Paper: Embedding Semantic Similarity in Tree Kernels for Domain Adaptation of Relation Extraction

ACL ID P13-1147
Title Embedding Semantic Similarity in Tree Kernels for Domain Adaptation of Relation Extraction
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2013
Authors

Relation Extraction (RE) is the task of extracting semantic relationships between entities in text. Recent studies on rela- tion extraction are mostly supervised. The clear drawback of supervised methods is the need of training data: labeled data is expensive to obtain, and there is often a mismatch between the training data and the data the system will be applied to. This is the problem of domain adapta- tion. In this paper, we propose to combine (i) term generalization approaches such as word clustering and latent semantic anal- ysis (LSA) and (ii) structured kernels to improve the adaptability of relation ex- tractors to new text genres/domains. The empirical evaluation on ACE 2005 do- mains shows that a suitable combination of syntax and lexical generalization is very promising for dom...