Ling7800/CSCI 7000: Computational Lexical Semantics
Martha Palmer and occasionally James Pustejovsky
Time and Location: Tue/Thur, 11:00 - 12:15, Ketchum 301, and some Fridays, 10:30-12, Hellems 285
Assessment: Five homeworks, one Paper presentation, and a term project.
Office Hours: Martha Palmer, Hellems 295, Tuesday 5-6pm, Thursday 1-2pm
Semantic Role Labeling (eBook),
Martha Palmer, Daniel Gildea, Nianwen Xue,
Synthesis Lectures on Human
Language Technologies ,
ed., Graeme Hirst, Morgan & Claypool, 2010. ISBN: 9781598298321
available on line on campus through Chinook
Representation and Inference for Natural Language.
A First Course in Computational Semantics.
Patrick Blackburn and Johan Bos, 2005,
CSLI Publications. ISBN: 1-57586-496-7
available from the CU bookstore
Lexical semantics is becoming an increasingly
important part of Natural Language Processing (NLP), as the field is
beginning to address semantics at a large scale. This introductory
lecture course will cover key issues in computational lexical
semantics. We will start with an introduction to theoretical models of
lexical semantics and events, considering both their adequacy as
linguistic models and their place in NLP. We will focus particularly
on computational lexical resources such as PropBank, VerbNet and the
Generative Lexicon, and examine their strengths and limitations with
respect to NLP applications. We will introduce apporoaches to
developing automatic classifiers that are intended to make use of
these resources and to offer richer representations of sentences in
context. These techniques can be fully supervised (requiring
hand-labeled training data), semi-supervised, or unsupervised
(learning lexical information from unlabeled text).
Several lectures will be shared with a parallel CS course on Computational
Semantics being run by
Professor James Pustejovsky at Brandeis University, who will avail us of
his expertise in semantics, especially with respect to the Generative
Lexicon and Event Representations.
Suggested Schedule and Readings
Introduction and Module 1: the Lexical Semantics of Verbs - Chap 1
- Aug 26 Course Overview and Natural Language Processing, the Pundit case study
Pundit Overview and
Palmer, Martha, Carl Weir, Rebecca Passonneau, and Tim Finin.
"The Kernel Text Understanding System."
Artificial Intelligence 63: 17-68: Special Issue on Text Understanding.
- Aug 28, Sep 2, Thematic Roles in Linguistics,
Aug 28 slides,
Sep 2 slides
Assignment 1: Exercises 1, 2 and 3, p. 19, SRL book, Due Sep 11
Background reading for Assignment:
- Fillmore, C. J. 1968 "The Case for Case" in E. Bach and R.T. Harms, eds.
Universals in Linguistic Theory, 1-88. New York: Holt, Rinehart and Winston. Section 3.
- Jackendoff, R.S. 1976 Towards an Explanatory Semantic Representation,
Linguistic Inquiry, 7:1, pp. 89-150.
- Dowty D.R 1991 Thematic Proto-Roles and Argument Selection.
Language 67: 547-619 sections 1-7 Paper
- Levin, B. English Verb Classes: A Preliminary Classification Introduction,
MIT Press, pp. 1-23, 1990., Paper
Module 2: Available Computational Lexicons - Chap 2
- Sep 4 Word Senses, WordNet and the OntoNotes Groupings
Sep 4 slides
- Palmer, M., Dang, H. and Fellbaum, C, 2007, Making Fine-grained and Coarse-grained sense distinctions,both manually and automatically,
Journal of Natural Language Engineering,13:2, 137-163.
- George A. Miller, Richard Beckwith, Christiane Fellbaum, Derek Gross, and Katherine Miller, 1993,
Introduction to WordNet: An On-line Lexical Database, 5 Papers on WordNet availalbe from the WordNet web site.
- Sep 9, 12 TUESDAY, FRIDAY: The Generative Lexicon James Pustejovsky
Sep 9, 12 slides
- Pustejovsky, James, 1991, The Generative Lexicon, ComputationaI Linguistics, Volume 17, Number 4, December. Paper
Background Reading for Sense Distinctions:
Edmonds, P. and Hirst, G., Near-Synonymy and Lexical Choice,
Computational Linguistics June, 2002, Vol. 28, No. 2, Pages 105-144,
- Atkins, S., Fillmore, C. J., Johnson, C. R.,
Lexicographic Relevance: Selecting Information from Corpus Evidence,
International Journal of Lexicography, Vol. 16 No. 3, Oxford University Press, 2003,
- Hanks, P. and Pustejovsky, J., A Pattern Dictionary for Natual Language Processing, Revue francaise de linguistique appliquie
2005/2 (Vol. X), CAIRN, INFO, 2005. Paper
- Sep 16 VerbNet and PropBank
Assignment 2: Exercises 1, 2, p. 29, SRL book, Due Sep 25
- Kipper, Karin, Anna Korhonen, Neville Ryant, Martha Palmer. "A Large-scale Classification of English Verbs." Language Resources and Evaluation Journal,42(1). Springer Netherland: 2008. pp. 21-40.
- Martha Palmer, Dan Gildea, Paul Kingsbury, 2005,
The Proposition Bank: An Annotated Corpus of Semantic Roles,
Computational Linguistics, 31:1 , pp. 71-105.
- Sep 18 Natural Language Processing: What we can and can't do
- Sep 23 FrameNet
- Fillmore et al 2001
"Building a large lexical databank which provides deep
Proceedings of the 15th Pacific Asia Conference on Language, Information and Computation. Eds. Benjamin Tsou, and Olivia Kwong. Hong Kong 2001.
- Fillmore, Charles J., Christopher R. Johnson, and Miriam R.L. Petruck. 2002.
Background to FrameNet.
International Journal of Lexicography, 16(3):2435
- Access to Computational Lexicons:
- Sep 25 Automatic Word Sense Disambiguation,Term Project Discussion, review Assignments 1 and 2
Module 3: Semantic representations
- Sep 30, Oct 2 Predicate Calculus
- Assignment 3, Due Oct 9
B&B Chap 1, and
Artificial Intelligence: A Modern Approach, Stuart J. Russell and Peter Norvig, Pearson Education, 2003, ISrBN:0137903952, Chap 15
- Oct 7 Mapping from the Generative Lexicon to VerbNet Joint with Brandeis
Assignment 4 Mapping GL-VN Due Oct 21
- Oct 9 Combinatory Categorial Grammar and Lambda Calculus
B&B, Chap 2
- Oct 14 CCG/Boxer examples Jared Desjardins
- Oct 16 Caused-Motion Constructions in VerbNet Jena Hwang
- Oct 21 Deeper Representations
Rappaport M. and B.Levin 1998 "Building Verb Meanings" in Butt,
Geuder, eds. The Projection of Arguments: Lexical and Compositional
Factors, CSLI Publications Paper
- Oct 23 Abstract Meaning Representations (AMRs) Tim O'Gorman
Assignment 5 Comparing Boxer to AMR, Due Nov 6
Module 4: Representations of Events
- Oct 28 Event Semantics I
Davidson D. 1967. "The Logical Form of Action Sentences,"
Reprinted in Davidson, D: Essays on Actions and Events, Oxford University Press
Events, Stanford Encyclopedia of Philosophy
- Oct 30 Event Semantics II & Temporal Relations
Parsons T. 1990 Events in Semantics of English . MIT Press, Boston
Casati, R., and Varzi, A., editors. Events . Dartmouth, Aldershot, 1996.
Possbile Additional Papers:
- The NAACL and ACL Events Workshops
- James Pustejovsky; Marc Verhagen, 2009, SemEval-2010
Task 13: Evaluating Events, Time Expressions, and Temporal Relations
(TempEval-2) In the Proceedings of the Workshop on Semantic
Evaluations: Recent Achievements and Future Directions (SEW-2009)
held with NAACL-2009, Boulder, CO.
- Nov 4 Richer Event Descriptions Tim O'Gorman
- Nov 6 Student Presentations: Events (2014 ACL, LREC papers)
- Nov 11 Event Extraction Jinying Chen
- Nov 14 FRIDAY: Events, going forward James Pustejovsky, joint with Brandeis
- Nov 19, 20 Ontologies
- Nov 24, 26 Fall Break
- Dec 2, 4, 9, 11 Student Presentations
Final Exam - Student Project Presentations, Tuesday, Dec 16, 4:30-7pm MUEN D 430, ICS Large conference room
Background in Ontologies
Description Logic, including
Machine Learning Background
Tom M. Mitchell, 2006, Machine Learning Department technical report CMU-ML-06-108, Carnegie Mellon University,
The Discipline of Machine Learning
Machine Learning Resources/Links
Dan Klein's Machine
Learning for Natural Language Processing: New Developments and Challenges
(slides and video)
Michael Collins tutorial on NLP
Introduction to Machine Learning, S V N Vishwanathan
Weka, a collection of machine learning algorithms for data mining tasks.
Orange, Open source data visualization and analysis for novice and experts. Data mining through visual programming or Python scripting.
Videos of Andrew Ng's Stanford ML course
Noah Smith's course titled Language and Statistics,