Coreference Resolution Using Neural MCDM and Fuzzy Weighting Technique

Abstract

Coreference resolution has been an active field of research in the past several decades and plays a vital role in many areas such as information extraction, document summarization, machine translation, and question answering systems. This paper presents a new coreference resolution approach by incorporating RoBERTa embedding with a neural multi-criteria decision making (MCDM) method. The proposed model does not use any syntactic and dependency parser. Mentions were extracted from the text with an unhand engineered mention detector and features were extracted from a deep neural network. Next, the problem is modeled in the form of effective parameters of the performance such as error rate reduction and enhances the F1 by Kohonen MCDM neural network. The weights assigned to the features represent their importance and suggests the best reference for a mention where such weights are computed using a fuzzy weighting method. Comparing to state-of-the-art coreference resolution models, the simulation results show significant improvements for the proposed approach on different datasets in terms of precision and recall and achieving marginal improvements on the following datasets: English CoNLL-2012 shared task (+3.1 F1), Yahoo's news site (+6.6 F1), and English Gigaword (+7.04).

On the use of Machine Learning and Deep Learning for Text Similarity and Categorization and its Application to Troubleshooting Automation

“…Hourali, Zahedi, and Fateh [35] present a new coreference resolution approach. A coreference occurs when two or more expressions in a text refer to the same person or thing.…”

“…Directly approaches TA -Classifying the issue [2,3,4,5,6,16,21,22,23,24,25,26,28,30,31,33,36,37,38,39,40,41,42,43] -Dealing with imbalanced data [15] Indirectly approaches TA -Analyzing historical interactions data [17,20,34] -Identifying double-meaning terms [29,32,35] -Identifying languages [18,27] -Receiving feedback…”