A Study of Query Expansion Strategies Effectiveness based on Natural Language Processing

The Information Retrieval (IR) process starts with the query entered by the user in the system. The IR system is mostly used in the search string in text utilities and web search engines. For IR algorithms to efficiently retrieve pertinent documents, the documents are usually converted into an appropriate representation. Query expansion and Natural Language Processing (NLP) are the most researched fields in Artificial Intelligence (AI). The query expansion process provides the additional information that is understood by the user in their query. This research study explores several NLP query expansion techniques that utilize word embedding methods for autonomous query expansion including deep learning, machine learning, and ontology approach. This research also discusses the importance of query expansion to improve search efficacy and provides a coherent view of NLP-based query expansion methods that leverage a range of data sources and methodologies.