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Computing semantic similarity measure between words using web search engine.
| Content Provider | CiteSeerX |
|---|---|
| Abstract | Semantic Similarity measures between words plays an important role in information retrieval, natural language processing and in various tasks on the web. In this paper, we have proposed a Modified Pattern Extraction Algorithm to compute the supervised semantic similarity measure between the words by combining both page count method and web snippets method. Four association measures are used to find semantic similarity between words in page count method using web search engines. We use a Sequential Minimal Optimization (SMO) support vector machines (SVM) to find the optimal combination of page counts-based similarity scores and top-ranking patterns from the web snippets method. The SVM is trained to classify synonymous word-pairs and non-synonymous word-pairs. The proposed Modified Pattern Extraction Algorithm outperforms by 89.8 percent of correlation value. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Page Count Method Web Snippet Important Role Modified Pattern Extraction Algorithm Modified Pattern Extraction Algorithm Outperforms Support Vector Machine Web Search Engine Correlation Value Semantic Similarity Natural Language Processing Sequential Minimal Optimization Page Counts-based Similarity Score Information Retrieval Optimal Combination Non-synonymous Word-pairs Various Task Supervised Semantic Similarity Measure Top-ranking Pattern Semantic Similarity Measure Synonymous Word-pairs Association Measure |
| Content Type | Text |
