Enhanced conditional random field‐long short‐term memory for name entity recognition in English texts. (1st February 2023)

• Record Type: Journal Article
• Authors: Bhumireddypalli, Veera Sekhar Reddy; Koppula, Srinivas Rao; Koppula, Neeraja
• Is Part Of: Concurrency and computation. Volume 35:Number 9(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-02-01
• Subjects: arithmetic optimization algorithm -- feature extraction -- long short‐term memory -- named entity recognition -- preprocessing; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.7640
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Named entity recognition (NER) is an essential topic in the real world during the advanced development of technologies. Hence, in this article, develop enhanced conditional random field‐long short‐term memory (ECRF‐LSTM) for NER in the English language. The proposed ECRF‐LSTM is the combination of conditional random field‐long short‐term memory (ECRF‐LSTM) and chaotic arithmetic optimization algorithm (CAOA). The proposed research concentrated to perform NER for Indian names from the given input database for Indian digital database management and processing. The Chaotic AOA leads to fast convergence and helps to avoid the local optima. The proposed method is working with three phases preprocessing phase, the feature extraction phase, and the NER phase. In the initial stage, the datasets are collected from the online system. In the preprocessing phase, the removal of the URL, removal of special symbol, username removal, tokenization, and stop word removal are done. After that, the essential features such as domain weight, event weight, textual similarity, spatial similarity, temporal similarity, and relative document‐term frequency difference are extracted and then applied to train the proposed model. To empower the training phase of the CRF‐LSTM method, CAOA is utilized to select optimal weight parameter coefficients of CRF‐LSTM for training the model parameters. The proposed method is validated by statistical measurements and compared with the conventional methods such as convolutional neural network‐particle swarm optimization and convolutional neural network respectively. The proposed method achieves 98.91 accuracy, 97.36 sensitivity, 97.19 specificity, 97.54 precision, and 97.63 recall which is better than the existing methodologies.