A Systematic Exploration of the Feature Space for Relation Extraction

Relation extraction is the task of finding semantic relations between entities from text. The state-of-the-art methods for relation extraction are mostly based on statistical learning, and thus all have to deal with feature selection, which can significantly affect the classification performance. In...

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Main Authors: JIANG, Jing, ZHAI, ChengXiang
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Language:English
Published: Institutional Knowledge at Singapore Management University 2007
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Online Access:https://ink.library.smu.edu.sg/sis_research/1254
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spelling sg-smu-ink.sis_research-22532010-12-22T08:24:06Z A Systematic Exploration of the Feature Space for Relation Extraction JIANG, Jing ZHAI, ChengXiang Relation extraction is the task of finding semantic relations between entities from text. The state-of-the-art methods for relation extraction are mostly based on statistical learning, and thus all have to deal with feature selection, which can significantly affect the classification performance. In this paper, we systematically explore a large space of features for relation extraction and evaluate the effectiveness of different feature subspaces. We present a general definition of feature spaces based on a graphic representation of relation instances, and explore three different representations of relation instances and features of different complexities within this framework. Our experiments show that using only basic unit features is generally sufficient to achieve state-of-the-art performance, while overinclusion of complex features may hurt the performance. A combination of features of different levels of complexity and from different sentence representations, coupled with task-oriented feature pruning, gives the best performance. 2007-04-01T07:00:00Z text https://ink.library.smu.edu.sg/sis_research/1254 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems Numerical Analysis and Scientific Computing
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Databases and Information Systems
Numerical Analysis and Scientific Computing
spellingShingle Databases and Information Systems
Numerical Analysis and Scientific Computing
JIANG, Jing
ZHAI, ChengXiang
A Systematic Exploration of the Feature Space for Relation Extraction
description Relation extraction is the task of finding semantic relations between entities from text. The state-of-the-art methods for relation extraction are mostly based on statistical learning, and thus all have to deal with feature selection, which can significantly affect the classification performance. In this paper, we systematically explore a large space of features for relation extraction and evaluate the effectiveness of different feature subspaces. We present a general definition of feature spaces based on a graphic representation of relation instances, and explore three different representations of relation instances and features of different complexities within this framework. Our experiments show that using only basic unit features is generally sufficient to achieve state-of-the-art performance, while overinclusion of complex features may hurt the performance. A combination of features of different levels of complexity and from different sentence representations, coupled with task-oriented feature pruning, gives the best performance.
format text
author JIANG, Jing
ZHAI, ChengXiang
author_facet JIANG, Jing
ZHAI, ChengXiang
author_sort JIANG, Jing
title A Systematic Exploration of the Feature Space for Relation Extraction
title_short A Systematic Exploration of the Feature Space for Relation Extraction
title_full A Systematic Exploration of the Feature Space for Relation Extraction
title_fullStr A Systematic Exploration of the Feature Space for Relation Extraction
title_full_unstemmed A Systematic Exploration of the Feature Space for Relation Extraction
title_sort systematic exploration of the feature space for relation extraction
publisher Institutional Knowledge at Singapore Management University
publishDate 2007
url https://ink.library.smu.edu.sg/sis_research/1254
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