BUT-TYPED: Using domain knowledge for computing typed similarity

This paper deals with knowledge-based text processing which aims at an intuitive notion of textual similarity. Entities and relations relevant for a particular domain are identified and disambiguated by means of semi-supervised machine learning techniques and resulting annotations are applied for computing typedsimilarity of individual texts. The work described in this paper particularly shows effects of the mentioned processes in the context of the *SEM 2013 pilot task on typed-similarity, a part of the Semantic Textual Similarity shared task. The goal is to evaluate the degree of semantic similarity between semi-structured records. As the evaluation dataset has been taken from Europeana – a collection of records on European cultural heritage objects – we focus on computing a semantic distance on field author which has the highest potential to benefit from the domain knowledge. Specific features that are employed in our system BUT-TYPED are briefly introduced together with a discussion on their efficient acquisition. Support Vector Regression is then used to combine the features and to provide a final similarity score. The system ranked third on the attribute author among 15 submitted runs in the typed-similarity task.