short-paper

Incorporating word correlation into tag-topic model for semantic knowledge acquisition

Authors:

Xiaohua HuAuthors Info & Claims

CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

Pages 1622 - 1626

https://doi.org/10.1145/2396761.2398485

Published: 29 October 2012 Publication History

Abstract

This paper presents a tag-topic model with Dirichlet Forest prior (TTM-DF) for semantic knowledge acquisition from blog. The TTM-DF model extends the tag-topic model (TTM) by replacing the Dirichlet prior with the Dirichlet Forest prior over the topic-word multinomial. The correlation between words are calculated to generate a set of Must-Links and Cannot-Links, then the structures of Dirichlet trees are obtained though encoding the constraints of Must-Links and Cannot-Links. Words under the same subtrees are expected to be more correlated than words under different subtrees. We conduct experiments on a synthetic and a blog dataset. Both of the experimental results show that the TTM-DF model performs much better than the TTM model. It can improve the coherence of the underlying topics and the tag-topic distributions, and capture semantic knowledge effectively.

References

[1]

Tingting He, Fang Li. 2012. Semantic Knowledge Acquisition from Blogs with Tag-Topic Model. China Communications, 2012, 9(3): 38--48.

[2]

D. M. Blei, A. Y. Ng, M. I. Jordan. 2003. Latent Dirichlet Allocation. Journal of Machine Learning Research, 2003, 3(4-5): 993--1022.

Digital Library

[3]

David Andrzejewski, Xiaojin Zhu, and Mark Craven. 2009. Incorporating Domain knowledge into topic modeling via Dirichlet Forest priors. In Proc. of ICML 2009, 25--32.

Digital Library

[4]

Basu, S., Davidson, I., & Wagstaff, K. (Eds.). 2008. Constrained clustering: Advances in algorithms, theory, and applications. Chapman & Hall/CRC.

Digital Library

[5]

S. P. Ponzetto, M. Strube. 2007. Deriving a large-scale taxonomy from Wikipedia. In Proc. of AAAI07, 1440--1445.

Digital Library

[6]

Suchanek, F. M., G. Kasneci & G.Weikum. 2007. YAGO: A core of semantic knowledge. In Proc. of WWW-07, 2007.

Digital Library

[7]

Michael Strube and Simon Paolo Ponzetto. 2006. WikiRelate! Computing semantic relatedness using Wikipedia. In Proc. of AI-06, Boston, Massachusetts, USA, 2006.

Digital Library

[8]

Xinhui Tu, Tingting He, Jing Luo and Long Chen. 2010. Wikipedia-based semantic smoothing for the language modeling approach to information retrieval. In Proc. of ECIR-2010, 370--381.

Digital Library

[9]

Marius Pasca. 2004. Acquisition of Categorized Named Entities for Web Search. In Proc. of CIKM 2004. USA.

Digital Library

[10]

Keiji Shinzato and Kentaro Torisawa. 2005. A Simple WWW-based Method for Semantic Word Class Acquisition. In Proc. of RANLP-05, 2005.

[11]

Thomas Hofmann. 1999. Probabilistic latent semantic indexing. In Proc. of SIGIR-99, 50--57.

Digital Library

[12]

X. Wei and W. B. Croft. 2006. LDA-based document models for ad-hoc retrieval. In Proc. of SIGIR-06, 178--185.

Digital Library

[13]

R. Arora and B. Ravindran. 2008. Latent dirichlet allocation based multi-document summarization. In Proc. of AND-08, 91--97.

Digital Library

[14]

Jordan Boyd-Graber, David M. Blei, and Xiaojin Zhu. 2007. A topic model for word sense disambiguation. In Proc. of EMNLP 2007.

[15]

David Andrzejewski, Xiaojin Zhu. 2009. Latent Dirichlet Allocation with Topic-in-Set Knowledge. Proceedings of the NAACL HLT Workshop on Semi-supervised Learning for Natural Language Processing, 43--48.

Digital Library

[16]

Jagadeesh Jagarlamudi, Hal Daume III, Raghavendra Udupa. 2010. Incorporating Lexical Priors into Topic Models. The 5th Annual Machine Learning Symposium, 2010.

Digital Library

[17]

Minka, T. P. 1999. The Dirichlet-tree distribution (Technical Report). http://research.microsoft.com/~minka/papers/dirichle-t/minka-dirtree.pdf.

[18]

T. L. Griffiths, and M. Steyvers. 2004, Finding scientific topics. Proceedings of National Academy of Sciences of the United States of America 101, 2004, 5228--5235.

[19]

Qun, L. I. U., Sujian, L. I. 2002. Word Similarity Computing Based on How-net, Computational Linguistics and Chinese Language Processing, 2002.

[20]

Xinhui Tu, Tingting He, Hongchun Zhang and Kunfeng Zhou. 2012. Extracting Structured Information from Chinese Wikipedia and Measuring Relatedness between Words. Journal of Chinese Information Process, 2012.

Cited By

Chen LJose JYu HYuan FBarrett RCummings RAgichtein EGabrilovich E(2017)A Semantic Graph-Based Approach for Mining Common Topics from Multiple Asynchronous Text StreamsProceedings of the 26th International Conference on World Wide Web10.1145/3038912.3052630(1201-1209)Online publication date: 3-Apr-2017
https://dl.acm.org/doi/10.1145/3038912.3052630
Shin SMoon I(2017)Guided HTMIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2016.262579029:2(330-343)Online publication date: 1-Feb-2017
https://dl.acm.org/doi/10.1109/TKDE.2016.2625790
Chen LJose JYu HYuan FZhang DBennett PJosifovski VNeville JRadlinski F(2016)A Semantic Graph based Topic Model for Question Retrieval in Community Question AnsweringProceedings of the Ninth ACM International Conference on Web Search and Data Mining10.1145/2835776.2835809(287-296)Online publication date: 8-Feb-2016
https://dl.acm.org/doi/10.1145/2835776.2835809
Show More Cited By

Index Terms

Incorporating word correlation into tag-topic model for semantic knowledge acquisition
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources

Recommendations

A Non-Parametric Topic Model for Short Texts Incorporating Word Coherence Knowledge
CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

Mining topics in short texts (e.g. tweets, instant messages) can help people grasp essential information and understand key contents, and is widely used in many applications related to social media and text analysis. The sparsity and noise of short ...
Topic-driven reader comments summarization
CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

Readers of a news article often read its comments contributed by other readers. By reading comments, readers obtain not only complementary information about this news article but also the opinions from other readers. However, the existing ranking ...
Research on Multi-document Summarization Based on LDA Topic Model
IHMSC '14: Proceedings of the 2014 Sixth International Conference on Intelligent Human-Machine Systems and Cybernetics - Volume 02

Compared with VSM (Vector Space Model) and graph-ranking models, LDA (Latent Dirichlet Allocation) Model can discover latent topics in the corpus and latent topics are beneficial to use sentence-ranking mechanisms to form a good summary. In the paper, ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

October 2012

2840 pages

ISBN:9781450311564

DOI:10.1145/2396761

General Chair:
Xuewen Chen
Wayne State University, USA
,
Program Chairs:
Guy Lebanon
Georgia Institute of Technology
,
Haixun Wang
Microsoft Research Asia
,
Mohammed J. Zaki
Rensselaer Polytechnic Institute

Copyright © 2012 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: 29 October 2012

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Conference

CIKM'12

Sponsor:

CIKM'12: 21st ACM International Conference on Information and Knowledge Management

October 29 - November 2, 2012

Hawaii, Maui, USA

Acceptance Rates

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

Upcoming Conference

CIKM '25

Sponsor:
sigir
sigir

The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

Seoul , Republic of Korea

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
363
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)0

Reflects downloads up to 01 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Chen LJose JYu HYuan FBarrett RCummings RAgichtein EGabrilovich E(2017)A Semantic Graph-Based Approach for Mining Common Topics from Multiple Asynchronous Text StreamsProceedings of the 26th International Conference on World Wide Web10.1145/3038912.3052630(1201-1209)Online publication date: 3-Apr-2017
https://dl.acm.org/doi/10.1145/3038912.3052630
Shin SMoon I(2017)Guided HTMIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2016.262579029:2(330-343)Online publication date: 1-Feb-2017
https://dl.acm.org/doi/10.1109/TKDE.2016.2625790
Chen LJose JYu HYuan FZhang DBennett PJosifovski VNeville JRadlinski F(2016)A Semantic Graph based Topic Model for Question Retrieval in Community Question AnsweringProceedings of the Ninth ACM International Conference on Web Search and Data Mining10.1145/2835776.2835809(287-296)Online publication date: 8-Feb-2016
https://dl.acm.org/doi/10.1145/2835776.2835809
Chen LJose JYu HYuan F(2016)A Hybrid Approach for Question Retrieval in Community Question AnswerinThe Computer Journal10.1093/comjnl/bxw036Online publication date: 8-Sep-2016
https://doi.org/10.1093/comjnl/bxw036
Nie WLiu AZhu XSu Y(2016)Quality models for venue recommendation in location-based social networkMultimedia Tools and Applications10.1007/s11042-014-2339-x75:20(12521-12534)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.1007/s11042-014-2339-x
Chen LJose JYu HYuan FZhang H(2016)Probabilistic Topic Modelling with Semantic GraphAdvances in Information Retrieval10.1007/978-3-319-30671-1_18(240-251)Online publication date: 2016
https://doi.org/10.1007/978-3-319-30671-1_18

View Options

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.

Figures

Tables

Media

View Table of Conten