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.
Contact details
Should you need more information, please email [Email Address Removed].
The application must include:
Research project outline of 2 pages (list of references excluded). The outline may provide details about
- Background and motivation, explaining the importance of the project, should be supported also by relevant literature. You can also discuss the applications you expect for the project results.
- Research questions or
- Methodology: types of data to be used, approach to data collection, and data analysis methods.
- List of references
The outline must be created solely by the applicant. Supervisors can only offer general discussions about the project idea without providing any additional support.
- Statement no longer than 1 page describing your motivations and fit with the project.
- Recent and complete curriculum vitae. The curriculum must include a declaration regarding the English language qualifications of the candidate.
- Supporting documents will have to be submitted by successful candidates.
- Two academic references (but if you have been out of education for more than three years, you may submit one academic and one professional reference), on the form can be downloaded here.
Applications can be submitted here. To be considered, the application must use:
- “SCEBE1123” as project code.
- the advertised title as project title
All applications must be received by 3rd December 2023. Applicants who have not been contacted by the 8th March 2024 should assume that they have been unsuccessful. Projects are anticipated to start on 1st October 2024.
Download a copy of the project details here.
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