Semantic relation extraction with kernels over typed dependency trees

An important step for understanding the semantic content of text is the extraction of semantic relations between entities in natural language documents. Automatic extraction techniques have to be able to identify different versions of the same relation which usually may be expressed in a great variety of ways. Therefore these techniques benefit from taking into account many syntactic and semantic features, especially parse trees generated by automatic sentence parsers. Typed dependency parse trees are edge and node labeled parse trees whose labels and topology contains valuable semantic clues. This information can be exploited for relation extraction by the use of kernels over structured data for classification. In this paper we present new tree kernels for relation extraction over typed dependency parse trees. On a public benchmark data set we are able to demonstrate a significant improvement in terms of relation extraction quality of our new kernels over other state-of-the-art kernels

Learning Vague Knowledge from Socially Generated Content in an Enterprise Framework

Part 7: First Mining Humanistic Data Workshop (MHDW 2012)International audienceThe advent and wide proliferation of Social Web in the recent years has promoted the concept of social interaction as an important influencing factor of the way enterprises and organizations conduct business. Among the fields influenced is that of Enterprise Knowledge Management, where adoption of social computing approaches aims at increasing and maintaining at high levels the active participation of users in the organization’s knowledge management activities. An important challenge towards this is the achievement of the right balance between informalities of socially generated data and the required formality of enterprise knowledge. In this context, we focus on the problem of mining vague knowledge from social content generated within an enterprise framework and we propose a learning framework based on microblogging and fuzzy ontologies

A logicbased approach to relation extraction from texts

Abstract. In recent years, text mining has moved far beyond the classical problem of text classification with an increased interest in more sophisticated processing of large text corpora, such as, for example, evaluations of complex queries. This and several other tasks are based on the essential step of relation extraction. This problem becomes a typical application of learning logic programs by considering the dependency trees of sentences as relational structures and examples of the target relation as ground atoms of a target predicate. In this way, each example is represented by a definite first-order Horn-clause. We show that Plotkin’s LGG operator can effectively be applied to such clauses and propose a simple and effective divide-and-conquer algorithm for listing a certain set of LGGs. We use these LGGs to generate binary features and compute the hypothesis by applying SVM to the feature vectors obtained. Empirical results on the ACE–2003 benchmark dataset indicate that the performance of our approach is comparable to state-of-the-art kernel methods.