Incorporating Entities in News Topic Modeling

Hu, Linmei; Li, Juanzi; Li, Zhihui; Shao, Chao; Li, Zhixing

doi:10.1007/978-3-642-41644-6_14

Cited by 11 publications

(4 citation statements)

References 13 publications

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“…Our test corpora consists of news-related publiclyavailable datasets: 1) 20 Newsgroups 1 : widely studied by NLP research community dataset (Aletras and Stevenson, 2013;Truica et al, 2016;Wallach et al, 2009;Röder et al, 2015;Hu et al, 2013). Contains 18846 documents with messages discussing news, people, events and other entities.…”

Section: Dataset and Preprocessingmentioning

confidence: 99%

“…It introduces two types of topics, general and entity, and represents word topics as a mixture of entity topics. Hu et al (2013) reverses the concept, assuming that entities are critical for newscentric content. Their entity-centered topic model (ECTM) designs entity topics as a mixture of word topics and shows better results in entity prediction than CorrLDA2 (Hu et al, 2013).…”

Section: Topic Modeling and Named Entitiesmentioning

confidence: 99%

“…Hu et al (2013) reverses the concept, assuming that entities are critical for newscentric content. Their entity-centered topic model (ECTM) designs entity topics as a mixture of word topics and shows better results in entity prediction than CorrLDA2 (Hu et al, 2013). Both models, however, introduce significant changes to the LDA algorithm.…”

Section: Topic Modeling and Named Entitiesmentioning

confidence: 99%

See 2 more Smart Citations

Improving Topic Quality by Promoting Named Entities in Topic Modeling

Krasnashchok¹,

Jouili²

2018

Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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News-related content has been extensively studied in both topic modeling research and named entity recognition. However, expressive power of named entities and their potential for improving the quality of discovered topics has not received much attention. In this paper we use named entities as domain-specific terms for newscentric content and present a new weighting model for Latent Dirichlet Allocation. Our experimental results indicate that involving more named entities in topic descriptors positively influences the overall quality of topics, improving their interpretability, specificity and diversity.

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Section: Dataset and Preprocessingmentioning

confidence: 99%

Section: Topic Modeling and Named Entitiesmentioning

confidence: 99%

Section: Topic Modeling and Named Entitiesmentioning

confidence: 99%

See 1 more Smart Citation

Improving Topic Quality by Promoting Named Entities in Topic Modeling

Krasnashchok¹,

Jouili²

2018

Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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show abstract

“…The study in [10] presents a new method called CorrLDA2 that was derived from LDA, where word topics contain a distribution over words and over entity topics. The authors of [33] present another version of CorrLDA2, The Entity-Centered Topic Model (ECTM), which models entity topics as a mixture of word topics, ECTM differs mainly from CorrLDA2 in the sampling order of entities and words. In [37], topic detection is not performed on the named entities, instead they are able to detect events using topic clustering and named entities together.…”

Section: Hierarchical Topic Detectionmentioning

confidence: 99%