Evaluation of a Deep Reinforcement Learning Method For Query Reformulation
Lee, Johnny Flame

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Cite this item:
Lee, J. F. (2020). Evaluation of a Deep Reinforcement Learning Method For Query Reformulation (Thesis, Master of Science). University of Otago. Retrieved from http://hdl.handle.net/10523/10051
Permanent link to OUR Archive version:
http://hdl.handle.net/10523/10051
Abstract:
Recent research suggests that a query reformulation system based on deep reinforcement learning has been effective at improving search performance, claiming superior performance to “traditional” techniques such as BM25. However, there is strong evidence to suggest that BM25 has not been systematically outperformed since the early 1990s because it is possibly the upper bound of ad-hoc retrieval. Given these two contrasting claims, we carry out our own investigations in this thesis by implementing a query reformulation system based on deep reinforcement learning and conduct a series of experiments with the goal of gathering further empirical evidence on the subject.
Date:
2020
Advisor:
Trotman, Andrew; Szymanski, Lech
Degree Name:
Master of Science
Degree Discipline:
Computer Science
Publisher:
University of Otago
Research Type:
Thesis
Languages:
English
Collections
- Computer Science [82]
- Thesis - Masters [3371]