scholarly journals End-to-End Recurrent Neural Network Models for Vietnamese Named Entity Recognition: Word-Level Vs. Character-Level

2018 ◽  
pp. 219-232 ◽  
Author(s):  
Thai-Hoang Pham ◽  
Phuong Le-Hong
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Named Entity Recognition ◽  
Network Models ◽  
Entity Recognition ◽  
Neural Network Models ◽  
Named Entity ◽  
Word Level ◽  
End To End

An Experimental Study of Hybrid Machine Learning Models for Extracting Named Entities

10.29007/dp5m ◽  
2019 ◽  
Author(s):  
Lei Jiang ◽  
Elena Bolshakova
Keyword(s):  
Neural Network ◽  
Conditional Random Fields ◽  
Named Entity Recognition ◽  
Network Models ◽  
Entity Recognition ◽  
Neural Network Models ◽  
Named Entities ◽  
Hybrid Neural Network ◽  
Named Entity ◽  
Two Hybrid

The paper describes two hybrid neural network models for named entity recognition (NER) in texts, as well as results of experiments with them. The first model, namely Bi-LSTM-CRF, is known and used for NER, while the other model named Gated-CNN- CRF is proposed in this work. It combines convolutional neural network (CNN), gated linear units, and conditional random fields (CRF). Both models were tested for NER on three different language datasets, for English, Russian, and Chinese. All resulted scores of precision, recall and F1-measure for both models are close to the state-of-the-art for NER, and for the English dataset CoNLL-2003, Gated-CNN-CRF model achieves 92.66 of F1-measure, outperforming the known result.

scholarly journals Research on Chinese medical named entity recognition based on collaborative cooperation of multiple neural network models

2020 ◽  
Vol 104 ◽  
pp. 103395 ◽  
Author(s):  
Bin Ji ◽  
Shasha Li ◽  
Jie Yu ◽  
Jun Ma ◽  
Jintao Tang ◽  
...  
Keyword(s):  
Neural Network ◽  
Named Entity Recognition ◽  
Network Models ◽  
Entity Recognition ◽  
Neural Network Models ◽  
Named Entity ◽  
Multiple Neural Network

scholarly journals Coreference Aware Representation Learning for Neural Named Entity Recognition

2019 ◽  
Author(s):  
Zeyu Dai ◽  
Hongliang Fei ◽  
Ping Li
Keyword(s):  
Neural Network ◽  
State Of The Art ◽  
Structural Information ◽  
Named Entity Recognition ◽  
Network Models ◽  
Third Party ◽  
Entity Recognition ◽  
Neural Network Models ◽  
Named Entity ◽  
Art Performance

Recent neural network models have achieved state-of-the-art performance on the task of named entity recognition (NER). However, previous neural network models typically treat the input sentences as a linear sequence of words but ignore rich structural information, such as the coreference relations among non-adjacent words, phrases or entities. In this paper, we propose a novel approach to learn coreference-aware word representations for the NER task at the document level. In particular, we enrich the well-known neural architecture ``CNN-BiLSTM-CRF'' with a coreference layer on top of the BiLSTM layer to incorporate coreferential relations. Furthermore, we introduce the coreference regularization to ensure the coreferential entities to share similar representations and consistent predictions within the same coreference cluster. Our proposed model achieves new state-of-the-art performance on two NER benchmarks: CoNLL-2003 and OntoNotes v5.0. More importantly, we demonstrate that our framework does not rely on gold coreference knowledge, and can still work well even when the coreferential relations are generated by a third-party toolkit.

scholarly journals An Instance Transfer-Based Approach Using Enhanced Recurrent Neural Network for Domain Named Entity Recognition

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 45263-45270
Author(s):  
Chuanbo Liu ◽  
Chaojie Fan ◽  
Zhengju Wang ◽  
Yueqing Sun
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entity

scholarly journals Ontology-Based Healthcare Named Entity Recognition from Twitter Messages Using a Recurrent Neural Network Approach

2019 ◽  
Vol 16 (19) ◽  
pp. 3628 ◽  
Author(s):  
Erdenebileg Batbaatar ◽  
Keun Ho Ryu
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Adverse Drug Events ◽  
Viterbi Algorithm ◽  
Short Term Memory ◽  
Conditional Random Field ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entity ◽  
Health Related

Named Entity Recognition (NER) in the healthcare domain involves identifying and categorizing disease, drugs, and symptoms for biosurveillance, extracting their related properties and activities, and identifying adverse drug events appearing in texts. These tasks are important challenges in healthcare. Analyzing user messages in social media networks such as Twitter can provide opportunities to detect and manage public health events. Twitter provides a broad range of short messages that contain interesting information for information extraction. In this paper, we present a Health-Related Named Entity Recognition (HNER) task using healthcare-domain ontology that can recognize health-related entities from large numbers of user messages from Twitter. For this task, we employ a deep learning architecture which is based on a recurrent neural network (RNN) with little feature engineering. To achieve our goal, we collected a large number of Twitter messages containing health-related information, and detected biomedical entities from the Unified Medical Language System (UMLS). A bidirectional long short-term memory (BiLSTM) model learned rich context information, and a convolutional neural network (CNN) was used to produce character-level features. The conditional random field (CRF) model predicted a sequence of labels that corresponded to a sequence of inputs, and the Viterbi algorithm was used to detect health-related entities from Twitter messages. We provide comprehensive results giving valuable insights for identifying medical entities in Twitter for various applications. The BiLSTM-CRF model achieved a precision of 93.99%, recall of 73.31%, and F1-score of 81.77% for disease or syndrome HNER; a precision of 90.83%, recall of 81.98%, and F1-score of 87.52% for sign or symptom HNER; and a precision of 94.85%, recall of 73.47%, and F1-score of 84.51% for pharmacologic substance named entities. The ontology-based manual annotation results show that it is possible to perform high-quality annotation despite the complexity of medical terminology and the lack of context in tweets.

scholarly journals Terminologies augmented recurrent neural network model for clinical named entity recognition

2020 ◽  
Vol 102 ◽  
pp. 103356 ◽  
Author(s):  
Ivan Lerner ◽  
Nicolas Paris ◽  
Xavier Tannier
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Recurrent Neural Network ◽  
Neural Network Model ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entity

scholarly journals Named Entity Correction in Neural Machine Translation Using the Attention Alignment Map

2021 ◽  
Vol 11 (15) ◽  
pp. 7026
Author(s):  
Jangwon Lee ◽  
Jungi Lee  ◽  
Minho Lee  ◽  
Gil-Jin Jang
Keyword(s):  
Machine Translation ◽  
Named Entity Recognition ◽  
Network Models ◽  
Entity Recognition ◽  
Neural Network Models ◽  
Named Entities ◽  
Neural Machine Translation ◽  
Named Entity ◽  
Input And Output ◽  
Artificial Neural Network Models

Neural machine translation (NMT) methods based on various artificial neural network models have shown remarkable performance in diverse tasks and have become mainstream for machine translation currently. Despite the recent successes of NMT applications, a predefined vocabulary is still required, meaning that it cannot cope with out-of-vocabulary (OOV) or rarely occurring words. In this paper, we propose a postprocessing method for correcting machine translation outputs using a named entity recognition (NER) model to overcome the problem of OOV words in NMT tasks. We use attention alignment mapping (AAM) between the named entities of input and output sentences, and mistranslated named entities are corrected using word look-up tables. The proposed method corrects named entities only, so it does not require retraining of existing NMT models. We carried out translation experiments on a Chinese-to-Korean translation task for Korean historical documents, and the evaluation results demonstrated that the proposed method improved the bilingual evaluation understudy (BLEU) score by 3.70 from the baseline.

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