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Probabilistic hyperspace analogue to language

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SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval

Pages 575 - 576

https://doi.org/10.1145/1076034.1076135

Published: 15 August 2005 Publication History

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Abstract

Song and Bruza [6] introduce a framework for Information Retrieval(IR) based on Gardenfor's three tiered cognitive model; Conceptual Spaces[4]. They instantiate a conceptual space using Hyperspace Analogue to Language (HAL[3] to generate higher order concepts which are later used for ad-hoc retrieval. In this poster, we propose an alternative implementation of the conceptual space by using a probabilistic HAL space (pHAL). To evaluate whether converting to such an implementation is beneficial we have performed an initial investigation comparing the concept combination of HAL against pHAL for the task of query expansion. Our experiments indicate that pHAL outperforms the original HAL method and that better query term selection methods can improve performance on both HAL and pHAL.

References

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[2]

P. D. Bruza and D. Song. A comparison of various approaches for using probabilistic dependencies in language modelling. In The 26th ACM SIGIR, pages 419--420. ACM Press, 2003.

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B. C., K. Livesay, and K. Lund. Explorations in context space: Words, sentences, discourse. Discourse Processes, 25(2-3):211--257, 1998.

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P. Gardenfors. Conceptual Spaces: The Geometry of Thought. MIT Press, 2000.

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W. Lowe and S. McDonald. The direct route: Priming in semantic space. In Proceedings of the Seventeen Annual Meeting of the Cognitive Science Society, pages 600--665, 2000.

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D. Song and P. D. Bruza. Discovering information flow using a high dimensional conceptual space. In The 24th ACM SIGIR, pages 327--333, New Orleans, LO, 2001.

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C. Zhai and J. Lafferty. A study of smoothing methods for language models applied to ad hoc information retrieval. In The 24th ACM SIGIR, pages 49--56, New Orleans, LO, 2001.

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Cited By

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Daoud M(2023)Similarity Detection of Time-Sensitive Online News Articles Based on RSS Feeds and Contextual DataAnnals of Emerging Technologies in Computing10.33166/AETiC.2023.01.0067:1(75-88)Online publication date: 1-Jan-2023
https://doi.org/10.33166/AETiC.2023.01.006
Liu WLi FPei SCheng C(2023)MOID: Many-to-One Patent Graph Embedding Base Infringement Detection ModelInternational Journal of Software Engineering and Knowledge Engineering10.1142/S021819402342001934:03(449-465)Online publication date: 28-Dec-2023
https://doi.org/10.1142/S0218194023420019
Maisto A(2022)Extract Similarities from Syntactic Contexts: a Distributional Semantic Model Based on Syntactic DistanceItalian Journal of Computational Linguistics10.4000/ijcol.10868:2Online publication date: 1-Dec-2022
https://doi.org/10.4000/ijcol.1086
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Index Terms

Probabilistic hyperspace analogue to language
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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Published In

SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval

August 2005

708 pages

ISBN:1595930345

DOI:10.1145/1076034

General Chairs:
Ricardo Baeza-Yates
University of Chile, Chile
,
Nivio Ziviani
Federal University of Minas Gerais, Brazil
,
Program Chairs:
Gary Marchionini
University of North Carolina, USA
,
Alistair Moffat
University of Melbourne, Australia
,
John Tait
University of Sunderland, UK

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 August 2005

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SIGIR

SIGIR05: The 28th ACM/SIGIR International Symposium on Information Retrieval 2005

August 15 - 19, 2005

Salvador, Brazil

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

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Cited By

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Daoud M(2023)Similarity Detection of Time-Sensitive Online News Articles Based on RSS Feeds and Contextual DataAnnals of Emerging Technologies in Computing10.33166/AETiC.2023.01.0067:1(75-88)Online publication date: 1-Jan-2023
https://doi.org/10.33166/AETiC.2023.01.006
Liu WLi FPei SCheng C(2023)MOID: Many-to-One Patent Graph Embedding Base Infringement Detection ModelInternational Journal of Software Engineering and Knowledge Engineering10.1142/S021819402342001934:03(449-465)Online publication date: 28-Dec-2023
https://doi.org/10.1142/S0218194023420019
Maisto A(2022)Extract Similarities from Syntactic Contexts: a Distributional Semantic Model Based on Syntactic DistanceItalian Journal of Computational Linguistics10.4000/ijcol.10868:2Online publication date: 1-Dec-2022
https://doi.org/10.4000/ijcol.1086
Rayala USeshadri K(2021)Word Embedding Techniques for Sentiment AnalyzersNew Opportunities for Sentiment Analysis and Information Processing10.4018/978-1-7998-8061-5.ch013(233-252)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-8061-5.ch013
Daoud M(2019)Novel Approach towards Arabic Question Similarity Detection2019 2nd International Conference on new Trends in Computing Sciences (ICTCS)10.1109/ICTCS.2019.8923102(1-6)Online publication date: Oct-2019
https://doi.org/10.1109/ICTCS.2019.8923102
Hourrane OMifrah SBenlahmar EBouhriz NRachdi M(2018)Using Deep Learning Word Embeddings for Citations Similarity in Academic PapersBig Data, Cloud and Applications10.1007/978-3-319-96292-4_15(185-196)Online publication date: 14-Aug-2018
https://doi.org/10.1007/978-3-319-96292-4_15
Zuccon GKoopman BBruza PAzzopardi L(2015)Integrating and Evaluating Neural Word Embeddings in Information RetrievalProceedings of the 20th Australasian Document Computing Symposium10.1145/2838931.2838936(1-8)Online publication date: 8-Dec-2015
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Hoenkamp EBruza P(2015)How everyday language can and will boost effective information retrievalJournal of the Association for Information Science and Technology10.1002/asi.2327966:8(1546-1558)Online publication date: 1-Aug-2015
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Xu TPeng Q(2012)Extended information inference model for unsupervised categorization of web short textsJournal of Information Science10.1177/016555151244898538:6(512-531)Online publication date: 15-Oct-2012
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Hoenkamp EBruza PSong DHuang Q(2009)An Effective Approach to Verbose Queries Using a Limited Dependencies Language ModelProceedings of the 2nd International Conference on Theory of Information Retrieval: Advances in Information Retrieval Theory10.1007/978-3-642-04417-5_11(116-127)Online publication date: 3-Sep-2009
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