scholarly journals Low-Cost Implementation of a Named Entity Recognition System for Voice-Activated Human-Appliance Interfaces in a Smart Home

2018 ◽  
Vol 10 (2) ◽  
pp. 488 ◽  
Author(s):  
◽  
Keyword(s):  
Smart Home ◽  
Low Cost ◽  
Named Entity Recognition ◽  
Recognition System ◽  
Entity Recognition ◽  
Named Entity

scholarly journals A Low-Cost Named Entity Recognition Research Based on Active Learning

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Han Huang ◽  
Hongyu Wang ◽  
Dawei Jin
Keyword(s):  
Active Learning ◽  
Language Processing ◽  
Selection Process ◽  
Conditional Random Field ◽  
Low Cost ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Training Set ◽  
Processing Technologies ◽  
Named Entity

Named entity recognition (NER) is an indispensable and very important part of many natural language processing technologies, such as information extraction, information retrieval, and intelligent Q & A. This paper describes the development of the AL-CRF model, which is a NER approach based on active learning (AL). The algorithmic sequence of the processes performed by the AL-CRF model is the following: first, the samples are clustered using the k-means approach. Then, stratified sampling is performed on the produced clusters in order to obtain initial samples, which are used to train the basic conditional random field (CRF) classifier. The next step includes the initiation of the selection process which uses the criterion of entropy. More specifically, samples having the highest entropy values are added to the training set. Afterwards, the learning process is repeated, and the CRF classifier is retrained based on the obtained training set. The learning and the selection process of the AL is running iteratively until the harmonic mean F stabilizes and the final NER model is obtained. Several NER experiments are performed on legislative and medical cases in order to validate the AL-CRF performance. The testing data include Chinese judicial documents and Chinese electronic medical records (EMRs). Testing indicates that our proposed algorithm has better recognition accuracy and recall rate compared to the conventional CRF model. Moreover, the main advantage of our approach is that it requires fewer manually labelled training samples, and at the same time, it is more effective. This can result in a more cost effective and more reliable process.

scholarly journals A Chinese Named Entity Recognition System with Neural Networks

2017 ◽  
Vol 12 ◽  
pp. 04002 ◽  
Author(s):  
Hui-Kang Yi ◽  
Jiu-Ming Huang ◽  
Shu-Qiang Yang
Keyword(s):  
Neural Networks ◽  
Named Entity Recognition ◽  
Recognition System ◽  
Entity Recognition ◽  
Named Entity
Sign in / Sign up

Export Citation Format

Share Document