017},
volume={E100-D},
number={4},
pages={807-810},
abstract={The translation based language model (TRLM) is state-of-the-art method to solve the lexical gap problem of the question retrieval in the community-based question answering (cQA). Some researchers tried to find methods for solving the lexical gap and improving the TRLM. In this paper, we propose a new dependency based model (DM) for the question retrieval. We explore how to utilize the results of a dependency parser for cQA. Dependency bigrams are extracted from the dependency parser and the language model is transformed using the dependency bigrams as bigram features. As a result, we obtain the significant improved performances when TRLM and DM approaches are effectively combined.},
keywords={},
doi={10.1587/transinf.2016DAL0001},
ISSN={1745-1361},
month={April},}
Copy
TY - JOUR
TI - Improving Question Retrieval in cQA Services Using a Dependency Parser
T2 - IEICE TRANSACTIONS on Information
SP - 807
EP - 810
AU - Kyoungman BAE
AU - Youngjoong KO
PY - 2017
DO - 10.1587/transinf.2016DAL0001
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E100-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2017
AB - The translation based language model (TRLM) is state-of-the-art method to solve the lexical gap problem of the question retrieval in the community-based question answering (cQA). Some researchers tried to find methods for solving the lexical gap and improving the TRLM. In this paper, we propose a new dependency based model (DM) for the question retrieval. We explore how to utilize the results of a dependency parser for cQA. Dependency bigrams are extracted from the dependency parser and the language model is transformed using the dependency bigrams as bigram features. As a result, we obtain the significant improved performances when TRLM and DM approaches are effectively combined.
ER -
English
