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Semantic Textual Similarity Using Various Approaches

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Machine Intelligence and Big Data in Industry

Part of the book series: Studies in Big Data ((SBD,volume 19))

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Abstract

The paper is devoted to the semantic textual similarity (STS) problem. Given two sentences of text, s1 and s2, the systems participating in this problem should compute how similar s1 and s2 are, returning a similarity score. We present our experience in this topic, ranging from the knowledge-poor approaches to some compact and easy applied knowledge-rich methods (using structured knowledge base frameworks like WordNet, Wikipedia or BabelNet). The evaluation of the proposed methods was performed using the datasets from SemEval-2014/15 tasks.

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Correspondence to Maciej Kazuła .

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Kazuła, M., Kozłowski, M. (2016). Semantic Textual Similarity Using Various Approaches. In: Ryżko, D., Gawrysiak, P., Kryszkiewicz, M., Rybiński, H. (eds) Machine Intelligence and Big Data in Industry. Studies in Big Data, vol 19. Springer, Cham. https://doi.org/10.1007/978-3-319-30315-4_5

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  • DOI: https://doi.org/10.1007/978-3-319-30315-4_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-30314-7

  • Online ISBN: 978-3-319-30315-4

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