Econo-ESA in semantic text similarity.

Area of Science:

• Natural Language Processing
• Computational Linguistics
• Information Retrieval

Background:

• Explicit Semantic Analysis (ESA) relies on large Wikipedia index matrices for text interpretation and similarity measurement.
• The computational cost of ESA's matrix multiplication and high-dimensional vector comparisons is prohibitively expensive.
• Existing ESA methods face challenges in scalability due to high resource demands.

Purpose of the Study:

• To propose and evaluate an economical scheme for Explicit Semantic Analysis (econo-ESA).
• To investigate the impact of dimensional reduction on ESA's performance and cost.
• To assess the effectiveness of econo-ESA across diverse semantic text similarity datasets.

Main Methods:

• Developed econo-ESA, a computationally efficient variant of Explicit Semantic Analysis.
• Implemented dimensional reduction techniques on ESA's high-dimensional concept vectors.
• Conducted experiments using eight diverse test collections for semantic text similarity evaluation.

Main Results:

• Dimensionality reduction significantly impacts the results of semantic text similarity.
• Test collection characteristics also play a crucial role in the performance of ESA methods.
• econo-ESA demonstrates a notable decrease in computational cost with only minor reductions in accuracy compared to standard ESA.

Conclusions:

• econo-ESA offers a practical and cost-effective alternative to traditional Explicit Semantic Analysis.
• Strategic dimensional reduction is key to optimizing ESA for real-world applications.
• The proposed method balances computational efficiency with semantic accuracy for text similarity tasks.