2016 AutomaticEntityRecognitionandTy

• (Ren et al., 2016) ⇒ Xiang Ren, Ahmed El-Kishky, Heng Ji, and Jiawei Han. (2016). “Automatic Entity Recognition and Typing in Massive Text Data.” In: Proceedings of the 2016 International Conference on Management of Data. ISBN:978-1-4503-3531-7 doi:10.1145/2882903.2912567

Subject Headings: Semi-Supervised NER.

Cited By

• http://scholar.google.com/scholar?q=%222016%22+Automatic+Entity+Recognition+and+Typing+in+Massive+Text+Data
• http://dl.acm.org/citation.cfm?id=2882903.2912567&preflayout=flat#citedby

Quotes

Abstract

In today's computerized and information-based society, individuals are constantly presented with vast amounts of text data, ranging from news articles, scientific publications, product reviews, to a wide range of textual information from social media. To extract value from these large, multi-domain pools of text, it is of great importance to gain an understanding of entities and their relationships. In this tutorial, we introduce data-driven methods to recognize typed entities of interest in massive, domain-specific text corpora. These methods can automatically identify token spans as entity mentions in documents and label their fine-grained types (e.g., people, product and food) in a scalable way. Since these methods do not rely on annotated data, predefined typing schema or hand-crafted features, they can be quickly adapted to a new domain, genre and language. We demonstrate on real datasets including various genres (e.g., news articles, discussion forum posts, and tweets), domains (general vs. bio-medical domains) and languages (e.g., English, Chinese, Arabic, and even low-resource languages like Hausa and Yoruba) how these typed entities aid in knowledge discovery and management.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2016 AutomaticEntityRecognitionandTy	Heng Ji Xiang Ren Ahmed El-Kishky Jiawei Han			Automatic Entity Recognition and Typing in Massive Text Data				10.1145/2882903.2912567		2016