2004 CollectiveInformationExtractionWithRelMarkNets

• (Bunescu & Mooney, 2004) ⇒ Razvan C. Bunescu, Raymond Mooney. (2004). “Collective Information Extraction with Relational Markov Networks.” In: Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL 2004). doi:10.3115/1218955.1219011.

Subject Headings: Information Extraction Algorithm.

Quotes

Abstract

• Most information extraction (IE) systems treat separate potential extractions as independent. However, in many cases, considering influences between different potential extractions could improve overall accuracy. Statistical methods based on undirected graphical models, such as conditional random fields (CRFs), have been shown to be an effective approach to learning accurate IE systems. We present a new IE method that employs Relational Markov Networks (a generalization of CRFs), which can represent arbitrary dependencies between extractions. This allows for "collective information extraction" that exploits the mutual influence between possible extractions. Experiments on learning to extract protein names from biomedical text demonstrate the advantages of this approach.

3. Candidate Entities and Entity Features

• … Definition 1: A base noun phrase is a maximal contiguous sequence of tokens whose POS tags are from {"JJ", "VBN", "VBG", "POS", "NN", "NNS", "NNP", "NNPS", "CD", "–"}, and whose last word (the head) is tagged either as a noun, or a number. Candidate extractions consist of base NPs, augmented with all their contiguous subsequences headed by a noun or number.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2004 CollectiveInformationExtractionWithRelMarkNets	Razvan C. Bunescu Raymond J. Mooney			Collective Information Extraction with Relational Markov Networks		Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics	http://acl.ldc.upenn.edu/P/P04/P04-1056.pdf	10.3115/1218955.1219011		2004