Pattern-enhanced Named Entity Recognition with Distant Supervision

2020 ◽  
Author(s):  
Xuan Wang ◽  
Yingjun Guan ◽  
Yu Zhang ◽  
Qi Li ◽  
Jiawei Han
Keyword(s):  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entity ◽  
Distant Supervision

Named Entity Recognition Using Distant Supervision and Active Bagging

2016 ◽  
Vol 43 (2) ◽  
pp. 269-274 ◽  
Author(s):  
Seong-hee Lee ◽  
Yeong-kil Song ◽  
Hark-soo Kim
Keyword(s):  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entity ◽  
Distant Supervision

scholarly journals ChemNER: Fine-Grained Chemistry Named Entity Recognition with Ontology-Guided Distant Supervision

2021 ◽  
Author(s):  
Xuan Wang ◽  
Vivian Hu ◽  
Xiangchen Song ◽  
Shweta Garg ◽  
Jinfeng Xiao ◽  
...  
Keyword(s):  
Named Entity Recognition ◽  
Entity Recognition ◽  
Fine Grained ◽  
Named Entity ◽  
Distant Supervision

scholarly journals HAMNER: Headword Amplified Multi-Span Distantly Supervised Method for Domain Specific Named Entity Recognition

2020 ◽  
Vol 34 (05) ◽  
pp. 8401-8408 ◽  
Author(s):  
Shifeng Liu ◽  
Yifang Sun ◽  
Bing Li ◽  
Wei Wang ◽  
Xiang Zhao
Keyword(s):  
Dynamic Programming Algorithm ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Programming Algorithm ◽  
Exact Matching ◽  
Named Entity ◽  
Distant Supervision ◽  
Benchmark Datasets ◽  
Supervised Methods ◽  
Level Model

To tackle Named Entity Recognition (NER) tasks, supervised methods need to obtain sufficient cleanly annotated data, which is labor and time consuming. On the contrary, distantly supervised methods acquire automatically annotated data using dictionaries to alleviate this requirement. Unfortunately, dictionaries hinder the effectiveness of distantly supervised methods for NER due to its limited coverage, especially in specific domains. In this paper, we aim at the limitations of the dictionary usage and mention boundary detection. We generalize the distant supervision by extending the dictionary with headword based non-exact matching. We apply a function to better weight the matched entity mentions. We propose a span-level model, which classifies all the possible spans then infers the selected spans with a proposed dynamic programming algorithm. Experiments on all three benchmark datasets demonstrate that our method outperforms previous state-of-the-art distantly supervised methods.

Arabic named entity recognition using optimized feature sets

2008 ◽  
Author(s):  
Yassine Benajiba ◽  
Mona Diab ◽  
Paolo Rosso
Keyword(s):  
Named Entity Recognition ◽  
Entity Recognition ◽  
Feature Sets ◽  
Named Entity
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