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An index-based algorithm for fast on-line query processing of latent semantic analysis.

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This study introduces ILSA, an efficient algorithm for Latent Semantic Analysis (LSA) that speeds up document similarity searches. ILSA significantly reduces processing time for large datasets by optimizing similarity computations.

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Area of Science:

  • Information Retrieval
  • Natural Language Processing
  • Computational Linguistics

Background:

  • Latent Semantic Analysis (LSA) is a standard technique for semantic similarity search.
  • Existing LSA methods face performance challenges with large datasets due to inefficient query processing.

Purpose of the Study:

  • To enhance the efficiency of on-line query processing in Latent Semantic Analysis.
  • To develop a faster method for searching semantically similar documents.

Main Methods:

  • Rewriting the LSA similarity equation using partial similarities stored in a partial index.
  • Developing an efficient algorithm for building the partial index, skipping low-value similarities.
  • Introducing the ILSA algorithm for fast on-line query processing by accumulating partial similarities.

Main Results:

  • ILSA significantly reduces the time cost of on-line query processing compared to standard LSA.
  • The algorithm effectively prunes unpromising candidate documents and skips non-contributory operations.
  • Experimental results demonstrate the efficiency and effectiveness of ILSA.

Conclusions:

  • ILSA offers a substantial improvement in the speed and efficiency of semantic document search.
  • The proposed partial index and accumulation method provide a scalable solution for LSA-based retrieval.
  • ILSA is effective for large-scale information retrieval tasks requiring fast response times.