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    <titleInfo>
        <title>Detecting Collocations Similarity via Logical-Linguistic Model</title>
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    <name type="personal">
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        <namePart type="family">Khairova</namePart>
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    <name type="personal">
        <namePart type="given">Svitlana</namePart>
        <namePart type="family">Petrasova</namePart>
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    <name type="personal">
        <namePart type="given">Orken</namePart>
        <namePart type="family">Mamyrbayev</namePart>
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    <name type="personal">
        <namePart type="given">Kuralay</namePart>
        <namePart type="family">Mukhsina</namePart>
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            <title>RELATIONS - Workshop on meaning relations between phrases and sentences</title>
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        <name type="personal">
            <namePart type="given">Venelin</namePart>
            <namePart type="family">Kovatchev</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Darina</namePart>
            <namePart type="family">Gold</namePart>
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        <name type="personal">
            <namePart type="given">Torsten</namePart>
            <namePart type="family">Zesch</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>Semantic similarity between collocations, along with words similarity, is one of the main issues of NLP, which must be addressed, in particular, in order 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 1 000 texts of scientific papers for Russian and Ukrainian.</abstract>
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    <identifier type="doi">10.18653/v1/W19-0802</identifier>
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%0 Conference Proceedings
%T Detecting Collocations Similarity via Logical-Linguistic Model
%A Khairova, Nina
%A Petrasova, Svitlana
%A Mamyrbayev, Orken
%A Mukhsina, Kuralay
%Y Kovatchev, Venelin
%Y Gold, Darina
%Y Zesch, Torsten
%S RELATIONS - Workshop on meaning relations between phrases and sentences
%D 2019
%8 May
%I Association for Computational Linguistics
%C Gothenburg, Sweden
%F khairova-etal-2019-detecting
%X Semantic similarity between collocations, along with words similarity, is one of the main issues of NLP, which must be addressed, in particular, in order 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 1 000 texts of scientific papers for Russian and Ukrainian.
%R 10.18653/v1/W19-0802
%U https://aclanthology.org/W19-0802
%U https://doi.org/10.18653/v1/W19-0802

Markdown (Informal)

[Detecting Collocations Similarity via Logical-Linguistic Model](https://aclanthology.org/W19-0802) (Khairova et al., IWCS 2019)

• Detecting Collocations Similarity via Logical-Linguistic Model (Khairova et al., IWCS 2019)

ACL

• Nina Khairova, Svitlana Petrasova, Orken Mamyrbayev, and Kuralay Mukhsina. 2019. Detecting Collocations Similarity via Logical-Linguistic Model. In RELATIONS - Workshop on meaning relations between phrases and sentences, Gothenburg, Sweden. Association for Computational Linguistics.