scholarly journals Biomedical Named Entity Recognition Using the SVM Methodologies and bio Tagging Schemes

2020 ◽  
Vol 72 (4) ◽  
pp. 52-64
Author(s):  
Thiyagu Meenachisundaram ◽  
Manjula Dhanabalachandran
Keyword(s):  
Machine Learning ◽  
Named Entity Recognition ◽  
Machine Learning Algorithms ◽  
Entity Recognition ◽  
Local Context ◽  
Named Entity ◽  
Conventional Machine ◽  
Context Features ◽  
Linear Text ◽  
Biomedical Named Entity Recognition

Biomedical Named Entity Recognition (BNER) is identification of entities such as drugs, genes, and chemicals from biomedical text, which help in information extraction from the domain literature. It would allow extracting information such as drug profiles, similar or related drugs and associations between drugs and their targets. This venue presents opportunities for improvement even though many machine learning methods have been applied. The efficiency can be improved in case of biological related chemical entities as there are varied structure and properties. This new approach combines two state-of-the-art algorithms and aims to improve the performance by applying it to varied sets of features including linguistic, orthographic, Morphological, domain features and local context features. It uses the sequence tagging capability of CRF to identify the boundary of the entity and classification efficiency of SVM to detect subtypes in BNER. The method is tested on two different datasets 1) GENIA and 2) CHEMDNER corpus with different types of entities. The result shows that proposed hybrid method enhances the BNER compared to the conventional machine learning algorithms. Moreover the detailed study of SVM and the methodologies has been discussed clearly. The linear and non linear text classification can be mapped clearly in the section 3. The final section describes the results and the evaluation of the proposed method.

scholarly journals Evaluating Word Representation Features in Biomedical Named Entity Recognition Tasks

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Buzhou Tang ◽  
Hongxin Cao ◽  
Xiaolong Wang ◽  
Qingcai Chen ◽  
Hua Xu
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Biomedical Domain ◽  
Crucial Step ◽  
Named Entity ◽  
Different Types ◽  
Word Representation ◽  
Biomedical Named Entity Recognition

Biomedical Named Entity Recognition (BNER), which extracts important entities such as genes and proteins, is a crucial step of natural language processing in the biomedical domain. Various machine learning-based approaches have been applied to BNER tasks and showed good performance. In this paper, we systematically investigated three different types of word representation (WR) features for BNER, including clustering-based representation, distributional representation, and word embeddings. We selected one algorithm from each of the three types of WR features and applied them to the JNLPBA and BioCreAtIvE II BNER tasks. Our results showed that all the three WR algorithms were beneficial to machine learning-based BNER systems. Moreover, combining these different types of WR features further improved BNER performance, indicating that they are complementary to each other. By combining all the three types of WR features, the improvements inF-measure on the BioCreAtIvE II GM and JNLPBA corpora were 3.75% and 1.39%, respectively, when compared with the systems using baseline features. To the best of our knowledge, this is the first study to systematically evaluate the effect of three different types of WR features for BNER tasks.

scholarly journals GRN: Gated Relation Network to Enhance Convolutional Neural Network for Named Entity Recognition

2019 ◽  
Vol 33 ◽  
pp. 6236-6243 ◽  
Author(s):  
Hui Chen ◽  
Zijia Lin ◽  
Guiguang Ding ◽  
Jianguang Lou ◽  
Yusen Zhang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Short Term Memory ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Local Context ◽  
Named Entity ◽  
Entire Sentence ◽  
Context Features ◽  
Gpu Parallelism

The dominant approaches for named entity recognitionm (NER) mostly adopt complex recurrent neural networks (RNN), e.g., long-short-term-memory (LSTM). However, RNNs are limited by their recurrent nature in terms of computational efficiency. In contrast, convolutional neural networks (CNN) can fully exploit the GPU parallelism with their feedforward architectures. However, little attention has been paid to performing NER with CNNs, mainly owing to their difficulties in capturing the long-term context information in a sequence. In this paper, we propose a simple but effective CNN-based network for NER, i.e., gated relation network (GRN), which is more capable than common CNNs in capturing long-term context. Specifically, in GRN we firstly employ CNNs to explore the local context features of each word. Then we model the relations between words and use them as gates to fuse local context features into global ones for predicting labels. Without using recurrent layers that process a sentence in a sequential manner, our GRN allows computations to be performed in parallel across the entire sentence. Experiments on two benchmark NER datasets (i.e., CoNLL2003 and Ontonotes 5.0) show that, our proposed GRN can achieve state-of-the-art performance with or without external knowledge. It also enjoys lower time costs to train and test.

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