Automatic extraction of synonymous collocation pairs from text corpora is a challenging task of NLP. In order to search collocations of similar meaning in English texts, we use logical-algebraic equations. These equations combine grammatical and semantic characteristics of words of substantive, attributive and verbal collocations types. With Stanford POS tagger and Stanford Universal Dependencies parser, we identify the grammatical characteristics of words. We exploit WordNet synsets to pick synonymous words of collocations. The potential synonymous word combinations found are checked for compliance with grammatical and semantic characteristics of the proposed logical-linguistic equations. Our dataset includes more than half a million Wikipedia articles from a few portals. The experiment shows that the more frequent synonymous collocations occur in texts, the more related topics of the texts might be. The precision of synonymous collocations search in our experiment has achieved the results close to other studies like ours.

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This paper proposes the model for searching similar collocations in English texts in order to determine semantically connected text fragments for social network data streams analysis. The logical-linguistic model uses semantic and grammatical features of words to obtain a sequence of semantically related to each other text fragments from different actors of a social network. In order to implement the model, we leverage Universal Dependencies parser and Natural Language Toolkit with the lexical database WordNet. Based on the Blog Authorship Corpus, the experiment achieves over 0.92 precision.

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