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PublicationA logic-based approach to relation extraction from textsIn 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 an adaptation of Plotkin's least general generalization (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.
PublicationContext-based clustering of image search resultsIn this work we propose to cluster image search results based on the textual contents of the referring webpages. The natural ambiguity and context-dependence of human languages lead to problems that plague modern image search engines: A user formulating a query usually has in mind just one topic, while the results produced to satisfy this query may (and usually do) belong to the different topics. Therefore, only part of the search results are relevant for a user. One of the possible ways to improve the user's experience is to cluster the results according to the topics they belong to and present the clustered results to the user. As opposed to the clustering based on visual features, an approach utilising the text information in the webpages containing the image is less computationally intensive and provides the resulting clusters with semantically meaningful names.