Article

Capturing term dependencies using a language model based on sentence trees

Authors:

Ramesh Nallapati,

James AllanAuthors Info & Claims

CIKM '02: Proceedings of the eleventh international conference on Information and knowledge management

Pages 383 - 390

https://doi.org/10.1145/584792.584855

Published: 04 November 2002 Publication History

Abstract

We describe a new probabilistic Sentence Tree Language Modeling approach that captures term dependency patterns in Topic Detection and Tracking's (TDT) Story Link Detection task. New features of the approach include modeling the syntactic structure of sentences in documents by a sentence-bin approach and a computationally efficient algorithm for capturing the most significant sentence-level term dependencies using a Maximum Spanning Tree approach, similar to Van Rijsbergen's modeling of document-level term dependencies.The new model is a good discriminator of on-topic and off-topic story pairs providing evidence that sentence-level term dependencies contain significant information about relevance. Although runs on a subset of the TDT2 corpus show that the model is outperformed by the unigram language model, a mixture of the unigram and the Sentence Tree models is shown to improve on the best performance especially in the regions of low false alarms.

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Index Terms

Capturing term dependencies using a language model based on sentence trees
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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Published In

cover image ACM Conferences

CIKM '02: Proceedings of the eleventh international conference on Information and knowledge management

November 2002

704 pages

ISBN:1581134924

DOI:10.1145/584792

General Chair:
Charles Nicholas
University of Maryland Baltimore County
,
Program Chairs:
David Grossman
Illinois Institute of Technology
,
Konstantinos Kalpakis
University of Maryland Baltimore County
,
Sajda Qureshi
Erasmus University, Rotterdam
,
Han van Dissel
Erasmus University, Rotterdam
,
Len Seligman
The MITRE Corporation

Copyright © 2002 ACM.

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Published: 04 November 2002

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November 4 - 9, 2002

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Cited By

Hambarde KProença H(2023)Information Retrieval: Recent Advances and BeyondIEEE Access10.1109/ACCESS.2023.329577611(76581-76604)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3295776
Guo SZhang G(2022)Comparisons of the Economist Topics on Three Countries from 1991 Through 2016Libri10.1515/libri-2022-002673:1(37-50)Online publication date: 30-Sep-2022
https://doi.org/10.1515/libri-2022-0026
Alma’aitah WTalib AOsman M(2021)Towards adaptive structured Dirichlet smoothing model for digital resource objectsMultimedia Tools and Applications10.1007/s11042-020-10305-wOnline publication date: 9-Jan-2021
https://doi.org/10.1007/s11042-020-10305-w
Büschken JAllenby G(2020)Improving Text Analysis Using Sentence Conjunctions and PunctuationMarketing Science10.1287/mksc.2019.121439:4(727-742)Online publication date: 1-Jul-2020
https://dl.acm.org/doi/10.1287/mksc.2019.1214
Yang WHan GXie X(2018)A new text representation method for clustering based on higher order Markov modelProceedings of the 2nd International Conference on Information System and Data Mining10.1145/3206098.3206099(1-6)Online publication date: 9-Apr-2018
https://dl.acm.org/doi/10.1145/3206098.3206099
Eickhoff Cde Vries A(2016)"Robust statistical methods in web retrieval" by Carsten Eickhoff and Arjen P. de Vries, with Martin Vesely as coordinatorACM SIGWEB Newsletter10.1145/2857659.28576632016:Winter(1-11)Online publication date: 11-Jan-2016
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Lee CYang HCheng STsai S(2015)A Hybrid Big Data Analytics Method for Yield Improvement in Semiconductor ManufacturingProceedings of the ASE BigData & SocialInformatics 201510.1145/2818869.2818934(1-4)Online publication date: 7-Oct-2015
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Eickhoff Cde Vries AHofmann TBaeza-Yates RLalmas MMoffat ARibeiro-Neto B(2015)Modelling Term Dependence with CopulasProceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/2766462.2767831(783-786)Online publication date: 9-Aug-2015
https://dl.acm.org/doi/10.1145/2766462.2767831
Lioma CSimonsen JLarsen BHansen NBaeza-Yates RLalmas MMoffat ARibeiro-Neto B(2015)Non-Compositional Term Dependence for Information RetrievalProceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/2766462.2767717(595-604)Online publication date: 9-Aug-2015
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Song DShi YZhang PHuang QKruschwitz UHou YWang B(2015)Incorporating Intra-Query Term Dependencies in an Aspect Query Language ModelComputational Intelligence10.1111/coin.1205831:4(699-720)Online publication date: 1-Nov-2015
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