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Student
Title
Supervisor
Faculty
Educational Programme
Final Grade
Year of Graduation
Veronika Sarkisyan
Using Syntactic Information for Stock Prices Prediction
2018
This paper proposes a method of transforming unstructured textual data into structured features (events). A corpus of Russian-language news texts is used as source data. The events in form of "subject-verb-object" triples are extracted from texts through syntactic-based rules that can capture both syntactic and semantic information. The SVO-triples are transformed into embeddings for subsequent use in the problem of stock prices prediction.

Keywords: Stock prices prediction, syntax, dependency tree, event extraction, SyntaxNet.

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