Coreference Resolution Using Semantic Features and Fully Connected Neural Network in the Persian Language
- https://doi.org/10.2991/ijcis.d.200706.002How to use a DOI?
- Coreference resolution, Fully connected neural networks, Deep learning, Hierarchical accumulative clustering, Persian language
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.
- © 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 -