@inproceedings{amoualian-etal-2017-topical,
title = "Topical Coherence in {LDA}-based Models through Induced Segmentation",
author = "Amoualian, Hesam and
Lu, Wei and
Gaussier, Eric and
Balikas, Georgios and
Amini, Massih R. and
Clausel, Marianne",
editor = "Barzilay, Regina and
Kan, Min-Yen",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-1165",
doi = "10.18653/v1/P17-1165",
pages = "1799--1809",
abstract = "This paper presents an LDA-based model that generates topically coherent segments within documents by jointly segmenting documents and assigning topics to their words. The coherence between topics is ensured through a copula, binding the topics associated to the words of a segment. In addition, this model relies on both document and segment specific topic distributions so as to capture fine grained differences in topic assignments. We show that the proposed model naturally encompasses other state-of-the-art LDA-based models designed for similar tasks. Furthermore, our experiments, conducted on six different publicly available datasets, show the effectiveness of our model in terms of perplexity, Normalized Pointwise Mutual Information, which captures the coherence between the generated topics, and the Micro F1 measure for text classification.",
}
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<abstract>This paper presents an LDA-based model that generates topically coherent segments within documents by jointly segmenting documents and assigning topics to their words. The coherence between topics is ensured through a copula, binding the topics associated to the words of a segment. In addition, this model relies on both document and segment specific topic distributions so as to capture fine grained differences in topic assignments. We show that the proposed model naturally encompasses other state-of-the-art LDA-based models designed for similar tasks. Furthermore, our experiments, conducted on six different publicly available datasets, show the effectiveness of our model in terms of perplexity, Normalized Pointwise Mutual Information, which captures the coherence between the generated topics, and the Micro F1 measure for text classification.</abstract>
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%0 Conference Proceedings
%T Topical Coherence in LDA-based Models through Induced Segmentation
%A Amoualian, Hesam
%A Lu, Wei
%A Gaussier, Eric
%A Balikas, Georgios
%A Amini, Massih R.
%A Clausel, Marianne
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F amoualian-etal-2017-topical
%X This paper presents an LDA-based model that generates topically coherent segments within documents by jointly segmenting documents and assigning topics to their words. The coherence between topics is ensured through a copula, binding the topics associated to the words of a segment. In addition, this model relies on both document and segment specific topic distributions so as to capture fine grained differences in topic assignments. We show that the proposed model naturally encompasses other state-of-the-art LDA-based models designed for similar tasks. Furthermore, our experiments, conducted on six different publicly available datasets, show the effectiveness of our model in terms of perplexity, Normalized Pointwise Mutual Information, which captures the coherence between the generated topics, and the Micro F1 measure for text classification.
%R 10.18653/v1/P17-1165
%U https://aclanthology.org/P17-1165
%U https://doi.org/10.18653/v1/P17-1165
%P 1799-1809
Markdown (Informal)
[Topical Coherence in LDA-based Models through Induced Segmentation](https://aclanthology.org/P17-1165) (Amoualian et al., ACL 2017)
ACL
- Hesam Amoualian, Wei Lu, Eric Gaussier, Georgios Balikas, Massih R. Amini, and Marianne Clausel. 2017. Topical Coherence in LDA-based Models through Induced Segmentation. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1799–1809, Vancouver, Canada. Association for Computational Linguistics.