Information Retrieval via Truncated Hilbert-Space Expansions

TL;DR

This paper introduces a novel method for representing term positions in documents using truncated Hilbert-space expansions, enabling efficient relevance evaluation and supporting applications like ranking, query expansion, and clustering.

Contribution

The paper presents a new approach for term-position representation that improves efficiency and supports multiple information retrieval applications.

Findings

Effective ranking optimization based on term distributions

Improved query expansion through distribution overlap

Successful cluster analysis of terms in documents

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. In this paper, a new approach for representing term positions in documents is presented. The approach allows an efficient evaluation of term-positional information at query evaluation time. Three applications are investigated: a function-based ranking optimization representing a user-defined document region, a query expansion technique based on overlapping the term distributions in the top-ranked documents, and cluster analysis of terms in documents. Experimental results demonstrate the effectiveness of the proposed approach.