Survey on Semantic Similarity Based on Document Clustering
Karwan Jacksi
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Abstract
Clustering is a branch of data mining which involves grouping similar data in a collection known as cluster. Clustering can be used in many fields, one of the important applications is the intelligent text clustering. Text clustering in traditional algorithms was collecting documents based on keyword matching, this means that the documents were clustered without having any descriptive notions. Hence, non-similar documents were collected in the same cluster. The key solution for this problem is to cluster documents based on semantic similarity, where the documents are clustered based on the meaning and not keywords. In this research, fifty papers which use semantic similarity in different fields have been reviewed, thirteen of them that are using semantic similarity based on document clustering in five recent years have been selected for a deep study. A comprehensive literature review for all the selected papers is stated. A comparison regarding their algorithms, used tools, and evaluation methods is given. Finally, an intensive discussion comparing the works is presented.
Key takeaways
- The objects can cluster in traditional algorithm, but the result of clustering cannot give description concept.
- For this paper we reviewed many papers that used semantic similarity but we choose only thirteen systems in these five recent years that using semantic similarity for clustering propose and we give some important information of each of them.
- As [40], the approach for semantic document clustering based on the graph similarity.
- The table below show the survey of approaches that using semantic similarity based on clustering.
- In this paper, after we reviewed all that papers, we conclude that each paper has specific approaches and various tools and measures are available for each approach, and as it can be seen that most popular steps are used in approaches were preprocessing to transform the document in better format and different steps are used in preprocessing like removing stop words, stemming, tokenization etc., word sense disambiguation are used for solving the synonymy and polysemy problems and feature selection another important step that many of approaches was using tf-idf for this case and there was another like Var-TFIDF, heuristic selection etc. however one of the most popular tools used is WordNet the English dataset for meaningful clustering, then clustering done by using clustering algorithms also the most usage one was K-mean algorithm because of its simplicity to use and can give tighter cluster as well as there is another like bisecting K-mean, fuzzy c-mean, hierarchical agglomerative clustering etc. finally, as we see that the main goal of all approaches trying to a chive a better efficiency, accuracy and quality of clustering.
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