Difference between revisions of "Projects:2017s1-150 Statistical Natural Language Processing"
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This project aims to investigate syntactic methods for classifying English language documents. Background research in Natural Language Processing (NLP) will be undertaken, and candidate methods suitable for document classification will be selected. On-line resources such as large corpuses of documents labelled according to various categories will be used to test these methods for classifying unlabelled documents. The performance of the selected methods will be assessed using statistical techniques. The project also aims to develop new ideas to improve the performance of selected methods. | This project aims to investigate syntactic methods for classifying English language documents. Background research in Natural Language Processing (NLP) will be undertaken, and candidate methods suitable for document classification will be selected. On-line resources such as large corpuses of documents labelled according to various categories will be used to test these methods for classifying unlabelled documents. The performance of the selected methods will be assessed using statistical techniques. The project also aims to develop new ideas to improve the performance of selected methods. | ||
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| + | '''Group members''' | ||
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| + | Zheng Li | ||
| + | Runsheng Liu | ||
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| + | '''Supervisor''' | ||
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| + | ---- | ||
| + | Prof Lang White | ||
Revision as of 13:03, 15 September 2017
Description
This project aims to investigate syntactic methods for classifying English language documents. Background research in Natural Language Processing (NLP) will be undertaken, and candidate methods suitable for document classification will be selected. On-line resources such as large corpuses of documents labelled according to various categories will be used to test these methods for classifying unlabelled documents. The performance of the selected methods will be assessed using statistical techniques. The project also aims to develop new ideas to improve the performance of selected methods.
Group members
Zheng Li Runsheng Liu
Supervisor
Prof Lang White
