Contextual Information Retrieval based on Algorithmic Information Theory and Statistical Outlier Detection

TL;DR

This paper introduces a novel information retrieval method combining Algorithmic Information Theory, statistical outlier detection, and innovative database structuring to improve retrieval accuracy for long text queries.

Contribution

It presents an integrated IR approach using NCD, outlier detection, and new database organization, addressing false positives and similarity measurement issues.

Findings

Effective detection of false positives in low-distance scenarios

Improved database structuring based on text length

Experimental validation of retrieval improvements

Abstract

The main contribution of this paper is to design an Information Retrieval (IR) technique based on Algorithmic Information Theory (using the Normalized Compression Distance- NCD), statistical techniques (outliers), and novel organization of data base structure. The paper shows how they can be integrated to retrieve information from generic databases using long (text-based) queries. Two important problems are analyzed in the paper. On the one hand, how to detect "false positives" when the distance among the documents is very low and there is actual similarity. On the other hand, we propose a way to structure a document database which similarities distance estimation depends on the length of the selected text. Finally, the experimental evaluations that have been carried out to study previous problems are shown.