  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.
