Natural Language Processing
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This course covers a broad range of topics in natural language processing, including word and sentence tokenization, text classification and sentiment analysis, spelling correction, information extraction, parsing, meaning extraction, and question answering, We will also introduce the underlying theory from probability, statistics, and machine learning that are crucial for the field, and cover fundamental algorithms like n-gram language modeling, naive bayes and maxent classifiers, sequence models like Hidden Markov Models, probabilistic dependency and constituent parsing, and vector-space models of meaning.
We are offering this course on Natural Language Processing free and online to students worldwide, continuing Stanford's exciting forays into large scale online instruction. Students have access to screencast lecture videos, are given quiz questions, assignments and exams, receive regular feedback on progress, and can participate in a discussion forum. Those who successfully complete the course will receive a statement of accomplishment. Taught by Professors Jurafsky and Manning, the curriculum draws from Stanford's courses in Natural Language Processing. You will need a decent internet connection for accessing course materials, but should be able to watch the videos on your smartphone.
The following topics will be covered in the first two weeks:
- Introduction and Overview:
- Basic Text Processing: J+M Chapters 2.1, 3.9; MR+S Chapters 2.1-2.2
- Minimum Edit Distance: J+M Chapter 3.11
- Language Modeling: J+M Chapter 4
- Spelling Correction: J+M Chapters 5.9, Peter Norvig (2007) How to Write a Spelling Corrector
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这门课程的授课老师是斯坦福教授Dan Jurafsky和Christopher Manning，两位都是NLP领域的大大牛，其他不说，仅仅是他们写的书应该是很多NLPer的入门书：前者写了《自然语言处理综论》，后者写了《统计自然语言处理基础》
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