Automatic Detection and Classification of Information Events in Media Texts

2020

Closed access Metadata (CSL-JSON)
DOI 10.3103/s0005105520040032
Data confidence 70 medium
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It is built from checkable properties of the record itself: whether the article has a DOI, how many independent sources confirmed its data, whether the required fields are filled in, and how recent the latest confirmation is. Disagreement between sources and a missing identifier lower it. A value of 80 or above means the record was published without further checking; 60 to 79 means it is published at reduced confidence, with a manual check advisable. A record verified by an editor by hand is never pushed below 90 by a recalculation. This scores our data about the article, not the article itself.

Abstract

Abstract The solution to the problem of automatically identifying and classifying information events in media texts is described based on the model of phraseological conceptual analysis of texts. The proposed solution is based on the use of previously developed methods for formalizing the semantic structure of sentences, as well as methods and algorithms for identifying fragments of media texts that describe information events. The developed algorithm implements the rules of C. Fillmore’s case grammar, which are based on the procedures of semantic–syntactic and conceptual analysis of texts.