Semi-Supervised Learning for Relation Extraction

Semi-Supervised Learning for Relation Extraction ZHOU GuoDong LI JunHui QIAN LongHua ZHU Qiaoming Jiangsu Provincial Key Lab for Computer Information Processing Technology School of Computer Science and Technology Soochow Univ., Suzhou, China 215006 Email : {gdzhou, lijunhui, qianlonghua, qmzhu}@suda.edu.cn Abstract This paper proposes a semi-supervised learn- ing method for relation extraction. Given a small amount of labeled data and a large amount of unlabeled data, it first bootstraps a moderate number of weighted support vectors via SVM through a co-training procedure with random feature projection and then applies a label propagation (LP) algorithm via the boot- strapped support vectors. Evaluation on the ACE RDC 2003 corpus shows that our method outperforms the normal LP algorithm via all the available labeled data without SVM boot- strapping. Moreover, our method can largely reduce the computational burden. This sug- gests that our proposed method can integrate the advantages of both SVM bootstrapping and label propagation. 1 Introduction Relation extraction is to detect and classify various predefined semantic relations between two entities from text and can be very useful in many NLP ap- plications such as question answering, e.g. to an- swer the query “Who is the president of the United States?”, and information retrieval, e.g. to expand the query “George W. Bush” with “the president of the United States” via his relationship with “the United States”. During the last decade, many methods have been proposed in relation extraction, such as su- pervised learning (Miller et al 2000; Zelenko et al 2003; Culota and Sorensen 2004; Zhao and Grish- man 2005; Zhang et al 2006; Zhou et al 2005, 2006), semi-supervised learning (Brin 1998; Agichtein and Gravano 2000; Zhang 2004; Chen et al 2006), and unsupervised learning (Hasegawa et al 2004; Zhang et al 2005). Among these methods, supervised learning-based methods perform much better than the other two alternatives. However, their performance much depends on the availability of a large amount of manually labeled data and it is normally difficult to adapt an existing system to other applications and domains. On the other hand, unsupervised learning-based methods do not need the definition of relation types and the availability of manually labeled data. However, they fail to classify exact relation types between two entities and their performance is normally very low. To achieve better portability and balance between hu- man efforts and performance, semi-supervised learning has drawn more and more attention re- cently in relation extraction and other NLP appli- cations. This paper proposes a semi-supervised learning method for relation extraction. Given a small amount of labeled data and a large amount of unla- beled data, our proposed method first bootstraps a moderate number of weighted support vectors from all the available data via SVM using a co-training procedure with random feature projection and then applies a label propagation (LP) algorithm to cap- ture the manifold structure in both the labeled and unlabeled data via the bootstrapped support vectors. Compared with previous methods, our method can integrate the advantages of both SVM bootstrap- ping in …

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