LING83800: Methods in Computational Linguistics II

Spring 2024

Graduate Center, CUNY

Instructors: Prof. Spencer Caplan and Prof. Kyle Gorman

Practicum leader: Natalia Tyulina

Lecture: Monday 4:15-6:15 (rm. 7314)

Practicum: Friday 11:45-1:45 (rm. 7400.13)

Office hours: (Caplan) Wednesday 12:30-2:30 (rm 7400.02), (Gorman) Wednesday 2-3 (rm 7400.05) and by appointment

Synopsis

This course is the second of a two-semester series introducing computational linguistics and software development. The intended audience are students interested in speech and language processing technologies, though the materials will be beneficial to all language researchers.

Objectives

Using the Python programming language, students will learn formalisms and technologies used to build speech and language technologies.

Materials

There is no textbook, but readings will be assigned. Students are strongly encouraged to bring a laptop computer to the practicum.

Assignments

Assignments will take the form of either pencil-and-paper assignments or small software development projects. Software assignments will always been turned in with a write-up describing the general approach taken and any challenges encountered. Students will often be able to verify the technical correctness of their code by running provided tests. We will use GitHub Classroom for assignment turn-in.

The final assignment will be an open-ended project which will either extend earlier projects, or build and evaluate a speech and language technology system. Students are encouraged to conceive of projects relevant to their research interests. Students should discuss project plans with the instructor during office hours to confirm that it is both feasible and of appropriate scope.

Grading

80% of students' grades will be derived from the assignments; the remaining 20% will be reserved for participation and attendance. Assignments should be submitted on time or may receive a 0 grade (barring a documented emergency). No separate grade will be assigned for the practicum.

Accommodations

The instructor will attempt to provide all reasonable accommodations to students upon request. If you believe you are covered under the Americans With Disabilities Act, please direct accommodations requests to Matthew G. Schoengood, Vice President for Student Affairs.

Attendance

Students are expected to attend all lectures and practica in person. Other absences will not be excused, and the instructor reserves the right to tie grades to attendance records.

Integrity

In line with the Student Handbook policies on plagiarism, students are expected to complete their own work. The instructor reserves the right to refer violations to the Academic Integrity Officer.

Respect

For the sake of privacy, students are not permitted to record lectures. Students are expected to be considerate of your peers and to treat them with respect during discussions.

Schedule

(Please note that this is subject to change and will be updated as we go.)

F	1/26		No class
M	1/29		Kyle	Syllabus tooling	Notes Slides
F	2/2		Natasha	Practicum	Handout 1 2
M	2/5		Kyle	Git GitHub	Handout	Chacon & Straub ch. 1.1-3.2, 6.1-6.3
F	2/9		Natasha	Practicum	Handout
M	2/12		No class
F	2/16		No class
M	2/19		No class
Th	2/22	HW1 due	Kyle	Formal languages I	Handout	Partee et al. ch. 1	(Hopcroft et al. ch. 1.5)
F	2/23		Natasha	Practicum	Handout
M	2/26		No class
W	2/28	HW2 due [solution]	Kyle	Formal languages II	Handout	Jurafsky & Martin ch. 17-17.5	(Jäger & Rogers; Graf)
F	3/1		Natasha	Practicum	Notebook
M	3/4		Kyle	Automata	Slides	Gorman & Sproat ch. 1-1.4 Jurafsky & Martin ch. 2-2.1	(Freeman et al. ch 10; Hopcroft et al. ch. 3-3.1, 3.3)
F	3/8		Natasha	Practicum	Notebook
M	3/11	HW3 due [solution]	Kyle	Transducers Rewrite rules	Slides Notebook	Gorman & Sproat ch. 5	(Hopcroft et al. ch. 2, 3.2)
F	3/15		Natasha	Practicum	Notebook
M	3/18	HW4 due [solution]	Spencer	Probability theory	Handout	Manning & Schütze ch. 2
F	3/22		Natasha	Practicum	Handout Notebook
M	3/25	HW5 due [solution]	Spencer	Language models I	Handout	Gorman & Sproat ch. 1.5-1.6 Roark & Sproat ch. 6.1	(Manning & Schütze ch. 6)
F	3/29			No classes
M	4/1		Spencer	Language models II	Slides 1 2 Handout
F	4/5		Natasha		Notebook Notes
M	4/8		Spencer	Tagging chunking	Slides	Bird et al. ch. 5 Jurafsky & Martin appendix A	(Manning & Schütze ch. 9)
W	4/10	HW6 due
F	4/12		Natasha		Slides Notebook
M	4/15		Spencer	Generative classifiers	Slides Handout	Bird et al. ch. 6.1-3, 6.5-6.9 Jurafsky & Martin ch. 4
F	4/19		Natasha	Practicum	Notebook Handout
M	4/22		No classes
F	4/26		No classes
M	4/29		No classes
F	5/3		No classes
M	5/6	HW7 due	Spencer	Discriminative classifiers		Pedregosa et al. Breiman Ng & Jordan	(Collins)
F	5/10		Natasha	Practicum
M	5/13		Spencer	Text classification Regularization & tuning		Scikit-learn tutorials 1, 2, 3
F	5/17		Natasha	Practicum
M	5/20	HW8 due	Kyle	Evaluation Ethical thinking	Slides 1 2 Handout	Resnik & Lin Hovy & Spruitt	(Gorman & Bedrick) (Strubell et al.)
W	5/22		End of semester
T	5/28	Term paper due

References

Bird, S., Klein, E. and Loper, E. n.d. Natural Language Processing with Python. URL: https://www.nltk.org/book/.
Breiman, L. 2001. Statistical modeling: the two cultures. Statistical Science 16(3): 199-231.
Church, K. W. n.d. Unix™ for poets. Ms., AT&T Research.
Chacon, S., and Straub, B. 2014. Pro Git. 2nd edition. Apress. URL: https://git-scm.com/book/en/v2.
Collins, M. 2002. Discriminative training methods for hidden Markov models: theory and experiments with perceptron algorithms. In Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing, pages 1-8.
Eisenstein, J. 2019. Introduction to Natural Language Processing. MIT Press.
Freeman, Eric, Freeman, Elisabeth, Sierra, K. and Bates, B. 2004. Head First Design Patterns: A Brain-friendly Guide. O'Reilly & Associates.
Freund, Y., and Schapire, R. E. 1999. Large margin classification using the perceptron algorithm. Machine Learning 37(3): 277-296.
Gorman, K. and Bedrick, S. 2019. We need to talk about standard splits. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2786-2791.
Gorman, K. and Sproat, R. 2021. Finite-State Text Processing. Morgan & Claypool.
Graf, T. 2022. Subregular linguistics: bridging theoretical linguistics and formal grammar. Theoretical Linguistics 48(3-4): 245-278.
Hopcroft, J. E., Motwani, R. and Ullman, J. D. 2008. Introduction to Automata Theory, Languages, and Computation. Pearson.
Hovy, D. and Spruit, S. L. 2016. The social impact of natural language processing. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 591-598.
Jäger, G. and Rogers, J. 2012. Formal language theory: refining the Chomsky hierarchy. Philosophical Transactions of the Royal Society B 367: 1956-1970.
Jelinek, F. 1997. Statistical Methods for Speech Recognition. MIT Press.
Jurafsky, D., and Martin, J. H. To appear. Speech and Language Processing. 3rd edition. Pearson. URL: https://web.stanford.edu/~jurafsky/slp3/.
Manning, C. D., and Schütze, H. 1999. Foundations of Statistical Natural Language Processing. MIT Press.
Ng, A. Y. and Jordan, M. I., 2002. On discriminative vs. generative classifiers: a comparison of logistic regression and naive Bayes. In NeurIPS, pages 841-848.
Partee, B. H., ter Meulen, A., and Wall, R. E. 1993. Mathematical Methods in Linguistics. 2nd edition. Kluwer Academic Publishers.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, É. 2011. Scikit-learn: machine learning in Python. Journal of Machine Learning Research 12: 2825-2830.
Resnik, P. and Lin, J. 2010. Evaluation of NLP systems. In Clark, A., Fox, C., and Lappin, S. (ed)., The Handbook of Computational Linguistics and Natural Language Processing, pages 271-295. Wiley-Blackwell.
Roark, B. and Sproat, R. 2007. Computational Approaches to Morphology and Syntax. Oxford University Press.
Strubell, E., Ganesh, A., and McCallum, A. 2019. Energy and policy considerations for deep learning in NLP. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3645-3650.