Applying Topic Segmentation to Document-Level Information Retrieval-Reference-Cited by-同舟云学术

Applying Topic Segmentation to Document-Level Information Retrieval

Published:2018 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 14th Central and Eastern European Software Engineering Conference Russia on ZZZ - CEE-SECR '18
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Author:

Shtekh Gennady¹,Kazakova Polina²,Nikitinsky Nikita²,Skachkov Nikolay³

Affiliation:

1. National University of Science and Technology MISIS, Moscow, Russia

2. Integrated Systems, Moscow, Russia

3. Lomonosov Moscow State University, Moscow, Russia

Funder

Ministry of Education and Science of the Russian Federation

Publisher

ACM Press

Reference22 articles.

1. Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2016. Enriching word vectors with subword information. arXiv preprint arXiv:1607.04606 (2016).

2. Shing-kai Chan, Lei Xie, and Helen Meng. 2007. Modeling the statistical behavior of lexical chains to capture word cohesiveness for automatic story segmentation. In Eighth Annual Conference of the International Speech Communication Association.

3. Andrew M Dai, Christopher Olah, and Quoc V Le. 2015. Document embedding with paragraph vectors. arXiv preprint arXiv:1507.07998 (2015).

4. Lan Du, Wray Buntine, and Mark Johnson. 2013. Topic segmentation with a structured topic model. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 190--200.

5. Michel Galley, Kathleen R McKeown, Eric Fosler-Lussier, and Hongyan Jing. 2003. Discourse segmentation of multi-party conversation. In Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics.

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