Automatic detection and attribution of quotes

Abstract

Quotations extraction and attribution are important practical tasks for the media, but most of the presented solutions are monolingual. In this work, I present a complex machine learning-based system for extraction and attribution of direct and indirect quotations, which is trained on English and tested on Czech and Russian data. Czech and Russian test datasets were manually annotated as part of this study. This system is compared against a rule-based baseline model. Baseline model demonstrates better precision in extraction of quotation elements, but low recall. The machine learning-based model is better overall in extracting separate elements of quotations and full quotations as well.