@article{3477,
  author = {Ayesha Banu Mohd},
  title = {A Comparison of Semantic Similarity Measures},
  journal = {International Journal of Computational Linguistics Research},
  year = {2022},
  volume = {13},
  number = {1},
  doi = {https://doi.org/10.6025/jcl/2022/13/1/1-8},
  url = {https://www.dline.info/jcl/fulltext/v13n1/jclv13n1_1.pdf},
  abstract = {In natural language processing, identifying semantic similarity is a major challenge for which several measures have been introduced and tested. There are four types of semantic measures which includes the following. First are the Ontology Based measures that depend on the closeness of the concepts in the taxonomy. Ontology-based measure detects the similarity in terms of the path linking the concepts and position of the concept in the hierarchy. The second one is related to the Information Content of concepts is considered to find the semantic similarity. The third and fourth includes the Featurebased similarity and Hybrid similarity measures. Semantic similarity measurements have wider impact in other areas such as data mining, computational intelligence, linguistics, information retrieval systems and so on. Whatever the measure is used, arriving at the quality is a prominent question. For identifying the effectiveness, the systems like the Psycholinguistic evaluation is used to justify the similarity measure quality. The comparison of the proposed semantic similarity measure values is being carried out with the expert opinion. The values are statistically tested suing Pearson Correlation Coefficient is used to test the quality of the similarity measure. When the correlation between the computational method value and the human assessment values. This work described the proposed ASC semantic similarity measure and its Psycholinguistic evaluation versus the opinion of the experts.},
}