Coreference Resolution Using Semantic Features and Fully Connected Neural Network in the Persian Language

Authors

Hossein Sahlani^{1, *,} , Maryam Hourali¹, Behrouz Minaei-Bidgoli²

¹Malek-Ashtar University of Technology, Tehran, Iran

²School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran

^*Corresponding author. Email: sahlani@mut.ac.ir; sahlani_h@yahoo.com

DOI

10.2991/ijcis.d.200706.002How to use a DOI?

Keywords

Coreference resolution; Fully connected neural networks; Deep learning; Hierarchical accumulative clustering; Persian language

Abstract

Coreference resolution is one of the most critical issues in various applications of natural language processing, such as machine translation, sentiment analysis, summarization, etc. In the process of coreference resolution, in this paper, a fully connected neural network approach has been adopted to enhance the performance of feature extraction whilst also facilitating the mention pair classification process for coreference resolution in the Persian language. For this purpose, first, we focus on the feature extraction phase by fusing some handcrafted features, word embedding features and semantic features. Then, a fully connected deep neural network is utilized to determine the probability of the validity of the mention pairs. After that, the numeric output of the last layer of the utilized neural network is considered as the feature vector of the valid mention pairs. Finally, the coreference mention pairs are specified by utilizing a hierarchical accumulative clustering method. The proposed method's evaluation on the Uppsala dataset demonstrates a meaningful improvement, as indicated by the F-score 64.54%, in comparison to state-of-the-art methods.

Copyright

© 2020 The Authors. Published by Atlantis Press SARL.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Hossein Sahlani
AU  - Maryam Hourali
AU  - Behrouz Minaei-Bidgoli
PY  - 2020
DA  - 2020/07/15
TI  - Coreference Resolution Using Semantic Features and Fully Connected Neural Network in the Persian Language
JO  - International Journal of Computational Intelligence Systems
SP  - 1002
EP  - 1013
VL  - 13
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.200706.002
DO  - 10.2991/ijcis.d.200706.002
ID  - Sahlani2020
ER  -