Currently, the volume of news data streams is growing that contributes to increasing interest in systems that allow automating the big data streams processing. Based on intelligent data processing tools, the semantic similarity identification of text information will make it possible to select common information spaces of news. The article analyzes up-to-date statistical metrics for identifying coherent fragments, in particular, from news texts displaying the agenda, identifies the main advantages and disadvantages as well. The information technology is proposed for identifying the common information space of relevant news in the data stream for a certain period of time. The technology includes the logical-linguistic and distributive-statistical models for identifying collocations. The MI distributional semantic model is applied at the stage of potential collocation extraction. At the same time, regular expressions developed in accordance with the grammar of the English language make it possible to identify grammatically correct constructions. The advantage of the developed logical-linguistic model formalizing the semantic-grammatical characteristics of collocations, based on the use of algebraicpredicate operations and a semantic equivalence predicate, is that both the grammatical structure of the language and the meaning of words (collocates) are analyzed. The WordNet thesaurus is used to determine the synonymy relationship between the main and dependent collocation components. Based on the investigated corpus of news texts from the CNN and BBC services, the effectiveness of the developed technology is assessed. The analysis shows that the precision coefficient is 0.96. The use of the proposed technology could improve the quality of news streams processing. The solution to the problem of automatic identification of semantic similarity can be used to identify texts of the same domain, relevant information, extract facts and eliminate semantic ambiguity, etc. Keywords: data stream, agenda, logical-linguistic model, distribution-statistical model, collocation, semantic similarity, WordNet, news text corpus, precision.