Acquiring Information from Wider Scope to Improve Event Extraction

Abstract

Event extraction is a particularly challenging type of information extraction (IE). Most current event extraction systems rely on local information at the phrase or sentence level. However, this local context may be insufficient to resolve ambiguities in identifying particular types of events; information from a wider scope can serve to resolve some of these ambiguities. In this thesis, we first investigate how to extract supervised and unsupervised features to improve a supervised baseline system. Then, we present two additional tasks to show the benefit of wider scope features in semi-supervised learning (self training) and active learning (co-testing). Experiments show that using features from wider scope can not only aid a supervised local event extraction baseline system, but also help the semi-supervised or active learning approach.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 2012
Accession Number
ADA563084

Entities

People

  • Shasha Liao

Organizations

  • New York University

Tags

Communities of Interest

  • Human Systems
  • Weapons Technologies

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Artificial Intelligence Software
  • Automata Theory
  • Bayesian Networks
  • Computational Linguistics
  • Computational Science
  • Computer Languages
  • Information Processing
  • Information Science
  • Machine Learning
  • Monte Carlo Method
  • Named Entity Recognition
  • Natural Language Processing
  • Natural Languages
  • Network Science
  • Semi-Supervised Learning
  • Supervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Computational Linguistics
  • Neural Network Machine Learning.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Information Retrieval
  • AI & ML - Neural Networks