Artificial Intelligence | Natural Language Processing Stanford course with Christopher D. Manning.
From the website:
This course is designed to introduce students to the fundamental concepts and ideas in natural language processing (NLP), and to get them up to speed with current research in the area. It develops an in-depth understanding of both the algorithms available for the processing of linguistic information and the underlying computational properties of natural languages. Wordlevel, syntactic, and semantic processing from both a linguistic and an algorithmic perspective are considered. The focus is on modern quantitative techniques in NLP: using large corpora, statistical models for acquisition, disambiguation, and parsing. Also, it examines and constructs representative systems.
Only the lecture notes, quizzes, etc. are available. Update: 29 April 2011 – Lecture notes, quizzes, and Video’s of the lectures are online.
Still, quite an interesting resource.
I am particularly interested in Manning’s approach of not building the class around an edifice to be mastered but rather around problems to be solved.
As primarily a theorist that is rather disturbing but at the same time, it is strangely attractive.
Wondering what a topic map class would look like that started with two or even three related but distinct data sets?
The sort of data sets that lead to topic maps and to walk through what problems we want to solve and unfold topic maps along the way.
Would be an opportunity to use other software, indexing software for example, to see how they compare with topic maps or can be used in their construction.
Thoughts, suggestions, comments?
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