International Journal of Computational Intelligence Systems

Volume 13, Issue 1, 2020, Pages 1002 - 1013

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

Authors
Hossein Sahlani1, *, ORCID, Maryam Hourali2, Behrouz Minaei-Bidgoli3
1 Malek-Ashtar University of Technology, Amin University, Tehran, Iran
2 Malek-Ashtar University of Technology, Tehran, Iran
3 School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran
*Corresponding author. Email: sahlani@mut.ac.ir; sahlani_h@yahoo.com
Corresponding Author
Hossein Sahlani
Received 9 February 2020, Accepted 5 June 2020, Available Online 15 July 2020.
DOI
https://doi.org/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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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
13 - 1
Pages
1002 - 1013
Publication Date
2020/07
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
https://doi.org/10.2991/ijcis.d.200706.002How to use a DOI?
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/).

Cite this article

TY  - JOUR
AU  - Hossein Sahlani
AU  - Maryam Hourali
AU  - Behrouz Minaei-Bidgoli
PY  - 2020
DA  - 2020/07
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  - https://doi.org/10.2991/ijcis.d.200706.002
ID  - Sahlani2020
ER  -