Longhua Qian
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Abstract
This article proposes a new approach to dynamically determine the tree span for tree kernel-based semantic relation extraction between named entities. The basic idea is to employ constituent dependency information in keeping the necessary nodes and their head children along the path connecting the two entities in the syntactic parse tree, while removing the noisy information from the tree, eventually leading to a dynamic syntactic parse tree. This article also explores various entity features and their possible combinations via a unified syntactic and semantic tree framework, which integrates both structural syntactic parse information and entity-related semantic information. Evaluation on the ACE RDC 2004 English and 2005 Chinese benchmark corpora shows that our dynamic syntactic parse tree much outperforms all previous tree spans, indicating its effectiveness in well representing the structural nature of relation instances while removing redundant information. Moreover, the unified parse and semantic tree significantly outperforms the single syntactic parse tree, largely due to the remarkable contributions from entity-related semantic features such as its type, subtype, mention-level as well as their bi-gram combinations. Finally, the best performance so far in semantic relation extraction is achieved via a composite kernel, which combines this tree kernel with a linear, state-of-the-art, feature-based kernel.
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Index Terms
- Employing Constituent Dependency Information for Tree Kernel-Based Semantic Relation Extraction between Named Entities
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