The Effectiveness of Sieve System Architecture on the Extraction of Coreferential Relationships in the Russian Language

Student: Lyell John robert

Educational Programme: Language Theory and Computational Linguistics (Master)

Year of Graduation: 2017

In this paper I seek to analyze and understand the effectiveness of employing an end-to-end sieve based architecture for textual data in the Russian language. Combining both heuristic sieves and statistical sieves trained using a Random Forest Classifier, I attempt to recreate and adapt several similar experiments that have been conducted on English language data, utilizing both local and entity level features.

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