Event Detection through Lexical Chain Based Semantic Similarity Algorithm

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.

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A GRAPH-BASED SEMANTIC SIMILARITY MEASURE FOR THE GENE ONTOLOGY

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720011005641 ◽

2011 ◽

Vol 09 (06) ◽

pp. 681-695 ◽

Cited By ~ 15

Author(s):

MARCO A. ALVAREZ ◽

CHANGHUI YAN

Keyword(s):

Gene Ontology ◽

Semantic Similarity ◽

Common Ancestor ◽

State Of The Art ◽

Sequence Similarity ◽

Similarity Score ◽

Gene Products ◽

Semantic Similarity Measure ◽

Similarity Algorithm ◽

Go Terms

Existing methods for calculating semantic similarities between pairs of Gene Ontology (GO) terms and gene products often rely on external databases like Gene Ontology Annotation (GOA) that annotate gene products using the GO terms. This dependency leads to some limitations in real applications. Here, we present a semantic similarity algorithm (SSA), that relies exclusively on the GO. When calculating the semantic similarity between a pair of input GO terms, SSA takes into account the shortest path between them, the depth of their nearest common ancestor, and a novel similarity score calculated between the definitions of the involved GO terms. In our work, we use SSA to calculate semantic similarities between pairs of proteins by combining pairwise semantic similarities between the GO terms that annotate the involved proteins. The reliability of SSA was evaluated by comparing the resulting semantic similarities between proteins with the functional similarities between proteins derived from expert annotations or sequence similarity. Comparisons with existing state-of-the-art methods showed that SSA is highly competitive with the other methods. SSA provides a reliable measure for semantics similarity independent of external databases of functional-annotation observations.

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A Semantic Similarity Algorithm Based on the Nearest Common Ancestor Node

DEStech Transactions on Computer Science and Engineering ◽

10.12783/dtcse/wcne2017/19882 ◽

2018 ◽

Author(s):

Zhe ZHANG ◽

Yun-xiao ZU ◽

Bin HOU

Keyword(s):

Semantic Similarity ◽

Common Ancestor ◽

Similarity Algorithm

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The Topic Tracking Based on Semantic Similarity of Sememe�s Lexical Chain

Proceedings of the 2nd International Conference on Software Engineering, Knowledge Engineering and Information Engineering (SEKEIE 2014) ◽

10.2991/sekeie-14.2014.28 ◽

2014 ◽

Author(s):

Jing Ma ◽

Fei Wu ◽

Chi Li

Keyword(s):

Semantic Similarity ◽

Topic Tracking ◽

Lexical Chain

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An improved semantic similarity algorithm based on HowNet and CiLin

MATEC Web of Conferences ◽

10.1051/matecconf/202030903004 ◽

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.

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