Extra Large Sequence Transformer Model for Chinese Word Segment

2021 ◽  
Author(s):  
Dezhou Shen
Keyword(s):  
Random Field ◽  
Conditional Random Field ◽  
Document Analysis ◽  
Chinese Word ◽  
Test Dataset ◽  
Field Layer ◽  
Segment Model ◽  
Proposed Model ◽  
Transformer Model

Abstract Chinese word segment is widely studied in document analysis. The accuracy of the current popular word segment model, LSTM+CRF, is still not satisfactory. Models trained by the popular dataset often fails in the out-domain situation. In this paper, combining the Transformer-XL layer, the Fully-Connect layer, and the Conditional Random Field layer, the proposed model improved 3.23% in the macro-F1 score, comparing to the BERT+CRF model, on the MSR2005 Chinese word segment test dataset.

scholarly journals A Subjective Expressions Extracting Method for Social Opinion Mining

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Mingyong Yin ◽  
Haizhou Wang ◽  
Xingshu Chen ◽  
Hong Yan ◽  
Rui Tang
Keyword(s):  
Neural Network ◽  
Public Opinion ◽  
Random Field ◽  
Opinion Mining ◽  
Conditional Random Field ◽  
Feature Engineering ◽  
Sufficient Information ◽  
Proposed Model ◽  
Opinion Expression ◽  
Representation Method

Opinion mining plays an important role in public opinion monitoring, commodity evaluation, government governance, and other areas. One of the basic tasks of opinion mining is to extract the expression elements, which can be further divided into direct subjective expression and expressive subjective expression. For the task of subjective expression extraction, the methods based on neural network can learn features automatically without exhaustive feature engineering and have been proved to be efficient for opinion mining. Constructing adequate input vector which can encode sufficient information is a challenge of neural network-based approach. To cope with this problem, a novel representation method that combines the different features with word vectors is proposed. Then, we use neural network and conditional random field to train and predict the expressions and carry out comparative experiments on different methods and features combinations. Experimental results show the performance of the proposed model, and the F value outperforms other methods in comparative experimental dataset. Our work can provide hint for further research on opinion expression extraction.

“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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