ANAS: Sentence Similarity Calculation Based on Automatic Neural Architecture Search

Author(s):  
Dong-Sheng Wang ◽  
Cui-Ping Zhao ◽  
Qi Wang ◽  
Kwame Dwomoh Ofori ◽  
Bin Han ◽  
...  
2014 ◽  
Vol 1049-1050 ◽  
pp. 1514-1517
Author(s):  
Sai Dong Lv ◽  
Ji Li Xie

Subjective question marking system at present is affected by the attention of people, the subjective topic grading principles are common contrast degree of exam questions similar to those of the reference answer, and based on the improved semantic similarity algorithm, calculation of sentence similarity, the similarity degree of exam questions and reference answer is obtained, thus give scores.And design based on semantic similarity experiment, the experiment results show that the proposed multi-level fusion similarity calculation method to improve the original method, on the basis of integration advantages of various methods, make the calculation results meet the requirements of the scoring system.


Author(s):  
Anutharsha Selvarasa ◽  
Nilasini Thirunavukkarasu ◽  
Niveathika Rajendran ◽  
Chinthoorie Yogalingam ◽  
Surangika Ranathunga ◽  
...  

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Ruiteng Yan ◽  
Dong Qiu ◽  
Haihuan Jiang

Sentence similarity calculation is one of the important foundations of natural language processing. The existing sentence similarity calculation measurements are based on either shallow semantics with the limitation of inadequately capturing latent semantics information or deep learning algorithms with the limitation of supervision. In this paper, we improve the traditional tolerance rough set model, with the advantages of lower time complexity and becoming incremental compared to the traditional one. And then we propose a sentence similarity computation model from the perspective of uncertainty of text data based on the probabilistic tolerance rough set model. It has the ability of mining latent semantics information and is unsupervised. Experiments on SICK2014 task and STSbenchmark dataset to calculate sentence similarity identify a significant and efficient performance of our model.


2014 ◽  
Vol 7 (1) ◽  
pp. 48 ◽  
Author(s):  
Huihui Zhang ◽  
Zhengtao Yu ◽  
Longhua Shen ◽  
Jianyi Guo ◽  
Xudong Hong

2021 ◽  
Vol 105 ◽  
pp. 377-383
Author(s):  
Bo Yang ◽  
Yu Qi Yao

At present, the research on automatic evaluation of computer online examination system has become a hot issue. Natural language processing technology based on text mining has unique advantages in text similarity calculation. This paper designs the TR-BFS-WE-WMD integrated algorithm for automatic review of Chinese subjective questions based on text mining, uses the word database to integrate the BFS algorithm, realizes the calculation of the text full sentence similarity and keyword matching, and solves the problem of text semantic similarity. Experimental results prove that this algorithm has good accuracy and effectiveness. The TR-BFS-WE-WMD algorithm provides a useful attempt for the intelligent research of the computer automatic review system and has good practical value.


1992 ◽  
Author(s):  
William Ross ◽  
Ennio Mingolla

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