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Dr Le An Ha
L/SL in Commercial Exploration
Research Institute in Information and Language Processing
RIILP - RGCL - University of Wolverhampton, UK
The subject of my PhD thesis is automatic terminology processing. In my PhD, “knowledge patterns”, which are extracted (semi)automatically from resources, are used to improve the performance of a terminology processing system. I have been working and publishing on a variety of subjects, including automatic terminology extraction, both monolingual and multilingual, multiple-choice question generation, analysis of multiple-choice test items, and multilingual preprocessing. I have extensive experience in developing commercial Natural Language Processing (NLP) vertical solutions. I have acted as an acting coordinator of an EU Leonardo project (TELLME) which developed a range of products including work-related language exercises, showcasing NLP technologies such as automatic term extraction and MCQ generation. I have developed a Computer-Aided Patient Notes Scoring System for a well-known US medical examiners organisation. I have been leading research activities in the domain of applying NLP technologies for licensing testing, funded by an US organization on a yearly rolling research contract, including American-British transliteration, information extraction, item difficulty prediction, item response time prediction, and item distractor prediction. I have experiences on utilizing eye tracking data for various tasks. I have extensive experiences in machine learning techniques algorithms, and platforms, both traditional (svm, linear regression, random forest, weka etc.) and recently developed (deep learning, tensorflow, keras).
I have taken part in the teaching of an European Master Programme in Computational Linguistics and currently supervising two PhD student (I has supervised three completed PhD projects). I also have a degree in Economy (specialised in International Trade).
Multiple choice question generation; Multiple choice question analysis; Machine learning; Deep learning; NLP applications;