A Probabilistic Topic Model based on an Arbitrary-Length Co-occurrence Window

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Rahimi Marziea; ZAHEDI MORTEZA; Mashayekhi Hoda

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Journal Paper

Paper Information

Journal: International Journal of Information and Communication Technology Research Year:2017 | Volume:9 | Issue:2 Page(s): 19-25

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Title

A Probabilistic Topic Model based on an Arbitrary-Length Co-occurrence Window

Author(s)

Rahimi Marziea | ZAHEDI MORTEZA | Mashayekhi Hoda | Issue Writer Certificate

Keywords

probabilistic topic modeling

co-occurrence

context window

Gibbs sampling

generative models

Abstract

Probabilistic topic models have been very popular in automatic text analysis since their introduction. These models work based on word co-occurrence, but are not very flexible with respect to the context in which cooccurrence is considered. Many probabilistic topic models do not allow for taking local or spatial data into account. In this paper, we introduce a probabilistic topic model that benefits from an arbitrary-length co-occurrence window and encodes local word dependencies for extracting topics. We assume a multinomial distribution with Dirichlet prior over the window positions to let the words in every position have a chance to influence topic assignments. In the proposed model, topics being shown by word pairs have a more meaningful presentation. The model is applied on a dataset of 2000 documents. The proposed model produces interesting meaningful topics and reduces the problem of sparseness.

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APA: Copy

Rahimi, Marziea, ZAHEDI, MORTEZA, & Mashayekhi, Hoda. (2017). A Probabilistic Topic Model based on an Arbitrary-Length Co-occurrence Window. INTERNATIONAL JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGY RESEARCH, 9(2), 19-25. SID. https://sid.ir/paper/315196/en

Vancouver: Copy

Rahimi Marziea, ZAHEDI MORTEZA, Mashayekhi Hoda. A Probabilistic Topic Model based on an Arbitrary-Length Co-occurrence Window. INTERNATIONAL JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGY RESEARCH[Internet]. 2017;9(2):19-25. Available from: https://sid.ir/paper/315196/en

IEEE: Copy

Marziea Rahimi, MORTEZA ZAHEDI, and Hoda Mashayekhi, “A Probabilistic Topic Model based on an Arbitrary-Length Co-occurrence Window,” INTERNATIONAL JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGY RESEARCH, vol. 9, no. 2, pp. 19–25, 2017, [Online]. Available: https://sid.ir/paper/315196/en

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