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courses:cs366:syllabus [2020/01/08 17:03]
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courses:cs366:syllabus [2020/01/21 17:28]
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 == Text == == Text ==
  
-  * Dan Jurafsky and James Martin, [[http://​www.cs.colorado.edu/​%7Emartin/​slp.html | Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics,​ and Speech Recognition ​ ]], Second Edition. ​Required(available in the Book Store).+  * Dan Jurafsky and James Martin, [[http://​www.cs.colorado.edu/​%7Emartin/​slp.html | Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics,​ and Speech Recognition ​ ]], Second Edition, and [[https://​web.stanford.edu/​~jurafsky/​slp3/​ | selected chapters online from the upcoming 3rd edition]].
   * We will also use [[http://​www.nltk.org/​book/​ | the online book associated with the Natural Language ToolKit (NLTK)]].   * We will also use [[http://​www.nltk.org/​book/​ | the online book associated with the Natural Language ToolKit (NLTK)]].
  
 == Other useful books and materials == == Other useful books and materials ==
- +  ​DickinsonBrewand Meuers. [[https://​www.wiley.com/en-us/Language+and+Computers-p-9781405183055 ​Language ​and Computers.]] Wiley Press.
-  ​WittenI. H.Eibe, F., Hall, M.A. [[https://​www.cs.vassar.edu/~cs366/docs/Data-Mining-Weka.pdf ​Data Mining: Practical Machine Learning Tools and Techniques]], Third Edition.+
   * Manning, C. and Schütze, H.  [[https://​nlp.stanford.edu/​fsnlp/​ | Foundations of Statistical Natural Language Processing.]] MIT Press.   * Manning, C. and Schütze, H.  [[https://​nlp.stanford.edu/​fsnlp/​ | Foundations of Statistical Natural Language Processing.]] MIT Press.
 +  * Witten, I. H., Eibe, F., Hall, M.A. [[https://​www.elsevier.com/​books/​data-mining/​witten/​978-0-12-804291-5 | Data Mining: Practical Machine Learning Tools and Techniques]],​ Fourth Edition.
   * Lutz and Ascher, [[http://​shop.oreilly.com/​product/​9780596002817.do | Learning Python]], O'​Reilly.   * Lutz and Ascher, [[http://​shop.oreilly.com/​product/​9780596002817.do | Learning Python]], O'​Reilly.
  
 == Software == == Software ==
  
 +In this course we will use Python, together with the Natural Language Toolkit (NLTK). It is assumed that you are either familiar with Python already, or, due to your excellent preparation in the Vassar Computer Science program, you have the ability to learn it quickly.
 You can use the computers in the Asprey Lab in the Computer Science Department, which have all of the required software already installed. If you want to use your own computer, you should install the following: You can use the computers in the Asprey Lab in the Computer Science Department, which have all of the required software already installed. If you want to use your own computer, you should install the following:
  
   * The Natural Language Toolkit--NLTK (available [[http://​nltk.org/​install | here ]]) and the NLTK data (available [[http://​nltk.org/​data | here ]]). If not already installed on your computer, you will also need to install [[http://​www.python.org/​download/​ | Python]].   * The Natural Language Toolkit--NLTK (available [[http://​nltk.org/​install | here ]]) and the NLTK data (available [[http://​nltk.org/​data | here ]]). If not already installed on your computer, you will also need to install [[http://​www.python.org/​download/​ | Python]].
- 
-  * Weka for machine learning, available [[ http://​sourceforge.net/​projects/​weka/​ | here ]]. 
- 
-  * (optional) the General Architecture for Text Engineering (GATE), available [[http://​gate.ac.uk|here]]. ​ 
  
 == Discussion == == Discussion ==
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 == Grades == == Grades ==
  
-Grades will be determined on the basis of performance on four assignments (40%), a final project and presentation (50%), and homework (10%). The [[courses:​cs3662020B:​schedule|Schedule of Lectures, Readings, and Assignments]] contains links to assignments,​ homework, lecture slides, and specifies due dates, etc. Information may also be disseminated via the [[https://​groups.google.com/​forum/#​!forum/​cs366-computational-linguistics-2020 | CS366 mailing list]].+Grades will be determined on the basis of performance on four assignments (40%), a final project and presentation (50%), and homework (10%). The [[https://​www.cs.vassar.edu/​~cs366/​schedule.html |Schedule of Lectures, Readings, and Assignments]] contains links to assignments,​ homework, lecture slides, etc. Information may also be disseminated via the [[https://​groups.google.com/​forum/#​!forum/​cs366-computational-linguistics-2020 | CS366 mailing list]].