信号处理原理

SIST Spring Course: Natural Language Processing

Course Description

Natural language processing is a research involving computer science, linguistics and cognitive science. This course gives systematic introduction of research topics on natural language processing, including morphology, lexicology, syntax, semantics, pragmatics and dialogue. To help the master students understand the natural language processing techniques easily, this course focuses on real applications and systems in course lectures. The following points make this course worth selecting.   

1. This course makes use of the classroom as technologic understanding and discussion and the lab for practicing on real natural language processing algorithms and systems. Students are encouraged to come up with novel ideas in course projects. And three special interest groups will be setup to accomplish three interesting projects.
2. Information presented in this course comes from most recent publications on natural language processing. This is helpful for students to grasp technological core as well as popular trends in this area.
3. This course will be presented in English. On the one hand, master students from oversea are encouraged to attend without special training on Chinese language.  On the other hand, this course is also helpful to Chinese native speakers to follow international development of this research area. In course project, they will also be trained on English communication.  

This course targets at master students from departments of computer science, automation and electronics who already have theoretical background of artificial intelligence. Besides, this course is also very helpful for master students from department of Chinese language whose major is computation linguistics.  

Syllabus

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Week	Topic
1	Course Introduction NLPRecent Development of Natural Language Processing Research
2	Lexical Analysis on Chinese Text and ICTCLAS
3	Dependency parsing and HIT Dpaser
4	WordNet and HowNet, the semantic knowledge base and applications
5	Guest Lecture on Natural Language Dialogue System
6	Corpus and Corpus based NLP Techniques
7	Guest Lecture on Machine Translation
8	Information Extraction Technologies
9	Machine Learning Approaches to NLP
10	Modern Information Retrieval
11	Text classification and clustering
12	Guest Lecture on NLP evaluation
13	Text Mining
14	Course project presentation (1)
15	Course project presentation (2)
16	Course project presentation (3)

 Reference textbooks

1. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition by Daniel Jurafsky and James H. Martin, Prentice Hall Press
2. Foundation of Statistical Natural Language Processing by Christopher D. Manning and Hinrich Schütze，MIT Press.
3. Foundations of Computational Linguistics: Human-Computer Communication in Natural Language by Roland Hausser, Springer.
4. Handbook for Natural Language Processing, edited by Robert Dale, Springer.