Volume 11, Issue 1 (3-2019)
itrc 2019, 11(1): 27-35 |
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1- School of Computer Engineering Iran University of Science and Technology
2- School of Computer Engineering Iran University of Science and Technology ,b_minaei@iust.ac.ir
3- NLP Research Lab. Computer Engineering Dept. Shahid Beheshti University
2- School of Computer Engineering Iran University of Science and Technology ,
3- NLP Research Lab. Computer Engineering Dept. Shahid Beheshti University
Abstract: (1897 Views)
Semantic relations between words like synsets are used in automatic ontology production which is a strong tool in many NLP tasks. Synset extraction is usually dependent on other languages and resources using techniques such as mapping or translation. In our proposed method, synsets are extracted merely from text and corpora. This frees us from the need for special resources including Word-Nets or dictionaries. The representation model for words of corpus is based on Vector Space model and the most similar words to each are extracted based on common features count (CFC) using a modified cosine similarity measure. Furthermore, a graph-based soft clustering approach is applied to create clusters of synonymous words.
To examine performance of the proposed method, Extracted synsets were compared to other Persian semantic resources. Results show an accuracy of 80.25%, which indicates improvement in comparison to the 69.5% accuracy of pure clustering by committee method.
To examine performance of the proposed method, Extracted synsets were compared to other Persian semantic resources. Results show an accuracy of 80.25%, which indicates improvement in comparison to the 69.5% accuracy of pure clustering by committee method.
Keywords: Automatic Synset Extraction, Semantic Relation, Graph-based Clustering, CBC clustering, Persian
Type of Study: Research |
Subject:
Information Technology
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