• SHIVA PRASAD YADAV
• RAMESH BHANOTHU

Using page counts and text samples from two phrases, an internet search engine generated an empirical technique for measuring semantic similarity. It accomplishes this by using page counts to create numerous word co-occurrence metrics and combines them with lexical patterns extracted from text snippets. To discover the many semantic linkages that exist between two supplied words, a novel pattern extraction technique and a pattern clustering algorithm are created. Random forest are used to find the optimal combination of co-occurrence metrics based on page counts and lexical pattern clusters. The proposed technique improves accuracy significantly in a group mining job. In a community extraction task, the recommended semantic similarity measure is used to discover links between elements, especially persons. The proposed approach outperforms the baselines with statistically significant accuracy and recall values. The findings of the community mining challenge show that the proposed approach may be used to compare the semantic similarity of not just words, but also named things for which manually constructed lexical ontologies are either lacking or incomplete.

LEXICAL PATTERN EXTRACTION USING RANDOM FOREST

"LEXICAL PATTERN EXTRACTION USING RANDOM FOREST", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 2, page no.49-54, February-2019, Available :http://www.jetir.org/papers/JETIRFE06010.pdf

"LEXICAL PATTERN EXTRACTION USING RANDOM FOREST", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 2, page no. pp49-54, February-2019, Available at : http://www.jetir.org/papers/JETIRFE06010.pdf