Tree Kernel-Based Semantic Relation Extraction Using Unified Dynamic Relation Tree

This paper proposes a unified dynamic relation tree (DRT) span for tree kernel-based semantic relation extraction between entity names. The basic idea is to apply a variety of linguistics-driven rules to dynamically prune out noisy information from a syntactic parse tree and include necessary contex...

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Bibliographic Details
Main Authors: Longhua Qian, Guodong Zhou, Fang Kong, Qiaomin Zhu, Peide Qian
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Cities and towns
Data mining
Feature extraction
Joining processes
Kernel
Noise measurement
semantic relation extraction
tree-kernel based methods
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Summary:This paper proposes a unified dynamic relation tree (DRT) span for tree kernel-based semantic relation extraction between entity names. The basic idea is to apply a variety of linguistics-driven rules to dynamically prune out noisy information from a syntactic parse tree and include necessary contextual information. In addition, different kinds of entity-related semantic information are unified into the syntactic parse tree. Evaluation on the ACE RDC 2004 corpus shows that the unified DRT span outperforms other widely-used tree spans, and our system achieves comparable performance with the state-of-the-art kernel-based ones. This indicates that our method can not only well model the structured syntactic information but also effectively capture entity-related semantic information.
DOI:10.1109/ALPIT.2008.26