Question Answering Systems supported by Reinforcement and Machine Learning Methods
The construction of Question Answering systems over unstructured and heterogeneous data sources requires the coordination of different Natural Language Processing (NLP) algorithms, data representation models and linguistic resources. The dynamic mapping from highly variable linguistic phenomena to the appropriate semantic interpretation method requires methods which are designed to cope with high semantic heterogeneity and complexity.
This project aims at building the next generation of Question Answering systems by developing a semantic parsing model which can be used for the interpretation of semantically heterogeneous data sources. The semantic parsing method will explore the fascinating intersection between explicit semantic representation models and machine/reinforcement learning models as well as the use of large bases of linguistic/data/distributional resources in the interpretation process.
Applicants are expected to have:
* An excellent undergraduate degree in Computer Science or Mathematics (or related discipline), and preferably, a relevant M.Sc. degree.
* Confidence and independence in programming complex systems in Java or Python. Industry experience is a plus.
* Previous academic or industry experience in Natural Language Processing or Machine Learning (desired).
* Excellent report writing and presentation skills.
Please note that applicants must additionally satisfy the standard requirements for postgraduate studies at the University of Manchester, such as a first-class or high upper-second class (or an equivalent international qualification) and English language qualifications, as stated in the PGR guidelines.
Qualified applicants are strongly encouraged to informally contact Andre Freitas ([Email Address Removed]) to discuss the application prior to applying.
Candidates who have been offered a place for PhD study in the School of Computer Science may be considered for funding by the School. Further details on School funding can be found at: http://www.cs.manchester.ac.uk/study/postgraduate-research/programmes/phd/funding/school-studentships/.
How good is research at University of Manchester in Computer Science and Informatics?
FTE Category A staff submitted: 44.86
Research output data provided by the Research Excellence Framework (REF)
Click here to see the results for all UK universities