Professor: Richard Wicentowski
Office: Science Center 251
Phone: (610) 690-5643
Office hours: Tuesday 2:30-4:00 pm and by appointment
Room: Science Center 128
Class Time: Tuesday, Thursday 1:15pm–2:30pm
Lab Times: Wednesday 4:00pm–5:00pm; Thursday 2:30m–4:00pm
Text: Jurafsky and Martin, Speech and Language Processing, 2nd edition
|WEEK||DAY||ANNOUNCEMENTS||TOPIC & READING||LABS|
|1||Aug 31||* J&M, Chapters 1-2: Introduction, Regular Expressions
* Lee, L., 2004. "I'm sorry Dave, I'm afraid I can't do that": Linguistics, Statistics, and Natural Language Processing circa 2001 (2up). Computer Science: Reflections on the Field, Reflections from the Field, pp. 111-118.
* (Reference) Mertz, D., 2003. Text Processing in Python, Chapter 3.
|2||Sep 07||* J&M, Chapter 4: Maximum Likelihood Estimation (MLE), N-gram models for generation and prediction, smoothing, Good-Turing, Kneser-Ney||Lab 2|
|Sep 09||Drop/Add ends (Sep 10)|
|3||Sep 14||* Jurafsky and Martin, Chapter 5 sections 5.1-5.4, 5.6
* Klein, S. and Simmons, R., 1963. A computational approach to grammatical coding of English words (2up). Journal of the Association for Computational Machinery 10, pp. 334-347.
|Sep 16||* (READ SECTIONS 2 AND 4, SKIPPING 4.4) Brill, E., 1995. Transformation-Based Error-Driven Learning and Natural Language Processing: A Case Study in Part of Speech Tagging (2up). Computational Linguistics 21:4, pp. 1-37
|4||Sep 21||Sequence tagging using Hidden Markov Models
* J&M, Section 2.2 (FSA), Section 3.4 (FST), Section 5.5 (HMM POS Tagging), Chapter 6 (HMMs, up to and including 6.5), Minimum Edit Distance
* M&S, Chapter 9 (Markov Models) and Chapter 10 (Part of Speech tagging) if you want a second perspective. (errata for M&S)
|5||Sep 28||Midterm Project|
|Sep 30||Unsupervised Morphological Analysis
* Harris (1955, 1967); Hafer and Weiss (1974); DéJean (1998)
|7||Oct 19||Supervised Morphological Analysis
* Yarowsky, D. and Wicentowski, R., 2000. Minimally Supervised Morphological Analysis by Multimodal Alignment (2up)
* Schone, P. and Jurafsky, D., 2000. Knowledge-Free Induction of Morphology Using Latent Semantic Analysis (2up)
* Schone, P. and Jurafsky, D., 2001. Knowledge-Free Induction of Inflectional Morphologies (2up)
|Oct 21||Lab 5|
|Oct 28||Lexical Semantics/Word Sense Disambiguation
* J&M, Chapter 19.1-19.3, 20.1-20.6
|9||Nov 02||* McCarthy, D. et al, 2004. Finding Predominant Word Senses in Untagged Text
|Nov 04||Last day to declare CR/NC or withdraw with a W (Nov 05)||* O'Connor, B. et al, 2010. From tweets to polls: Linking text sentiment to public opinion time series
|10||Nov 09||* Riloff, E., Wiebe, J., and Wilson, T. 2003. Learning subjective nouns using extraction pattern bootstrapping.
* Riloff, E. and Wiebe, J. 2003. Learning extraction patterns for subjective expressions.
|Nov 11||* Pang, B. et al. 2002. Thumbs up? sentiment classification using machine learning techniques.
* J&M, Sections 6.6 and 6.7 (Maximimum Entropy Models)
|11||Nov 16||* Knight, K., 1997. Automating Knowledge Acquisition for Machine Translation. AI Magazine, Volume 18, No. 4, 1997.
(Sections 3 and 4 are optional)
|Nov 18||* Knight, K., 1999. A Statistical MT Tutorial Workbook. Prepared for the 1999 JHU Summer Workshop.||Lab 9|
* J&M, Chapter 12 (through 12.4)
* J&M, Section 13.4.1 (CKY Parsing)
|50%||Labs and midterm project|
|5%||Class pariticipation and Attendance|
You will submit your assignments electronically using the handin65 program. You may submit your assignment multiple times, but each submission overwrites the previous one and only the final submission will be graded. Normally, late assignments will not be accepted; however, special exceptions can be made if you contact me well in advance of the deadline. Even if you do not fully complete an assignment, you may submit what you have done to receive partial credit.
Some assignments may take a considerable amount of time, so you are strongly encouraged to begin working on assignments well before the due date.
Assignments will presuppose knowledge of Python. You will almost certainly end up learning some basic Perl and bash scripting, but you are not expected to know this yet.
Please make sure that each program you turn in has:
Academic honesty is required in all work you submit to be graded. With the exception of your partner on assignments, you may not submit work done with (or by) someone else, or examine or use work done by others to complete your own work.
You may discuss assignment specifications and requirements with others in the class to be sure you understand the problem. In addition, you are allowed to work with others to help learn the course material. However, with the exception of your lab partner, you may not work with others on your assignments in any capacity.
All code you submit must be your own with the following permissible exceptions: code distributed by me as part of the class, code found in the course text book, and code worked on with your assignment partner. You should always include detailed comments that indicates which parts of the assignment you received help on, and what your sources were.
Please see me if there are any questions about what is permissible.
Contact Tracey Rush at the Dean's office and follow these steps for obtaining accommodations.
Jurafsky and Martin, Speech and Language Processing (2/e), 2008
Manning and Schutze, Foundations of Statistical Natural Language Processing, 1999
Mertz, Text Processing in Python, 2003
NLTK: Natural Language Toolkit
The ACL Anthology
How To Think Like a Computer Scientist: Learning with Python