Automatically Detecting and Attributing Indirect Quotations

Abstract

Direct quotations are used for opinion min- ing and information extraction as they have an easy to extract span and they can be attributed to a speaker with high accuracy. However, simply focusing on direct quotations ignores around half of all reported speech, which is in the form of indirect or mixed speech. This work presents the first large-scale experiments in indirect and mixed quotation extraction and attribution. We propose two methods of ex- tracting all quote types from news articles and evaluate them on two large annotated corpora, one of which is a contribution of this work. We further show that direct quotation attribu- tion methods can be successfully applied to in- direct and mixed quotation attribution.