Using sub-word-level information for confidence estimation with conditional random field models

2012 ◽  
Author(s):  
Matthew S. Seigel ◽  
Phillip C. Woodland
Keyword(s):  
Random Field ◽  
Conditional Random Field ◽  
Confidence Estimation ◽  
Word Level ◽  
Level Information

“Opinion Target: Opinion Word” Pairs Extraction Based on CRF

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 251
Author(s):  
Yan Yan ◽  
Faguo Zhou ◽  
Yifan Ge ◽  
Cheng Liu ◽  
Jingwu Feng
Keyword(s):  
Random Field ◽  
Mobile Applications ◽  
Conditional Random Field ◽  
User Reviews ◽  
Visualization System ◽  
Target Extraction

With the spread of mobile applications and online interactive platforms, the number of user reviews are increasing explosively and becoming one of the most important ways for users to voice opinions. Opinion target extraction and opinion word extraction are two key procedures used to determine the helpfulness of reviews. In this paper, we implement a system to extract “opinion target:opinion word” pairs based on the Conditional Random Field (CRF). Firstly, we used the CRF model to extract opinion targets and opinion words, then combined these into pairs in order. In addition, Node.js was used to build a visualization system to display “opinion target:opinion word” pairs. In order to verify the effectiveness of the system, experiments were conducted on the Laptop and Restaurant datasets of SemEval-2014-task4, and the accuracy of the F value extracted by the model reached 86% and 90%, respectively. All the code and datasets for this experiment are available on GitHub.

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