Work

A context pattern induction method for named entity extraction

Year: 2006

Type: conference-paper

Abstract: We present a novel context pattern induction method for information extraction, specifically named entity extraction. Using this method, we extended several classes of seed entity lists into much larger high-precision lists. Using token membership in these extended lists as additional features, we improved the accuracy of a conditional random field-based named entity tagger. In contrast, features derived from the seed lists decreased extractor accuracy.

Source: Conference on Computational Natural Language Learning

DOI: 10.3115/1596276.1596303

Authors: Partha Pratim Talukdar, Thorsten Brants, Mark Liberman, Fernando Pereira

Institutions: University of Pennsylvania, Google (United States)

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funder: National Science Foundation

Award: ITR: Mining the Bibliome -- Information Extraction from the Biomedical Literature