2013 MiningEvidencesforNamedEntityDi

• (Li et al., 2013) ⇒ Yang Li, Chi Wang, Fangqiu Han, Jiawei Han, Dan Roth, and Xifeng Yan. (2013). “Mining Evidences for Named Entity Disambiguation.” In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ISBN:978-1-4503-2174-7 doi:10.1145/2487575.2487681

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222013%22+Mining+Evidences+for+Named+Entity+Disambiguation
• http://dl.acm.org/citation.cfm?id=2487575.2487681&preflayout=flat#citedby

Quotes

Author Keywords

• Entity disambiguation; evidence mining; general; generative model; knowledge expansion; semi-supervised learning

Abstract

Named entity disambiguation is the task of disambiguating named entity mentions in natural language text and link them to their corresponding entries in a knowledge base such as Wikipedia. Such disambiguation can help enhance readability and add semantics to plain text. It is also a central step in constructing high-quality information network or knowledge graph from unstructured text. Previous research has tackled this problem by making use of various textual and structural features from a knowledge base. Most of the proposed algorithms assume that a knowledge base can provide enough explicit and useful information to help disambiguate a mention to the right entity. However, the existing knowledge bases are rarely complete (likely will never be), thus leading to poor performance on short queries with not well-known contexts. In such cases, we need to collect additional evidences scattered in internal and external corpus to augment the knowledge bases and enhance their disambiguation power. In this work, we propose a generative model and an incremental algorithm to automatically mine useful evidences across documents. With a specific modeling of “background topic” and “unknown entities", our model is able to harvest useful evidences out of noisy information. Experimental results show that our proposed method outperforms the state-of-the-art approaches significantly: boosting the disambiguation accuracy from 43% (baseline) to 86% on short queries derived from tweets.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2013 MiningEvidencesforNamedEntityDi	Xifeng Yan Chi Wang Yang Li Fangqiu Han Dan Roth Jiawei Han			Mining Evidences for Named Entity Disambiguation				10.1145/2487575.2487681		2013