Learning Better Context Characterizations: An Intelligent Information Retrieval Approach

TL;DR

This paper introduces an incremental approach for intelligent systems to learn more effective context descriptions by refining vocabulary through search queries, improving relevance in information retrieval.

Contribution

It presents a novel method for dynamically enhancing thematic context descriptions using search engine feedback, outperforming initial simple descriptions.

Findings

Learned vocabulary significantly improves retrieval effectiveness.

Method adapts to various topics with consistent performance.

Refinement process enhances query relevance.

Abstract

This paper proposes an incremental method that can be used by an intelligent system to learn better descriptions of a thematic context. The method starts with a small number of terms selected from a simple description of the topic under analysis and uses this description as the initial search context. Using these terms, a set of queries are built and submitted to a search engine. New documents and terms are used to refine the learned vocabulary. Evaluations performed on a large number of topics indicate that the learned vocabulary is much more effective than the original one at the time of constructing queries to retrieve relevant material.