research-article

Concept-based Short Text Classification and Ranking

Authors:

Zhongyuan Wang,

Ji-Rong WenAuthors Info & Claims

CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

Pages 1069 - 1078

https://doi.org/10.1145/2661829.2662067

Published: 03 November 2014 Publication History

Abstract

Most existing approaches for text classification represent texts as vectors of words, namely ``Bag-of-Words.'' This text representation results in a very high dimensionality of feature space and frequently suffers from surface mismatching. Short texts make these issues even more serious, due to their shortness and sparsity. In this paper, we propose using ``Bag-of-Concepts'' in short text representation, aiming to avoid the surface mismatching and handle the synonym and polysemy problem. Based on ``Bag-of-Concepts,'' a novel framework is proposed for lightweight short text classification applications. By leveraging a large taxonomy knowledgebase, it learns a concept model for each category, and conceptualizes a short text to a set of relevant concepts. A concept-based similarity mechanism is presented to classify the given short text to the most similar category. One advantage of this mechanism is that it facilitates short text ranking after classification, which is needed in many applications, such as query or ad recommendation. We demonstrate the usage of our proposed framework through a real online application: Channel-based Query Recommendation. Experiments show that our framework can map queries to channels with a high degree of precision (avg. precision=90.3%), which is critical for recommendation applications.

References

[1]

C. C. Aggarwal and C. Zhai. Mining text data. Springer, 2012.

Digital Library

[2]

A. Anagnostopoulos, L. Becchetti, C. Castillo, and A. Gionis. An optimization framework for query recommendation. In WSDM, pages 161--170. ACM, 2010.

Digital Library

[3]

R. Baeza-Yates, C. Hurtado, and M. Mendoza. Query recommendation using query logs in search engines. In EDBT, pages 588--596. Springer, 2005.

Digital Library

[4]

S. M. Beitzel, E. C. Jensen, O. Frieder, D. D. Lewis, A. Chowdhury, and A. Kolcz. Improving automatic query classification via semi-supervised learning. In ICDM, 2005.

Digital Library

[5]

I. Bordino, G. De Francisci Morales, I. Weber, and F. Bonchi. From machu_picchu to rafting the urubamba river: anticipating information needs via the entity-query graph. In WSDM, pages 275--284. ACM, 2013.

Digital Library

[6]

C.-C. Chang and C.-J. Lin. Libsvm: a library for support vector machines. TIST, 2(3):27, 2011.

Digital Library

[7]

M. Chen, X. Jin, and D. Shen. Short text classification improved by learning multi-granularity topics. In IJCAI, pages 1776--1781. AAAI Press, 2011.

Digital Library

[8]

C. Cortes and V. Vapnik. Support-vector networks. Machine learning, 20(3):273--297, 1995.

Digital Library

[9]

N. Craswell and M. Szummer. Random walks on the click graph. In SIGIR, pages 239--246. ACM, 2007.

Digital Library

[10]

W. B. Croft, M. Bendersky, H. Li, and G. Xu. Query representation and understanding workshop. In SIGIR Forum, volume 44, pages 48--53, 2010.

Digital Library

[11]

H. K. Dai, L. Zhao, Z. Nie, J.-R. Wen, L. Wang, and Y. Li. Detecting online commercial intention (oci). In WWW, 2006.

Digital Library

[12]

V. Dang and W. B. Croft. Diversity by proportionality: an election-based approach to search result diversification. In SIGIR, pages 65--74. ACM, 2012.

Digital Library

[13]

H. Feild and J. Allan. Task-aware query recommendation. In SIGIR, pages 83--92. ACM, 2013.

Digital Library

[14]

E. Gabrilovich and S. Markovitch. Overcoming the brittleness bottleneck using wikipedia: Enhancing text categorization with encyclopedic knowledge. In AAAI, 2006.

Digital Library

[15]

E. Gabrilovich and S. Markovitch. Computing semantic relatedness using wikipedia-based explicit semantic analysis. In IJCAI, volume 7, pages 1606--1611, 2007.

Digital Library

[16]

J. He, V. Hollink, and A. de Vries. Combining implicit and explicit topic representations for result diversification. In SIGIR, pages 851--860. ACM, 2012.

Digital Library

[17]

X. Hu, N. Sun, C. Zhang, and T.-S. Chua. Exploiting internal and external semantics for the clustering of short texts using world knowledge. In CIKM, pages 919--928. ACM, 2009.

Digital Library

[18]

L. Huang. Concept-based text clustering. PhD thesis, The University of Waikato, 2011.

[19]

A. Jordan. On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes. NIPS, 14:841, 2002.

[20]

Y.-H. Kim, S.-Y. Hahn, and B.-T. Zhang. Text filtering by boosting naive bayes classifiers. In SIGIR, 2000.

Digital Library

[21]

T. Lee, Z. Wang, H. Wang, and S.-w. Hwang. Attribute extraction and scoring: A probabilistic approach. In ICDE, pages 194--205. IEEE, 2013.

Digital Library

[22]

P. Li, H. Wang, K. Q. Zhu, Z. Wang, and X. Wu. Computing term similarity by large probabilistic isa knowledge. In CIKM, pages 1401--1410. ACM, 2013.

Digital Library

[23]

R. Li, B. Kao, B. Bi, R. Cheng, and E. Lo. Dqr: a probabilistic approach to diversified query recommendation. In CIKM, pages 16--25. ACM, 2012.

Digital Library

[24]

X. Li, Y.-Y. Wang, and A. Acero. Learning query intent from regularized click graphs. In SIGIR, 2008.

Digital Library

[25]

Y. Li, D. McLean, Z. A. Bandar, J. D. O'shea, and K. Crockett. Sentence similarity based on semantic nets and corpus statistics. TKDE, 18(8):1138--1150, 2006.

Digital Library

[26]

H. T. Ng, W. B. Goh, and K. L. Low. Feature selection, perceptron learning, and a usability case study for text categorization. In SIGIR. ACM, 1997.

Digital Library

[27]

X.-H. Phan, L.-M. Nguyen, and S. Horiguchi. Learning to classify short and sparse text & web with hidden topics from large-scale data collections. In WWW, 2008.

Digital Library

[28]

J. R. Quinlan. Induction of decision trees. Machine learning, pages 81--106, 1986.

Digital Library

[29]

M. Sahlgren and R. Cöster. Using bag-of-concepts to improve the performance of support vector machines in text categorization. In COLING, page 487. ACL, 2004.

Digital Library

[30]

G. Salton, A. Wong, and C.-S. Yang. A vector space model for automatic indexing. Communications of the ACM, 1975.

Digital Library

[31]

D. Shen, R. Pan, J.-T. Sun, J. J. Pan, K. Wu, J. Yin, and Q. Yang. Q2c@ust: our winning solution to query classification in kddcup 2005. SIGKDD, 7(2):100--110, 2005.

Digital Library

[32]

D. Shen, R. Pan, J.-T. Sun, J. J. Pan, K. Wu, J. Yin, and Q. Yang. Query enrichment for web-query classification. TOIS, 24(3):320--352, 2006.

Digital Library

[33]

D. Shen, J.-T. Sun, Q. Yang, and Z. Chen. Building bridges for web query classification. In SIGIR, 2006.

Digital Library

[34]

F. Song and W. B. Croft. A general language model for information retrieval. In CIKM, pages 316--321. ACM, 1999.

Digital Library

[35]

Y. Song, H. Wang, Z. Wang, H. Li, and W. Chen. Short text conceptualization using a probabilistic knowledgebase. In IJCAI, pages 2330--2336. AAAI Press, 2011.

Digital Library

[36]

F. M. Suchanek, G. Kasneci, and G. Weikum. Yago: a core of semantic knowledge. In WWW, pages 697--706. ACM, 2007.

Digital Library

[37]

A. Sun. Short text classification using very few words. In SIGIR, pages 1145--1146. ACM, 2012.

Digital Library

[38]

I. Szpektor, A. Gionis, and Y. Maarek. Improving recommendation for long-tail queries via templates. In WWW, pages 47--56. ACM, 2011.

Digital Library

[39]

Z. Wang, H. Wang, and Z. Hu. Head, modifier, and constraint detection in short texts. In ICDE, pages 280--291, 2014.

[40]

W. Wu, H. Li, H. Wang, and K. Q. Zhu. Probase: A probabilistic taxonomy for text understanding. In SIGMOD, pages 481--492. ACM, 2012.

Digital Library

[41]

E. Yeh, D. Ramage, C. D. Manning, E. Agirre, and A. Soroa. Wikiwalk: random walks on wikipedia for semantic relatedness. In ACL Workshop, pages 41--49. ACL, 2009.

Digital Library

[42]

C. Zhai and J. Lafferty. A study of smoothing methods for language models applied to ad hoc information retrieval. In SIGIR, pages 334--342. ACM, 2001.

Digital Library

[43]

Z. Zhang and O. Nasraoui. Mining search engine query logs for query recommendation. In WWW, pages 1039--1040, 2006.

Digital Library

Cited By

Ahmad PYuanchao LAurangzeb KAnwar MHaq Q(2024)Semantic web-based propaganda text detection from social media using meta-learningService Oriented Computing and Applications10.1007/s11761-024-00422-xOnline publication date: 2-Aug-2024
https://doi.org/10.1007/s11761-024-00422-x
Wang YZhang XChen TZhang Y(2024)A Flexible Simplicity Enhancement Model for Knowledge Graph Completion TaskArtificial Intelligence10.1007/978-981-99-9119-8_27(298-309)Online publication date: 3-Feb-2024
https://doi.org/10.1007/978-981-99-9119-8_27
Wang YOuyang XGuo DZhu X(2023)MEGA: Meta-Graph Augmented Pre-Training Model for Knowledge Graph CompletionACM Transactions on Knowledge Discovery from Data10.1145/361737918:1(1-24)Online publication date: 16-Oct-2023
https://dl.acm.org/doi/10.1145/3617379
Show More Cited By

Index Terms

Concept-based Short Text Classification and Ranking
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees

Recommendations

Short text classification using very few words
SIGIR '12: Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval

We propose a simple, scalable, and non-parametric approach for short text classification. Leveraging the well studied and scalable Information Retrieval (IR) framework, our approach mimics human labeling process for a piece of short text. It first ...
Enhancing naive bayes with various smoothing methods for short text classification
WWW '12 Companion: Proceedings of the 21st International Conference on World Wide Web

Partly due to the proliferance of microblog, short texts are becoming prominent. A huge number of short texts are generated every day, which calls for a method that can efficiently accommodate new data to incrementally adjust classification models. ...
Short Text Classification Using Wikipedia Concept Based Document Representation
ITA '13: Proceedings of the 2013 International Conference on Information Technology and Applications

Short text classification is a difficult and challenging task in information retrieval systems since the text data is short, sparse and multidimensional. In this paper, we represent short text with Wikipedia concepts for classification. Short document ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

November 2014

2152 pages

ISBN:9781450325981

DOI:10.1145/2661829

General Chairs:
Jianzhong Li
Harbin Inst. of Technology
,
X. Sean Wang
Fudan University
,
Program Chairs:
Minos Garofalakis
Technical University of Crete, Greece
,
Ian Soboroff
National Institute of Standards, USA
,
Torsten Suel
New York University, USA
,
Min Wang
Google Research, USA

Copyright © 2014 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 ACM 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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 November 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

National Natural Science Foundation of China
Fund of the State Key Laboratory of Software Development Environment
Ministry of Science and Technology of the People's Republic of China
Ministry of Education of the People's Republic of China
Microsoft Research

Conference

CIKM '14

Sponsor:

CIKM '14: 2014 ACM Conference on Information and Knowledge Management

November 3 - 7, 2014

Shanghai, China

Acceptance Rates

CIKM '14 Paper Acceptance Rate 175 of 838 submissions, 21%;

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

Upcoming Conference

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

68
Total Citations
View Citations
1,098
Total Downloads

Downloads (Last 12 months)36
Downloads (Last 6 weeks)6

Reflects downloads up to 15 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Ahmad PYuanchao LAurangzeb KAnwar MHaq Q(2024)Semantic web-based propaganda text detection from social media using meta-learningService Oriented Computing and Applications10.1007/s11761-024-00422-xOnline publication date: 2-Aug-2024
https://doi.org/10.1007/s11761-024-00422-x
Wang YZhang XChen TZhang Y(2024)A Flexible Simplicity Enhancement Model for Knowledge Graph Completion TaskArtificial Intelligence10.1007/978-981-99-9119-8_27(298-309)Online publication date: 3-Feb-2024
https://doi.org/10.1007/978-981-99-9119-8_27
Wang YOuyang XGuo DZhu X(2023)MEGA: Meta-Graph Augmented Pre-Training Model for Knowledge Graph CompletionACM Transactions on Knowledge Discovery from Data10.1145/361737918:1(1-24)Online publication date: 16-Oct-2023
https://dl.acm.org/doi/10.1145/3617379
Wang YWang ZZhang HLiu Z(2023)Microblog Retrieval Based on Concept-Enhanced Pre-Training ModelACM Transactions on Knowledge Discovery from Data10.1145/355231117:3(1-32)Online publication date: 22-Feb-2023
https://dl.acm.org/doi/10.1145/3552311
Gao NWang YChen PTang J(2023)Boosting Short Text Classification by Solving the OOV ProblemIEEE/ACM Transactions on Audio, Speech and Language Processing10.1109/TASLP.2023.331642231(4014-4024)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TASLP.2023.3316422
Zhu XWang YZhang YZhang X(2023)Leveraging Concept-Driven Pre-Training Model for Shot-Text Conceptualization Task2023 8th International Conference on Data Science in Cyberspace (DSC)10.1109/DSC59305.2023.00054(329-336)Online publication date: 18-Aug-2023
https://doi.org/10.1109/DSC59305.2023.00054
Yuan LWang YChen TZhang XLiu CZhang Y(2023)A Novel Concept-Driven Negative Sampling Mechanism for Enhancing Semanticity and Interpretability of Knowledge Graph Completion Task2023 8th International Conference on Data Science in Cyberspace (DSC)10.1109/DSC59305.2023.00047(277-284)Online publication date: 18-Aug-2023
https://doi.org/10.1109/DSC59305.2023.00047
Wu M(2023)Commonsense knowledge powered heterogeneous graph attention networks for semi-supervised short text classification▪Expert Systems with Applications: An International Journal10.1016/j.eswa.2023.120800232:COnline publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1016/j.eswa.2023.120800
Li LWang YGuo XYuan LLi BXu S(2023)Short-Text Conceptualization Based on Hyper-Graph Learning and Multiple Prior KnowledgeSocial Media Processing10.1007/978-981-99-7596-9_8(104-117)Online publication date: 15-Nov-2023
https://doi.org/10.1007/978-981-99-7596-9_8
Ouyang XWang YLi QJu ZLiu CZhang Y(2023)Leverage Heterogeneous Graph Neural Networks for Short-Text ConceptualizationSocial Media Processing10.1007/978-981-99-7596-9_7(90-103)Online publication date: 15-Nov-2023
https://doi.org/10.1007/978-981-99-7596-9_7
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents