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A novel topic model for automatic term extraction

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Baobao ChangAuthors Info & Claims

SIGIR '13: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval

Pages 885 - 888

https://doi.org/10.1145/2484028.2484106

Published: 28 July 2013 Publication History

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Abstract

Automatic term extraction (ATE) aims at extracting domain-specific terms from a corpus of a certain domain. Termhood is one essential measure for judging whether a phrase is a term. Previous researches on termhood mainly depend on the word frequency information. In this paper, we propose to compute termhood based on semantic representation of words. A novel topic model, namely i-SWB, is developed to map the domain corpus into a latent semantic space, which is composed of some general topics, a background topic and a documents-specific topic. Experiments on four domains demonstrate that our approach outperforms the state-of-the-art ATE approaches.

References

[1]

Blei, D. M., Ng, A. Y., Jordan, M.I. 2003. Latent Dirichlet allocation, The Journal of Machine Learning Research, pp. 993--1022.

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[2]

Chang J.-S. 2005. Domain specific word extraction from hierarchical web documents: a first step toward building lexicon trees from web corpora. In Proceedings of the 4th SIGHAN Workshop on Chinese Language Learning: 64--71.

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[3]

Chemudugunta, C., Smyth, P. and Steyvers, M. 2006. Modeling general and specific aspects of documents with a probabilistic topic model. In NIPS 19, pp. 241--248.

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[4]

Hisamitsu, T., Niwa, Y., and Tsujii, J. 2000. A method of measuring term representativeness - baseline method using co-occurrence distribution, COLING 2000, pp.320--326.

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[5]

Frantzi, K., Ananiadou, S. and Mima, H. 2000. Automatic recognition of multi-word terms: the C-value/NC-value method. International Journal of Digital Libraries, 3(2): 117--132

Crossref

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Kageura, K. and Umino, B. 1996. Methods of automatic term recognition. Terminology, 3(2).

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Li FTian MWen Y(2024)MOOC Concept Extraction from Chinese Texts: A Rule and Graph Propagation Based Method2024 11th International Conference on Behavioural and Social Computing (BESC)10.1109/BESC64747.2024.10780751(1-7)Online publication date: 16-Aug-2024
https://doi.org/10.1109/BESC64747.2024.10780751
Shao WHuang LLiu SMa SSong L(2022)Towards Better Understanding with Uniformity and Explicit Regularization of Embeddings in Embedding-based Neural Topic Models2022 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN55064.2022.9892128(1-9)Online publication date: 18-Jul-2022
https://doi.org/10.1109/IJCNN55064.2022.9892128
Nugumanova AAkhmed-Zaki DMansurova MBaiburin YMaulit A(2022)NMF-based approach to automatic term extractionExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.117179199:COnline publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1016/j.eswa.2022.117179
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Index Terms

A novel topic model for automatic term extraction
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
2. Information systems
  1. Information retrieval
    1. Document representation
      1. Content analysis and feature selection

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Published In

SIGIR '13: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval

July 2013

1188 pages

ISBN:9781450320344

DOI:10.1145/2484028

General Chairs:
Gareth J.F. Jones
Dublin City University, Ireland
,
Páraic Sheridan
Dublin City University, Ireland
,
Program Chairs:
Diane Kelly
University of North Carolina, Chapel Hill, USA
,
Maarten de Rijke
University of Amsterdam, The Netherlands
,
Tetsuya Sakai
Microsoft Research Asia, China

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Association for Computing Machinery

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Published: 28 July 2013

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SIGIR '13: The 36th International ACM SIGIR conference on research and development in Information Retrieval

July 28 - August 1, 2013

Dublin, Ireland

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SIGIR '13 Paper Acceptance Rate 73 of 366 submissions, 20%;

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

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Li FTian MWen Y(2024)MOOC Concept Extraction from Chinese Texts: A Rule and Graph Propagation Based Method2024 11th International Conference on Behavioural and Social Computing (BESC)10.1109/BESC64747.2024.10780751(1-7)Online publication date: 16-Aug-2024
https://doi.org/10.1109/BESC64747.2024.10780751
Shao WHuang LLiu SMa SSong L(2022)Towards Better Understanding with Uniformity and Explicit Regularization of Embeddings in Embedding-based Neural Topic Models2022 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN55064.2022.9892128(1-9)Online publication date: 18-Jul-2022
https://doi.org/10.1109/IJCNN55064.2022.9892128
Nugumanova AAkhmed-Zaki DMansurova MBaiburin YMaulit A(2022)NMF-based approach to automatic term extractionExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.117179199:COnline publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1016/j.eswa.2022.117179
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Risch JKrestel RChen JGonçalves MAllen JFox EKan MPetras V(2018)My Approach = Your Apparatus?Proceedings of the 18th ACM/IEEE on Joint Conference on Digital Libraries10.1145/3197026.3197038(283-292)Online publication date: 23-May-2018
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Research on Multi-document Summarization Based on LDA Topic Model

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