o-HETM: An Online Hierarchical Entity Topic Model for News Streams

Hu, Linmei; Li, Juanzi; Zhang, Jing; Shao, Chao

doi:10.1007/978-3-319-18038-0_54

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…However, their focus is on sequential topic flows of entities and entity groups in a single document (Jeong and Choi 2012) or on dynamic topic hierarchies and timeliness of news data (Hu et al . 2015). Our task and our focus are not on the dynamics of topics.…”

Section: Methodsmentioning

confidence: 99%

“…Beyond the baselines mentioned, there is a growing body of work on topic models that involve entities (Jeong and Choi 2012). However, their focus is on sequential topic flows of entities and entity groups in a single document (Jeong and Choi 2012) or on dynamic topic hierarchies and timeliness of news data (Hu et al 2015). Our task and our focus are not on the dynamics of topics.…”

Section: Baselines and Parameter Settingsmentioning

confidence: 99%

See 1 more Smart Citation

It all starts with entities: A Salient entity topic model

Kanoulas

Rijke

2019

Nat. Lang. Eng.

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Entities play an essential role in understanding textual documents, regardless of whether the documents are short, such as tweets, or long, such as news articles. In short textual documents, all entities mentioned are usually considered equally important because of the limited amount of information. In long textual documents, however, not all entities are equally important: some are salient and others are not. Traditional entity topic models (ETMs) focus on ways to incorporate entity information into topic models to better explain the generative process of documents. However, entities are usually treated equally, without considering whether they are salient or not. In this work, we propose a novel ETM, Salient Entity Topic Model, to take salient entities into consideration in the document generation process. In particular, we model salient entities as a source of topics used to generate words in documents, in addition to the topic distribution of documents used in traditional topic models. Qualitative and quantitative analysis is performed on the proposed model. Application to entity salience detection demonstrates the effectiveness of our model compared to the state-of-the-art topic model baselines.

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Section: Methodsmentioning

confidence: 99%

Section: Baselines and Parameter Settingsmentioning

confidence: 99%

It all starts with entities: A Salient entity topic model

Kanoulas

Rijke

2019

Nat. Lang. Eng.

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“…Existing work mainly focused on learning topic hierarchies from texts only and used traditional hierarchical clustering methods (Chuang and Chien, 2004) or topic models such as HLDA (Griffiths and Tenenbaum, 2004), HPAM (Mimno et al, 2007), hHDP (Zavitsanos et al, 2011), and HETM (Hu et al, 2015). Differently, we focus on structured contents tables with corresponding text descriptions.…”

Section: Related Workmentioning

confidence: 99%

Learning Topic Hierarchies for Wikipedia Categories

Wang²,

Zhang³

et al. 2015

Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Confere

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Existing studies have utilized Wikipedia for various knowledge acquisition tasks. However, no attempts have been made to explore multi-level topic knowledge contained in Wikipedia articles' Contents tables. The articles with similar subjects are grouped together into Wikipedia categories. In this work, we propose novel methods to automatically construct comprehensive topic hierarchies for given categories based on the structured Contents tables as well as corresponding unstructured text descriptions. Such a hierarchy is important for information browsing, document organization and topic prediction. Experimental results show our proposed approach, incorporating both the structural and textual information, achieves high quality category topic hierarchies.

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“…Though a lot of research has been conducted on events [6][7][8] and topic hierarchy construction from events [9][10][11][12][13][14][15], it remains unknown how to incrementally learn from similar news events in order to acquire a comprehensive topic hierarchy representing the knowledge about the subject of the events. An obvious approach is to directly apply existing topic hierarchy construction methods such as hierarchical topic models on the news articles of all the events.…”

Section: Introductionmentioning

confidence: 99%