Recent Query Reformulation Approaches for Information Retrieval System - A Survey

Abstract

Around trillions of data are uploaded to the internet every year. Extracting useful information using only a few keywords has become a major challenge. The field of Query Reformulation (QR) is focused on the efficient retrieval of information to overcome this. It is widely used in the domain of information retrieval (IR) and related fields such as search engines, multimedia IR, cross-language IR, recommender systems, and so on. Query reformulation technique incur extra computational cost. Due to this reason, the use of query reformulation techniques is sometimes prohibited in internet searches as searching over the internet requires fast response time. But due to success of NLP (Natural Language Processing) using machine learning/deep learning in recent years, there has been a boom of study in this area. In this literature, a variety of term selection, term extraction, and query reformulation strategies based on recent technologies used by researchers have been presented, necessitating a wide survey to focus research in this promising area. Recent QR approaches along with the datasets, techniques and evaluation metrics used are provided in this paper that will help researchers to understand and to focus more on research in this promising area so that better solution will be proposed in future. From the survey, it may be observed that one of the hottest subjects in the field of IR right now is applying deep learning to IR system for query reformulation.

A deep learning approach for Named Entity Recognition in Urdu language

“…Named Entity Recognition (NER) is one of the leading research areas in the NLP domain that extracts the named entities (NEs) from that data and classifies them into a predefined set of categories [1]. NER plays a crucial role in many NLP applications including question answering [2][3][4], query auto-completion systems [5][6][7], entity linking [8][9][10], and search engines [11][12][13] and the accurate classification of NEs depends significantly on extracting the NEs correctly. However, the complexities in extracting and classifying NEs vary across languages.…”