Course Information(This is from the 2013 NLP course page; this part will be updated soon.)
ESO 211 Data Structures. Some familiarity with probability would help.
NLP attempts to interact with humans and human texts via language. Problems in the domain include analyzing texts to discover structures and to make decisions. Translating from one language to another. Interacting with humans in dialogue systems or cooperative tasks. Particular emphasis on Indian languages. Issues for languages with relatively poor tagged resources are how to boost unsupervised and semi-supervised methods for the purposes of analysis. Also, how to discover structures via parallel corpora.
- Jurafsky, Daniel; James H. Martin; Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition Pearson Education India, 2000.
- Manning, Christopher D.; Hinrich Schuetze; Foundations of Statistical Natural Language Processing Cambridge, MIT Press, 1999.
- Kiraz, George Anton; Computational Nonlinear Morphology: With Emphasis on Semitic Languages Cambridge University Press, 2001, 171 pages
Additional Readings: Oxford Handbook of Computational Linguistics.
Also: several video lectures will be prescribed.
An important part of this course will be a course project. Each of you is also expected to select a project in which you will investigate some topic of current research interest, and you are expected to be able to communicate the key ideas of your project to others in the course. Owing to the high project weightage, project groups will be formed by lottery within the first two weeks of class. Subsequently, project topics will be defined based on discussions with each group.
- Two written exams (two hours each): 2 x 20%
- Course discussions, Homework and Labs: 15-20%
- Final Project: 40-50%
(Approx: Proposal: 5%, Presentation: 10%, Report: 15%, Demo/Oral: 15%)
Lecs 1/2 Introduction and Overview : Language Structures and Levels - Sounds / Words / Sentences / Discourse Objectives of NLP Morphological processing Syntactic analysis - parsing. Regular Expressions, demonstrations of use on corpus. Manning / Shuetze: Ch 1 - Rule-based (rationalist) vs probabilistic (empiricist) history Lec 3/4 Morphological processing Rule-based: Porter stemmer Machine-Learning - Unsupervised approaches - HW1 : Unsupervised Morphological Processing / Parallel Corpora [Selection of Papers for Review] NOTE: All Homeworks will be for a non-English language. Lec 5-6 Part of Speech Tagging Supervised Learning / SVM Hidden Markov Models Unsupervised POS tagging [Sub-groups to review Papers on subtopics] Lec 7 Probability and Information Theory ; Naive Bayes models HW2: Spell checker Lecs 8-9 Grammars - CFG grammars - rule-based parsing difficulties. Alternative: Probabilistic grammars Discovering grammars from patterns in text HW3: Unsupervised Syntax discovery [*Special Session: Project Proposal Presentations] [* = extra session] Lec 10-11 Semantic modeling Classical ontology-driven approaches Latent Semantic Analysis Linking Language to Vision / Robotics Lec 12 Word discovery from real situations Aligning unsupervised syntax with sensory structures Lec 13 Machine Translation Acquiring structures from Parallel Corpora Lec 14 Spatial Language and Semantics Lecs 15-19: Interim Project Presentations. Lec END NLP and the rest of AI. Turing test. [*Final Poster Presentations]