Academy & Industry Research Collaboration Center (AIRCC)

Volume 9, Number 7, June 2019

A Comparative Mention-Pair Models for Coreference Resolution in DARI Language for Information Extraction


Ghezal Ahmad Jan Zia1, Ahmad Zia Sharifi2, Fazl Ahmad Amini3, and Niaz Mohammad Ramaki3, 1Technical University of Berlin, Germany, 2Nangarhar University, Afghanistan and 3Kabul University Kabul Polytechnique University, Afghanistan


Coreference resolution plays an important role in Information Extraction.This paper covers the investigation of two strategies based on a mention-pair resolver using Decision Tree classifier on structured and unstructured dataset, targeting coreference resolution in Dari language. Strategies are (1) training separate models which is specialized in particular categories (e.g., lexical, syntactic and semantic) and types of mentions (e.g. pronouns, proper nouns) and (2) using a structured dataset on a machine learning library that is designed to classify numerical values. Moreover, these modifications and comparative models describe a contribution of comprehensive factors involved in the resolution of texts. Specifically, we developed the first Dari corpus ('DariCoref') based on OntoNotes and WikiCoref scheme. Both strategies are produced f-score of state-of-the-art.