Document Relevance Evaluation via Term Distribution Analysis Using Fourier Series Expansion

TL;DR

This paper introduces a novel Fourier series-based method for analyzing term distributions in documents to improve relevance evaluation and ranking in information retrieval systems.

Contribution

It proposes a new approach using Fourier series to model term distributions and two methods for relevance optimization, enhancing document retrieval accuracy.

Findings

Fourier series effectively models term distributions in documents.

The proposed methods improve retrieval relevance and ranking.

Experimental results confirm the approach's effectiveness.

Abstract

In addition to the frequency of terms in a document collection, the distribution of terms plays an important role in determining the relevance of documents for a given search query. In this paper, term distribution analysis using Fourier series expansion as a novel approach for calculating an abstract representation of term positions in a document corpus is introduced. Based on this approach, two methods for improving the evaluation of document relevance are proposed: (a) a function-based ranking optimization representing a user defined document region, and (b) a query expansion technique based on overlapping the term distributions in the top-ranked documents. Experimental results demonstrate the effectiveness of the proposed approach in providing new possibilities for optimizing the retrieval process.