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In information retrieval (IR), query performance prediction (QPP) aims to predict the search effectiveness for a given query without resorting to relevance judgments. QPP may be advantageous in many ways, such as signaling an IR system whether a search query would be effective or underperforming. Based on that information, the system can either apply a query reformulation [6] or an adaptive retrieval configuration [1,4] or engage in an interactive session with the user (i.e., conversational search [7]) to understand the search intent and provide a better search experience.
Predicting query performance is a challenging problem due to many characteristics of queries, collections, and search systems. Existing QPPs are extracted from traditional retrieval models (e.g., BM25 or Divergence from randomness) using the pre-retrieval features based on the collection statistics or the post-retrieval features based on the top-retrieved documents [2, 3]. With the advent of language models (e.g., BERT [6]), neural IR models have been proposed and shown to have better retrieval effectiveness [8]. However, QPP on the neural retrieval model has not yet been explored [9].
This Ph.D. project aims to develop neural query performance predictors from neural IR models and combine them with existing QPPs based on traditional IR models. Experiments could be conducted on standard TREC collections (e.g., MS MARCO and TREC Deep learning tracks) to demonstrate the effectiveness of the QPPs and compare them with the state-of-the-art approaches. Another goal of this project would be to design appropriate metrics to evaluate the QPPs and apply them to Conversational search [7].
Prospective applicants are encouraged to contact the Supervisor before submitting their applications. Please consult with the Supervisor if you want to work on the broad areas of IR and NLP.
Academic qualifications
A first-class honours degree, or a distinction at master level, or equivalent achievements in Computer Science or Data Science or Software Engineering.
English language requirement
If your first language is not English, comply with the University requirements for research degree programmes in terms of English language.
Application process
Prospective applicants are encouraged to contact the supervisor, Dr. Md Zia Ullah ([Email Address Removed]) to discuss the content of the project and the fit with their qualifications and skills before preparing an application.
The application must include:
Research project outline of 2 pages (list of references excluded). The outline may provide details about
The outline must be created solely by the applicant. Supervisors can only offer general discussions about the project idea without providing any additional support.
Applications can be submitted here.
Download a copy of the project details here.
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