Combining Word Embeddings and Feature Embeddings for Fine-grained Relation Extraction

2015 ◽  
Author(s):  
Mo Yu ◽  
Matthew R. Gormley ◽  
Mark Dredze
Keyword(s):  
Relation Extraction ◽  
Word Embeddings ◽  
Fine Grained

scholarly journals Joint Fine-Grained Components Continuously Enhance Chinese Word Embeddings

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 174699-174708
Author(s):  
Chengyang Zhuang ◽  
Yuanjie Zheng ◽  
Wenhui Huang ◽  
Weikuan Jia
Keyword(s):  
Word Embeddings ◽  
Chinese Word ◽  
Fine Grained

scholarly journals Evaluating Word Embeddings in Multi-label Classification Using Fine-Grained Name Typing

2018 ◽  
Author(s):  
Yadollah Yaghoobzadeh ◽  
Katharina Kann ◽  
Hinrich Schütze
Keyword(s):  
Word Embeddings ◽  
Fine Grained

scholarly journals Fine-Grained Temporal Relation Extraction

2019 ◽  
Author(s):  
Siddharth Vashishtha ◽  
Benjamin Van Durme ◽  
Aaron Steven White
Keyword(s):  
Relation Extraction ◽  
Temporal Relation ◽  
Fine Grained ◽  
Temporal Relation Extraction

Drug Knowledge Extraction Framework with Entity Pair Calibration for Chinese Drug Instructions

2021 ◽  
Author(s):  
Xiaoliang Zhang ◽  
Lunsheng Zhou ◽  
Feng Gao ◽  
Zhongmin Wang ◽  
Yongqing Wang ◽  
...  
Keyword(s):  
Information Extraction ◽  
Relation Extraction ◽  
Knowledge Extraction ◽  
Entity Recognition ◽  
Chinese Drug ◽  
Training Models ◽  
Fine Grained ◽  
Score Improvement ◽  
Drug Knowledge ◽  
Relationship Identification

Abstract Existing pharmaceutical information extraction research often focus on standalone entity or relationship identification tasks over drug instructions. There is a lack of a holistic solution for drug knowledge extraction. Moreover, current methods perform poorly in extracting fine-grained interaction relations from drug instructions. To solve these problems, this paper proposes an information extraction framework for drug instructions. The framework proposes deep learning models with fine-tuned pre-training models for entity recognition and relation extraction, in addition, it incorporates an novel entity pair calibration process to promote the performance for fine-grained relation extraction. The framework experiments on more than 60k Chinese drug description sentences from 4000 drug instructions. Empirical results show that the framework can successfully identify drug related entities (F1 >= 0.95) and their relations (F1 >= 0.83) from the realistic dataset, and the entity pair calibration plays an important role (~5% F1 score improvement) in extracting fine-grained relations.

Leveraging More Fine-grained Representation to Reduce Instability within Word Embeddings

2019 ◽  
Vol 23 (3) ◽  
pp. 1-18
Author(s):  
Suzi Park ◽  
Hyopil Shin
Keyword(s):  
Word Embeddings ◽  
Fine Grained

One-shot learning for fine-grained relation extraction via convolutional siamese neural network

2017 ◽  
Author(s):  
Jianbo Yuan ◽  
Han Guo ◽  
Zhiwei Jin ◽  
Hongxia Jin ◽  
Xianchao Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Relation Extraction ◽  
Fine Grained
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