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Article

Semi-Supervised Bidirectional Long Short-Term Memory and Conditional Random Fields Model for Named-Entity Recognition Using Embeddings from Language Models Representations

by 1,2, 1,* and 1
1
School of Information Science and Technology, Northwest University, Xi’an 710127, China
2
School of Engineering and Technology, Xi’an Fanyi University, 710105 Xi’an, China
*
Author to whom correspondence should be addressed.
Entropy 2020, 22(2), 252; https://doi.org/10.3390/e22020252
Received: 27 January 2020 / Revised: 20 February 2020 / Accepted: 21 February 2020 / Published: 22 February 2020
(This article belongs to the Special Issue Information Theory and Graph Signal Processing)
Increasingly, popular online museums have significantly changed the way people acquire cultural knowledge. These online museums have been generating abundant amounts of cultural relics data. In recent years, researchers have used deep learning models that can automatically extract complex features and have rich representation capabilities to implement named-entity recognition (NER). However, the lack of labeled data in the field of cultural relics makes it difficult for deep learning models that rely on labeled data to achieve excellent performance. To address this problem, this paper proposes a semi-supervised deep learning model named SCRNER (Semi-supervised model for Cultural Relics’ Named Entity Recognition) that utilizes the bidirectional long short-term memory (BiLSTM) and conditional random fields (CRF) model trained by seldom labeled data and abundant unlabeled data to attain an effective performance. To satisfy the semi-supervised sample selection, we propose a repeat-labeled (relabeled) strategy to select samples of high confidence to enlarge the training set iteratively. In addition, we use embeddings from language model (ELMo) representations to dynamically acquire word representations as the input of the model to solve the problem of the blurred boundaries of cultural objects and Chinese characteristics of texts in the field of cultural relics. Experimental results demonstrate that our proposed model, trained on limited labeled data, achieves an effective performance in the task of named entity recognition of cultural relics. View Full-Text
Keywords: cultural relics; named-entity recognition; semi-supervised learning; embeddings from language models; bidirectional long short-term memory network; conditional random fields cultural relics; named-entity recognition; semi-supervised learning; embeddings from language models; bidirectional long short-term memory network; conditional random fields
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MDPI and ACS Style

Zhang, M.; Geng, G.; Chen, J. Semi-Supervised Bidirectional Long Short-Term Memory and Conditional Random Fields Model for Named-Entity Recognition Using Embeddings from Language Models Representations. Entropy 2020, 22, 252. https://doi.org/10.3390/e22020252

AMA Style

Zhang M, Geng G, Chen J. Semi-Supervised Bidirectional Long Short-Term Memory and Conditional Random Fields Model for Named-Entity Recognition Using Embeddings from Language Models Representations. Entropy. 2020; 22(2):252. https://doi.org/10.3390/e22020252

Chicago/Turabian Style

Zhang, Min, Guohua Geng, and Jing Chen. 2020. "Semi-Supervised Bidirectional Long Short-Term Memory and Conditional Random Fields Model for Named-Entity Recognition Using Embeddings from Language Models Representations" Entropy 22, no. 2: 252. https://doi.org/10.3390/e22020252

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