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- 325 creator danushka-bollegala.
- 325 creator mitsuru-ishizuka.
- 325 creator nguyen-duc.
- 325 creator yutaka-matsuo.
- 325 type InProceedings.
- 325 label "Relational Duality: Unsupervised Extraction of Semantic Relations between Entities on the Web".
- 325 sameAs 325.
- 325 abstract "Extracting semantic relations between entities is an important first step in various tasks in Web mining and natural language processing such as, information extraction, relation detection, and social network mining. A relation can be expressed extensionally by stating all the instances of that relation, or intensionally by defining all the paraphrases of that relation. For example, consider the ACQUISITION relation between two companies. An extensional definition of ACQUISITION contains all pairs of companies where one company is acquired by the other (e.g. (YouTube, Google) or (Powerset, Microsoft)). On the other hand we can intensionally define ACQUISITION as the relation described by lexical patterns such as, X is acquired by Y, or Y purchased X, where X and Y denote two companies. We utilize this dual representation of semantic relations to propose a novel sequential co-clustering algorithm that can efficiently extract a large number of relations from unlabeled data. We provide an efficient heuristic to find the parameters of the proposed co-clustering algorithm. Using the clusters produced by the algorithm, we train an L1 regularized logistic regression model to identify the representative patterns that describe the relation expressed by each cluster. We evaluate the proposed method in three different tasks: measuring relational similarity between entity pairs, open information extraction (Open IE), and classifying relations in a social network system. Experiments conducted using a benchmark dataset show that the proposed method improves existing relational similarity measures. Moreover, the proposed method significantly outperforms the current state-of-the-art Open IE systems in both precision and recall. The proposed method correctly classifies 53 relation types in an online social network containing 470,671 nodes and 35,652,475 edges, thereby demonstrating its efficacy in real-world relation detection tasks.".
- 325 hasAuthorList authorList.
- 325 isPartOf proceedings.
- 325 keyword "Relational data on the Web".
- 325 title "Relational Duality: Unsupervised Extraction of Semantic Relations between Entities on the Web".