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Title: Detecting Collocations Similarity via Logical-Linguistic Model
Authors: Khairova, N. F.
Petrasova, S. V.
Mamyrbayev, Orken
Mukhsina, Kuralay
Issue Date: 2019
Publisher: Association for Computational Linguistics, USA
Citation: Detecting Collocations Similarity via Logical-Linguistic Model / N. Khairova [et al.] // Relations 2019 : workshop on meaning relations between phrases and sentences, May 23, 2019, Gothenburg, Sweden / orgcomm.: V. Kovatchev, D. Gold, T. Zesch. – Gothenburg : ACL, 2019. – P. 15-22.
Abstract: Semantic similarity between collocations, along with words similarity, is one of the main issues of NLP. In particular, it might be addressed to facilitate the automatic thesaurus generation. In the paper, we consider the logical-linguistic model that allows defining the relation of semantic similarity of collocations via the logical-algebraic equations. We provide the model for English, Ukrainian and Russian text corpora. The implementation for each language is slightly different in the equations of the finite predicates algebra and used linguistic resources. As a dataset for our experiment, we use 5801 pairs of sentences of Microsoft Research Paraphrase Corpus for English and more than 1000 texts of scientific papers for Russian and Ukrainian.
Appears in Collections:Кафедра "Інтелектуальні комп'ютерні системи"

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