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

TY - GEN

T1 - Embedding Semantic Similarity in Tree Kernels for Domain Adaptation of Relation Extraction

AU - Plank, Barbara

AU - Moschitti, Alessandro

PY - 2013

Y1 - 2013

N2 - Relation Extraction (RE) is the task of extracting semantic relationships between entities in text. Recent studies on relation 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 adaptation. In this paper, we propose to combine (i) term generalization approaches such as word clustering and latent semantic analysis (LSA) and (ii) structured kernels to improve the adaptability of relation extractors to new text genres/domains. The empirical evaluation on ACE 2005 domains shows that a suitable combination of syntax and lexical generalization is very promising for domain adaptation.

AB - Relation Extraction (RE) is the task of extracting semantic relationships between entities in text. Recent studies on relation 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 adaptation. In this paper, we propose to combine (i) term generalization approaches such as word clustering and latent semantic analysis (LSA) and (ii) structured kernels to improve the adaptability of relation extractors to new text genres/domains. The empirical evaluation on ACE 2005 domains shows that a suitable combination of syntax and lexical generalization is very promising for domain adaptation.

M3 - Article in proceedings

SP - 1498

EP - 1507

BT - Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL)

PB - Association for Computational Linguistics

ER -