short-paper

Using Term Location Information to Enhance Probabilistic Information Retrieval

Authors:

Jimmy Xiangji HuangAuthors Info & Claims

SIGIR '15: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 883 - 886

https://doi.org/10.1145/2766462.2767827

Published: 09 August 2015 Publication History

Abstract

Nouns are more important than other parts of speech in information retrieval and are more often found near the beginning or the end of sentences. In this paper, we investigate the effects of rewarding terms based on their location in sentences on information retrieval. Particularly, we propose a novel Term Location (TEL) retrieval model based on BM25 to enhance probabilistic information retrieval, where a kernel-based method is used to capture term placement patterns. Experiments on five TREC datasets of varied size and content indicate the proposed model significantly outperforms the optimized BM25 and DirichletLM in MAP over all datasets with all kernel functions, and excels the optimized BM25 and DirichletLM over most of the datasets in P@5 and P@20 with different kernel functions.

References

[1]

H. Ann. The Essentials of English -- a writer's handbook. New York, Pearson Education, 2003.

[2]

C. Dickens. Hard Times. Bradbury & Evans, 1854.

[3]

D. A. Evans and C. Zhai. Noun-phrase analysis in unrestricted text for information retrieval. In ACL 1996, pages 17--24.

Digital Library

[4]

S.-H. Hung, C.-H. Lin, and J.-S. Hong. Web mining for event-based commonsense knowledge using lexico-syntactic pattern matching and semantic role labeling. Expert Systems with Applications, 37(1):341--347, 2010.

Digital Library

[5]

F. Ibekwe-SanJuan and et al. Annotation of scientific summaries for information retrieval. CoRR, 2011.

[6]

Y. Jing and W. B. Croft. An association thesaurus for information retrieval. In RIAO'94, pages 146--160.

[7]

K. Liu and et al. Effectiveness of lexico-syntactic pattern matching for ontology enrichment with clinical documents. Meth. of info. in med., 50(5):397, 2011.

[8]

S. Liu, F. Liu, C. Yu, and W. Meng. An effective approach to document retrieval via utilizing wordnet and recognizing phrases. In SIGIR'04, pages 266--272.

Digital Library

[9]

Y. Lv and C. Zhai. Positional language models for information retrieval. In SIGIR'09, pages 299--306.

Digital Library

[10]

C. F. Meyer. Introducing English Linguistics. Cambridge University Press, 2010.

[11]

I. Ounis and et al. Overview of the TREC-2006 blog track.

[12]

O. Vechtomova. Noun phrases in interactive query expansion and document ranking. Info. Retrieval, 9:399--420, 2006.

Digital Library

[13]

E. Voorhees and D. Harman. TREC: Experiment and evaluation in information retrieval. MIT Press, 2005.

Digital Library

[14]

D. Yang and et al. A natural language processing and semantic-based system for contract analysis. In ICTAI 2013, pages 707--712.

Digital Library

[15]

J. Zhao, X. Huang, and S. Wu. Rewarding term location information to enhance probabilistic information retrieval. In SIGIR'12.

Digital Library

[16]

J. Zhao, X. Huang, and Z. Ye. Modeling term associations for probabilistic information retrieval. ACM Trans. Inf. Syst., 32(2), 2014.

Digital Library

[17]

H.-T. Zheng and et al. Exploiting noun phrases and semantic relationships for text document clustering. Info. Sci., 179(13):2249--2262, 2009.

Digital Library

Cited By

Li XDing LDu YFan YShen F(2022)Position-Enhanced Multi-Head Self-Attention Based Bidirectional Gated Recurrent Unit for Aspect-Level Sentiment ClassificationFrontiers in Psychology10.3389/fpsyg.2021.79992612Online publication date: 25-Jan-2022
https://doi.org/10.3389/fpsyg.2021.799926
Pan MWang JHuang JHuang AChen QChen J(2022)A probabilistic framework for integrating sentence-level semantics via BERT into pseudo-relevance feedbackInformation Processing and Management: an International Journal10.1016/j.ipm.2021.10273459:1Online publication date: 9-Apr-2022
https://dl.acm.org/doi/10.1016/j.ipm.2021.102734
Gu TZhao HLi M(2022)Effective inter-aspect words modeling for aspect-based sentiment analysisApplied Intelligence10.1007/s10489-022-03630-053:4(4366-4379)Online publication date: 8-Jun-2022
https://doi.org/10.1007/s10489-022-03630-0
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Index Terms

Using Term Location Information to Enhance Probabilistic Information Retrieval
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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Published In

cover image ACM Conferences

SIGIR '15: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval

August 2015

1198 pages

ISBN:9781450336215

DOI:10.1145/2766462

General Chair:
Ricardo Baeza-Yates
Yahoo Labs, USA
,
Program Chairs:
Mounia Lalmas
Yahoo Labs, UK
,
Alistair Moffat
University of Melbourne, Australia
,
Berthier Ribeiro-Neto
Google, Brazil, and UFMG, Brazil

Copyright © 2015 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]

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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 August 2015

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SIGIR '15

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SIGIR

SIGIR '15: The 38th International ACM SIGIR conference on research and development in Information Retrieval

August 9 - 13, 2015

Santiago, Chile

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SIGIR '15 Paper Acceptance Rate 70 of 351 submissions, 20%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Li XDing LDu YFan YShen F(2022)Position-Enhanced Multi-Head Self-Attention Based Bidirectional Gated Recurrent Unit for Aspect-Level Sentiment ClassificationFrontiers in Psychology10.3389/fpsyg.2021.79992612Online publication date: 25-Jan-2022
https://doi.org/10.3389/fpsyg.2021.799926
Pan MWang JHuang JHuang AChen QChen J(2022)A probabilistic framework for integrating sentence-level semantics via BERT into pseudo-relevance feedbackInformation Processing and Management: an International Journal10.1016/j.ipm.2021.10273459:1Online publication date: 9-Apr-2022
https://dl.acm.org/doi/10.1016/j.ipm.2021.102734
Gu TZhao HLi M(2022)Effective inter-aspect words modeling for aspect-based sentiment analysisApplied Intelligence10.1007/s10489-022-03630-053:4(4366-4379)Online publication date: 8-Jun-2022
https://doi.org/10.1007/s10489-022-03630-0
Bi MZhang QZuo MXu ZJin Q(2021)Bi-directional Long Short-Term Memory Model with Semantic Positional Attention for the Question Answering SystemACM Transactions on Asian and Low-Resource Language Information Processing10.1145/343980020:5(1-13)Online publication date: 30-Jun-2021
https://dl.acm.org/doi/10.1145/3439800
Muntean CNardini FPerego RTonellotto NFrieder O(2020)Weighting Passages Enhances AccuracyACM Transactions on Information Systems10.1145/342868739:2(1-11)Online publication date: 17-Dec-2020
https://dl.acm.org/doi/10.1145/3428687
Ying ZHuang JZhou J(2019)A New Digital Signal Processing Based Model With Multi-Aspect Term Frequency for Information RetrievalIEEE Access10.1109/ACCESS.2019.29462887(160738-160754)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2946288
Zhou ZLiu FWang Q(2019)R-Transformer Network Based on Position and Self-Attention Mechanism for Aspect-Level Sentiment ClassificationIEEE Access10.1109/ACCESS.2019.29388547(127754-127764)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2938854
Xu YLiu WChen GRen BZhang SGao SGuo J(2019)Enhancing Machine Reading Comprehension With Position InformationIEEE Access10.1109/ACCESS.2019.29304077(141602-141611)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2930407
Zeng JMa XZhou K(2019)Enhancing Attention-based LSTM with Position Context for Aspect-level Sentiment ClassificationIEEE Access10.1109/ACCESS.2019.2893806(1-1)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2893806
Ma XZeng JPeng LFortino GZhang Y(2019)Modeling multi-aspects within one opinionated sentence simultaneously for aspect-level sentiment analysisFuture Generation Computer Systems10.1016/j.future.2018.10.04193(304-311)Online publication date: Apr-2019
https://doi.org/10.1016/j.future.2018.10.041
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