Abstract
The paper describes the use of semantic-syntactic word valence vectors as context vectors in the Formal Concept Analysis for building high-quality taxonomies. Research and experiments have confirmed a significant improvement in the quality of constructed taxonomies when in tensor models the number of dimensions is increased while generating semantic-syntactic word valence vectors. The increased arity of the tensor model gives a more accurate description of the multidimensional semantic and syntactic relations and allows allocating more commutative semantic-syntactic properties of the words that are used in the Formal Concept Analysis.
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Marchenko, O. (2016). Semantic-Syntactic Word Valence Vectors for Building a Taxonomy. In: Métais, E., Meziane, F., Saraee, M., Sugumaran, V., Vadera, S. (eds) Natural Language Processing and Information Systems. NLDB 2016. Lecture Notes in Computer Science(), vol 9612. Springer, Cham. https://doi.org/10.1007/978-3-319-41754-7_19
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DOI: https://doi.org/10.1007/978-3-319-41754-7_19
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