scholarly journals An improved semantic similarity algorithm based on HowNet and CiLin

2020 ◽  
Vol 309 ◽  
pp. 03004
Author(s):  
Ying Wang ◽  
Xiwei Feng ◽  
Yue Zhang ◽  
Haiming Chen ◽  
Lijie Xing
Keyword(s):  
Semantic Similarity ◽  
Calculation Method ◽  
Improved Method ◽  
Data Set ◽  
Specific Calculation ◽  
Word Similarity ◽  
Pearson Coefficient ◽  
Similarity Calculation ◽  
Similarity Algorithm

This paper explores an improved method for the semantic similarity calculation of words combined with HowNet and CiLin. Firstly, we designing the algorithm based on HowNet’s sememe similarity improvement calculation, comprehensively considering the influence of each part of sememe on the overall meaning, and improving the calculation of word similarity based on HowNet by changing the specific calculation method of each part of sememe. At the same time, we adopt different strategies for the different results obtained in the similarity calculation of CiLin. The experimental RG data set proves that the modified Pearson coefficient of the method reaches 0.87.

Research on the Subjective Topic Grading Agorithm Based on the Computer Course

2014 ◽  
Vol 1049-1050 ◽  
pp. 1514-1517
Author(s):  
Sai Dong Lv ◽  
Ji Li Xie
Keyword(s):  
Semantic Similarity ◽  
Calculation Method ◽  
Original Method ◽  
Sentence Similarity ◽  
Similarity Calculation ◽  
Similarity Algorithm ◽  
Computer Course ◽  
Calculation Results ◽  
Multi Level ◽  
Level Fusion

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.

An Analysis Method of Semantic Relevancy on Controlled Tag

2012 ◽  
Vol 263-266 ◽  
pp. 1588-1592
Author(s):  
Jiu Qing Li ◽  
Chi Zhang ◽  
Peng Zhou Zhang
Keyword(s):  
Semantic Similarity ◽  
Calculation Method ◽  
Domain Ontology ◽  
Analysis Method ◽  
Semantic Correlation ◽  
Retrieval Efficiency ◽  
Similarity Calculation

To solve resource-tagging inefficiency and low-precision retrieval in special field, an analysis method of tag semantic relevancy based on controlled database was proposed. The characteristic of special field and building method for controlled database were discussed. Domain ontology correlation calculation method was used to get semantic correlation. The tag semantic similarity calculation method was developed for semantic similarity, and normalization was used to increase the similarity accuracy. With semantic correlation and similarity as parameters, the semantic relevancy in special field can be obtained. This method was used successfully in the special field of actual projects, improved resource-tagging and retrieval efficiency.

Word Similarity Algorithm Based on WordNet And HowNet

2012 ◽  
Vol 155-156 ◽  
pp. 375-380 ◽  
Author(s):  
Wu Ling Ren ◽  
Jin Ju Guo
Keyword(s):  
Tree Structure ◽  
Hierarchical Tree ◽  
Word Similarity ◽  
Similarity Calculation ◽  
Similarity Algorithm ◽  
Precision And Accuracy ◽  
Hierarchical Tree Structure

To make the word similarity calculated results more reasonable and accurate, a new word similarity algorithm is proposed. It uses HowNet primitive hierarchical tree structure, and calculates the two primitives’ distance with the method computing WordNet node distance which considers the tree depth, density, path and connecting intensity, etc. Moreover, algorithm also improves the method that distance into similarity. Finally, this algorithm is compared with related algorithms through experiment. The results show that the proposed algorithm effectively improves the precision and accuracy of word similarity calculation.

Study on Application of Domain Ontology in Semantic Information Retrieval

2013 ◽  
Vol 433-435 ◽  
pp. 1662-1665
Author(s):  
Huan Hai Yang ◽  
Ming Yu Sun
Keyword(s):  
Information Retrieval ◽  
Semantic Similarity ◽  
Calculation Method ◽  
Semantic Information ◽  
Domain Ontology ◽  
Semantic Retrieval ◽  
Retrieval Model ◽  
Retrieval Method ◽  
Similarity Calculation ◽  
Semantic Information Retrieval

Considering weakness of the traditional retrieval method based on keyword matching, the paper introduced semantic into information retrieval, and proposed a semantic retrieval model based on ontology. The paper offered a construction method of domain ontology and implemented semantic reasoning using Jena and improved a semantic similarity calculation method.

Text Similarity Computation Model for Identifying Rumor Based on Bayesian Network in Microblog

2020 ◽  
Vol 17 (5) ◽  
pp. 731-741
Author(s):  
Chengcheng Li ◽  
Fengming Liu ◽  
Pu Li
Keyword(s):  
Bayesian Network ◽  
Calculation Model ◽  
Computation Model ◽  
Text Similarity ◽  
Data Set ◽  
Proposed Model ◽  
Similarity Calculation ◽  
Similarity Algorithm ◽  
User Data ◽  
And Control

The research of text similarity, especially for rumor texts, which constructed the calculation model by known rumors and calculated its similarity. From which, people can recognize the rumor in advance, and improve their vigilance to effectively block and control rumors dissemination. Based on the Bayesian network, the similarity calculation model of microblog rumor texts was built. At the same time, taking into account not only the rumor texts have similar characters, but also the rumor producers have similar characters, and therefore the similarity calculation model of rumor texts makers was constructed. Then, the similarity between the text and the user was integrated, and the microblog similarity calculation model was established. Finally, also experimentally studied the performance of the proposed model on the microblog rumor text and the user data set. The experimental results indicated that the similarity algorithm proposed in this paper could be used to identify the rumors of texts and predict the characters of users more accurately and effectively

scholarly journals Accurate Estimation of English Word Similarity Based on Semantic Network

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Qifeng Gong
Keyword(s):  
Calculation Method ◽  
Semantic Network ◽  
English Word ◽  
Computing Methods ◽  
Accurate Estimation ◽  
Processing Efficiency ◽  
Web Based ◽  
Word Similarity ◽  
Similarity Calculation ◽  
Translation Accuracy

The application of artificial intelligence in the field of English needs to process a large amount of English text data, but the deviation of English word similarity reduces its overall English translation accuracy and data processing efficiency. Therefore, this paper proposes an accurate estimation of English word similarity based on semantic network, which combines a variety of computing methods to form a compound computing structure based on semantic network. The experimental results show that the error between the Semantic Web-based English word similarity calculation method and manual evaluation is small, and the accuracy of English word similarity calculation is improved to a certain extent. In addition, compared with other English word similarity calculation methods, the English word similarity calculation method based on semantic network is more in line with people’s cognition and understanding of knowledge, has higher reliability, and has certain practical value in the field of English.

scholarly journals Classification of Semantic Similarity Technique between Word Pairs using Word Net

2019 ◽  
Vol 9 (2) ◽  
pp. 4397-4402
Keyword(s):  
Semantic Similarity ◽  
Word Pair ◽  
Similarity Measure ◽  
Digital Data ◽  
Data Set ◽  
Semantic Similarity Measure ◽  
Similarity Algorithm

The concept of relevancy is a most blazing topic in information regaining process. In the last few years there is a drastically increase the digital data so there is a need to increase the accuracy of information regaining process .Semantic Similarity measure the similarity between word-pair by using WordNet as ontology.We have analyzed the different category of semantic similarity algorithm to compute semantic closeness between word-pair and evaluate its value by using WordNet.We have compared various algorithms on Miller- Charles data set of 30 word-pair is used to rank them category wise.

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