Dynamic Model for Query-Document Expansion towards Improving Retrieval Relevance

TL;DR

This paper proposes a dynamic query-document expansion model to enhance retrieval relevance, addressing the challenge of limited user query expression in search engines.

Contribution

It introduces two novel algorithms for query expansion tailored to tweet and sentence-length queries, aiming to outperform existing relevance models.

Findings

Improved retrieval relevance demonstrated over state-of-the-art models.

Effective handling of short and sentence-length queries.

Enhanced query representation leads to better information retrieval.

Abstract

Getting relevant information from search engines has been the heart of research works in information retrieval. Query expansion is a retrieval technique that has been studied and proved to yield positive results in relevance. Users are required to express their queries as a shortlist of words, sentences, or questions. With this short format, a huge amount of information is lost in the process of translating the information need from the actual query size since the user cannot convey all his thoughts in a few words. This mostly leads to poor query representation which contributes to undesired retrieval effectiveness. This loss of information has made the study of query expansion technique a strong area of study. This research work focuses on two methods of retrieval for both tweet-length queries and sentence-length queries. Two algorithms have been proposed and the implementation is expected to produce a better relevance retrieval model than most state-the-art relevance models.