Please wait a minute...
Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (3): 66-75    DOI: 10.11925/infotech.2096-3467.2018.0550
Current Issue | Archive | Adv Search |
Temporal Intent Classification with Query Expression Feature
Sisi Gui1,2,Wei Lu3,Xiaojuan Zhang4()
1School of Information Management, Wuhan University, Wuhan 430072, China
2Institute for Information Retrieval and Knowledge Mining, Wuhan University, Wuhan 430072, China
3Center for Studies of Information Resources, Wuhan University, Wuhan 430072, China
4School of Computer and Information Science, Southwest University, Chongqing 400715, China
Download: PDF (2148 KB)   HTML ( 3
Export: BibTeX | EndNote (RIS)      
Abstract  

[Objective] This paper investigates the effectiveness of query-based features and compares the performance of two types of classifiers in a query temporal intent classification task. [Methods] This paper first reviews all query-based features and then classifies those features into three types, according to their temporal relevance, namely, atemporal, implicit temporal and explicit temporal. Then, it tests accuracy of a temporal query intent classification task, using a supervised classifier and a semi-supervised classifier individually, with various combinations of query-based features of different types. [Results] Among all tested query-based features, using explicit temporal features achieves best accuracy, especially for the feature on whether a query contains a year; The performance hardly varies across classifiers; Our best macro average accuracy of 81.14% is higher than that in previous studies with the same experimental setups. [Limitations] Due to accessibility of dataset, our experiments are done on a limited size dataset. Only existing query-based features are studied and no new feature is proposed or tested. [Conclusions] Using highly temporal relevant features can improve accuracy in temporal query intent classification task, whereas using slightly temporal relevant features could hardly improve accuracy.

Key wordsTemporal Intent      Supervised Classification      Semi-supervised Classification      Feature Engineering     
Received: 17 May 2018      Published: 17 April 2019

Cite this article:

Sisi Gui,Wei Lu,Xiaojuan Zhang. Temporal Intent Classification with Query Expression Feature. Data Analysis and Knowledge Discovery, 2019, 3(3): 66-75.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2018.0550     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2019/V3/I3/66

[1] Broder A.A Taxonomy of Web Search[J]. SIGIR Forum, 2002, 36(2): 3-10.
[2] Sushmita S, Piwowarski B, Lalmas M.Dynamics of Genre and Domain Intents[C]// Proceedings of the 6th Asia Information Retrieval Societies Conference on Information Retrieval Technology. Springer, 2010: 399-409.
[3] Calderón-Benavides L, González-Caro C, Baeza-Yates R A. Towards a Deeper Understanding of the User’s Query Intent[C]// Proceedings of the 2010 Workshop on Query Representation and Understanding. 2010: 21-24.
[4] Nguyen B V, Kan M.Functional Faceted Web Query Analysis[C]// Proceedings of the 16th International World Wide Web Conference. 2007.
[5] González-Caro C, Baeza-Yates R.A Multi-faceted Approach to Query Intent Classification[C]// Proceedings of the 18th International Conference on String Processing and Information Retrieval. 2011: 368-379.
[6] Campos R, Dias G, Jorge A M.What is the Temporal Value of Web Snippets?[C]// Proceedings of the 1st International Temporal Web Analytics Workshop. 2011: 9-16.
[7] 张晓娟, 韩毅. 时态信息检索研究综述[J]. 数据分析与知识发现, 2017, 1(1): 3-15.
[7] (Zhang Xiaojuan, Han Yi.Reviews on Temporal Information Retrieval[J]. Data Analysis and Knowledge Discovery, 2017, 1(1): 3-15.)
[8] Jones R, Diaz F. Temporal Profiles of Queries[J]. ACM Transactions on Information Systems, 2007, 25(3): Article No.14.
[9] Joho H, Jatowt A, Blanco R, et al.Overview of NTCIR-11 Temporal Information Access (Temporalia) Task[C]// Proceedings of the 11th NTCIR Conference on Evaluation of Information Access Technologies. 2014: 217-224.
[10] Mizzaro S.How Many Relevances in Information Retrieval?[J]. Interacting with Computers, 1998, 10(3): 303-320.
[11] Yu H, Kang X, Ren F.TUTA1 at the NTCIR-11 Temporalia Task[C]// Proceedings of the 11th NTCIR Conference on Evaluation of Information Access Technologies. 2014: 461-467.
[12] Shah A, Shah D, Majumder P.Andd7@NTCIR-11 Temporal Information Access Task[C]// Proceedings of the 11th NTCIR Conference on Evaluation of Information Access Technologies. 2014: 456-460.
[13] Filannino M, Nenadic G.Using Machine Learning to Predict Temporal Orientation of Search Engines’ Queries in the Temporalia Challenge[C]// Proceedings of the 11th NTCIR Conference on Evaluation of Information Access Technologies. 2014: 438-442.
[14] Burghartz R, Berberich K.MPI-INF at the NTCIR-11 Temporal Query Classification Task[C]// Proceedings of the 11th NTCIR Conference on Evaluation of Information Access Technologies. 2014: 443-450.
[15] Hasanuzzaman M, Dias G, Ferrari S.HULTECH at the NTCIR-11 Temporalia Task: Ensemble Learning for Temporal Query Intent Classification[C]// Proceedings of the 11th NTCIR Conference on Evaluation of Information Access Technologies. 2014: 478-482.
[16] Campos R, Dias G, Jorge A, et al.GTE: A Distributional Second-order Co-occurrence Approach to Improve the Identification of Top Relevant Dates in Web Snippets[C]// Proceedings of the 21st ACM International Conference on Information and Knowledge Management. 2012: 2035-2039.
[17] Hasanuzzaman M, Saha S, Dias G, et al.Understanding Temporal Query Intent[C]// Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2015: 823-826.
[18] Hou Y, Tan C, Xu J, et al.HITSZ-ICRC at NTCIR-11 Temporalia Task[C]// Proceedings of the 11th NTCIR Conference on Evaluation of Information Access Technologies. 2014: 468-473.
[19] Miller G A.WordNet: A Lexical Database for English[J]. Communications of the ACM, 1995, 38(11): 39-41.
[20] Sokolova M, Lapalme G.A Systematic Analysis of Performance Measures for Classification Tasks[J]. Information Processing and Management, 2009, 45(4): 427-437.
[1] Yu Bengong,Ji Haomin. Semi-Supervised Method for Text Classification Based on DW-TCI[J]. 数据分析与知识发现, 2020, 4(10): 58-69.
[2] Zhang Xiaojuan,Han Yi. Reviews on Temporal Information Retrieval[J]. 数据分析与知识发现, 2017, 1(1): 3-15.
[3] Zhang Xiaojuan, Lu Wei, Zhou Hongxia. Analyzing and Retrieval Modeling on Implicit Temporal Intents in User's Queries[J]. 现代图书情报技术, 2011, (11): 38-43.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn