short-paper

Learning to Re-Rank Questions in Community Question Answering Using Advanced Features

Authors:

Giovanni Da San Martino,

Alberto Barrón Cedeño,

Salvatore Romeo,

Alessandro MoschittiAuthors Info & Claims

CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

Pages 1997 - 2000

https://doi.org/10.1145/2983323.2983893

Published: 24 October 2016 Publication History

Abstract

We study the impact of different types of features for question ranking in community Question Answering: bag-of-words models (BoW), syntactic tree kernels (TKs) and rank features. It should be noted that structural kernels have never been applied to the question reranking task, i.e., question to question similarity, where they have to model paraphrase relations. Additionally, the informal text, typically present in forums, poses new challenges to the use of TKs. We compare our learning to rank (L2R) algorithms against a strong baseline given by the Google rank (GR). The results show that (i) our shallow structures used in TKs are robust enough to noisy data and (ii) improving GR requires effective BoW features and TKs along with an accurate model of GR features in the used L2R algorithm.

References

[1]

L. Allison and T. Dix. A bit-string longest-common-subsequence algorithm. Inf. Process. Lett., 23(6):305--310, Dec. 1986.

Digital Library

[2]

A. Barrón-Cedeno, G. Da San Martino, S. Joty, A. Moschitti, F. Al-Obaidli, S. Romeo, K. Tymoshenko, and A. Uva. ConvKN at SemEval-2016 Task 3: Answer and question selection for question answering on arabic and english fora. In Proceedings of SemEval '16, pages 896--903, San Diego, California, June 2016. ACL.

[3]

beginflushleftX. Cao, G. Cong, B. Cui, C. S. Jensen, and C. Zhang. The use of categorization information in language models for question retrieval. In CIKM, pages 265--274, 2009.

Digital Library

[4]

H. Duan, Y. Cao, C.-Y. Lin, and Y. Yu. Searching questions by identifying question topic and question focus. In ACL, pages 156--164, 2008.

[5]

S. Filice, D. Croce, A. Moschitti, and R. Basili. Kelp at semeval-2016 task 3: Learning semantic relations between questions and answers. In Proceedings of SemEval '16, pages 1116--1123, San Diego, California, June 2016. ACL.

[6]

M. Franco-Salvador, S. Kar, T. Solorio, and P. Rosso. UH-PRHLT at SemEval-2016 Task 3: Combining lexical and semantic-based features for community question answering. In Proceedings of SemEval '16, pages 814--821, San Diego, California, June 2016. ACL.

[7]

T. Joachims. Optimizing search engines using clickthrough data. KDD, pages 133--142, 2002.

Digital Library

[8]

C. Lyon, J. Malcolm, and B. Dickerson. Detecting short passages of similar text in large document collections. EMNLP, pages 118--125, 2001.

[9]

A. Moschitti. Efficient Convolution Kernels for Dependency and Constituent Syntactic Trees. In ECML, pages 318--329. 2006.

Digital Library

[10]

P. Nakov, L. Màrquez, A. Moschitti, W. Magdy, H. Mubarak, A. A. Freihat, J. Glass, and B. Randeree. SemEval-2016 task 3: Community question answering. In Proceedings of SemEval '16. ACL, 2016.

[11]

A. Severyn and A. Moschitti. Structural relationships for large-scale learning of answer re-ranking. SIGIR, pages 741--750, 2012.

Digital Library

[12]

K. Tymoshenko and A. Moschitti. Assessing the impact of syntactic and semantic structures for answer passages reranking. In Proceedings of CIKM '15, pages 1451--1460, New York, NY, USA, 2015. ACM.

Digital Library

[13]

K. Wang, Z. Ming, and T.-S. Chua. A syntactic tree matching approach to finding similar questions in community-based qa services. In SIGIR, pages 187--194, 2009.

Digital Library

[14]

M. Wise. Yap3: Improved detection of similarities in computer program and other texts. In SIGCSE, pages 130--134, 1996.

Digital Library

[15]

G. Zhou, L. Cai, J. Zhao, and K. Liu. Phrase-based translation model for question retrieval in community question answer archives. In ACL, pages 653--662, 2011.

Digital Library

Cited By

Sajid NHasan MIbrahim M(2024)Feature engineering in learning-to-rank for community question answering taskInternational Journal of Computers and Applications10.1080/1206212X.2024.238064746:8(555-566)Online publication date: 7-Aug-2024
https://doi.org/10.1080/1206212X.2024.2380647
Othman NFaiz RSmaïli K(2022)Learning English and Arabic question similarity with Siamese Neural Networks in community question answering servicesData & Knowledge Engineering10.1016/j.datak.2021.101962138:COnline publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1016/j.datak.2021.101962
Kancherla LPrasath R(2021)Case Based Reasoning Approaches for Reuse and Adaptation in Community Question Answering SystemAdvanced Informatics for Computing Research10.1007/978-981-16-3660-8_12(126-140)Online publication date: 20-Jun-2021
https://doi.org/10.1007/978-981-16-3660-8_12
Show More Cited By

Index Terms

Learning to Re-Rank Questions in Community Question Answering Using Advanced Features
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Learning to rank
    2. Machine learning approaches
      1. Kernel methods
        Support vector machines
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Question answering

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cover image ACM Conferences

CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

October 2016

2566 pages

ISBN:9781450340731

DOI:10.1145/2983323

General Chairs:
Snehasis Mukhopadhyay
Indiana University Purdue University Indianapolis, USA
,
ChengXiang Zhai
University of Illinois at Urbana-Champaign, USA
,
Program Chairs:
Elisa Bertino
Purdue University
,
Fabio Crestani
University of Lugano
,
Javed Mostafa
University of North Carolina
,
Jie Tang
Tsinghua University
,
Luo Si
Alibaba Group Inc & Purdue University
,
Xiaofang Zhou
University of Queensland
,
Yi Chang
Yahoo Research
,
Yunyao Li
IBM Research - Almaden
,
Parikshit Sondhi
WalmartLabs

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 24 October 2016

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CIKM'16: ACM Conference on Information and Knowledge Management

October 24 - 28, 2016

Indiana, Indianapolis, USA

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CIKM '16 Paper Acceptance Rate 160 of 701 submissions, 23%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Sajid NHasan MIbrahim M(2024)Feature engineering in learning-to-rank for community question answering taskInternational Journal of Computers and Applications10.1080/1206212X.2024.238064746:8(555-566)Online publication date: 7-Aug-2024
https://doi.org/10.1080/1206212X.2024.2380647
Othman NFaiz RSmaïli K(2022)Learning English and Arabic question similarity with Siamese Neural Networks in community question answering servicesData & Knowledge Engineering10.1016/j.datak.2021.101962138:COnline publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1016/j.datak.2021.101962
Kancherla LPrasath R(2021)Case Based Reasoning Approaches for Reuse and Adaptation in Community Question Answering SystemAdvanced Informatics for Computing Research10.1007/978-981-16-3660-8_12(126-140)Online publication date: 20-Jun-2021
https://doi.org/10.1007/978-981-16-3660-8_12
Aksu IChen ND’Haro LBanchs R(2021)Reranking of Responses Using Transfer Learning for a Retrieval-Based ChatbotIncreasing Naturalness and Flexibility in Spoken Dialogue Interaction10.1007/978-981-15-9323-9_20(239-250)Online publication date: 11-Mar-2021
https://doi.org/10.1007/978-981-15-9323-9_20
Filice SCohen NCarmel D(2020)Voice-based Reformulation of Community AnswersProceedings of The Web Conference 202010.1145/3366423.3380053(2885-2891)Online publication date: 20-Apr-2020
https://dl.acm.org/doi/10.1145/3366423.3380053
Huang WQu QYang M(2020)Interactive knowledge-enhanced attention network for answer selectionNeural Computing and Applications10.1007/s00521-019-04630-xOnline publication date: 21-Jan-2020
https://doi.org/10.1007/s00521-019-04630-x
Rücklé ASwarnkar KGurevych I(2019)Improved Cross-Lingual Question Retrieval for Community Question AnsweringThe World Wide Web Conference10.1145/3308558.3313502(3179-3186)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313502
Hoogeveen DWang LBaldwin TVerspoor K(2018)Web Forum Retrieval and Text AnalyticsFoundations and Trends in Information Retrieval10.1561/150000006212:1(1-163)Online publication date: 3-Jan-2018
https://dl.acm.org/doi/10.1561/1500000062
Abad ANabi MMoschitti AKando NSakai TJoho HLi Hde Vries AWhite R(2017)Autonomous Crowdsourcing through Human-Machine Collaborative LearningProceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3077136.3080666(873-876)Online publication date: 7-Aug-2017
https://dl.acm.org/doi/10.1145/3077136.3080666

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