Context similarity measure using Fuzzy Formal Concept Analysis

  • Authors:

  • K. Selvi;R. M. Suresh

  • Affiliations:

  • Sathyabama University;IEEE

  • Venue:
  • Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
  • Year:
  • 2012

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Abstract

In information retrieval, one of the main problems is to retrieve a set of documents that is semantically related to a given user query. Efficient estimation of semantic similarity between words is critical for various natural language processing tasks such as Word Sense Disambiguation (WSD), textual entailment and automatic text summarization. We propose an empirical method to estimate semantic similarity using Fuzzy Formal Concept Analysis. Grouping the different lexical patterns enable us to represent a semantic relation between two words accurately. Specifically, we define various word cooccurrence measures using page counts and integrate those with lexical patterns extracted from text snippets. To identify the numerous semantic relations that exist between two given words, we propose a novel pattern extraction algorithm and a pattern clustering algorithm. The optimal combination of page counts-based co-occurrence measures and lexical pattern clusters is learned using support vector machines. The proposed method outperforms various baselines and previously proposed web-based semantic similarity measures on three benchmark data sets showing a high correlation with human ratings. Moreover, the proposed method significantly improves the accuracy in a community mining task.