2006 StatisticalEntityTopicModels

(Newman et al., 2006) ⇒ David Newman, Chaitanya Chemudugunta, and Padhraic Smyth. (2006). “Statistical Entity-topic Models.” In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. doi:10.1145/1150402.1150487

Subject Headings: Statistical Topic Modeling Algorithm, Entity Mention Recognition Task, News Corpora, ANNIE System, Lingua EN Tagger, Entity Relation Detection Task.

Notes

It builds Topic Models that account for the different Entity Types in a Corpora.
It Preprocessed the Documents with a Named Entity Recognition Systems to identify the Entity Mentions.
It is unclear if they perform Entity Mention Resolution.
It can predict Entity membership to a Topic and latent structure between entities.

Cited By

Quotes

Author Keywords

entity recognition; text modeling; topic modeling

Abstract

The primary purpose of news articles is to convey information about who, what, when and where. But learning and summarizing these relationships for collections of thousands to millions of articles is difficult. While statistical topic models have been highly successful at topically summarizing huge collections of text documents, they do not explicitly address the textual interactions between who/where, i.e. named entities (persons, organizations, locations) and what, i.e. the topics. We present new graphical models that directly learn the relationship between topics discussed in news articles and entities mentioned in each article. We show how these entity-topic models, through a better understanding of the entity-topic relationships, are better at making predictions about entities.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2006 StatisticalEntityTopicModels	Padhraic Smyth David Newman Chaitanya Chemudugunta			Statistical Entity-topic Models				10.1145/1150402.1150487