Ling7800/CSCI 7000: Computational Lexical Semantics
Time and Location: Tue/Thur, 2:00 - 3:15, ECCR 150
Assessment: Four homeworks, one Paper presentation, and a term project.
Office Hours: Martha Palmer, Wednesday 4-5, Friday 4:30-5:30, Fleming 289
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
selected chapters, available from the CU bookstore and D2L
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).
Suggested Schedule and Readings
Introduction and Module 1: the Lexical Semantics of Verbs - Chap 1
- Jan 17 Course Overview and Natural Language Processing, the Pundit case study
Palmer, Martha, Carl Weir, Rebecca Passonneau, and Tim Finin.
"The Kernel Text Understanding System."
Artificial Intelligence 63: 17-68: Special Issue on Text Understanding.
- Jan 19 Thematic Roles in Linguistics,
Assignment 1: Exercises 1, 2 and 3, p. 19, SRL book, Due Jan 31
- 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
- Jan 24, 26, 31 Word Senses, WordNet and the OntoNotes Groupings, Review Ass 1 (Jan 31)
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.
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
- Feb 2 The Generative Lexicon
Pustejovsky, James, 1991, The Generative Lexicon, ComputationaI Linguistics, Volume 17, Number 4, December. Paper
- Feb 7, 9 VerbNet and PropBank,
Assignment 2: Exercises 2,3,4 p. 29, SRL book, Due Feb 21
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.
- Feb 14 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:
Module 3: Machine Learning
Module 4: Sentence Representations
- Mar 7, 9 Predicate Logic, Assignment 3: Predicate Logic due March 21
B&B Chap 1 and 2 and
Artificial Intelligence: A Modern Approach, Stuart J. Russell and Peter Norvig, Pearson Education, 2003,
ISrBN:0137903952, Chap 14 and 15
Symbolic Logic: A First Course, Gary Hardegree, UMASS,
- March 14 Combinatory Categorial Grammar and Lambda Calculus, CCG examples Ghazaleh Kazeminejad
- March 16 Combinatory Categorial Grammar and Statistical Parsing Skatje Meyers
- March 21, 23 Review Assignment 3, Abstract Meaning Representations (AMRs) and After AMRs
Assignment 4: Comparing Predicate Logic to AMR, due April 6
- March 28, 30 Spring Break
Module 5: Term Project Paper Presentations
- April 4, 6, 11, 13, 18 Student Presentations
Module 6: Representations of Events
- April 20 Event Semantics and Event Structure
Parsons T. 1990 Events in Semantics of English . MIT Press, Boston
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
Casati, R., and Varzi, A., editors. Events . Dartmouth, Aldershot, 1996.
James Pustejovsky, The Syntax of Event Structure, Cognition,
Volume 41, Issues 1-3, December 1991, Pages 47-81
- April 25 VerbNet and Generative Lexicon Event Structure
Rappaport M. and B.Levin 1998 "Building Verb Meanings" in Butt,
Geuder, eds. The Projection of Arguments: Lexical and Compositional
Factors, CSLI Publications Paper
- April 27 Spatial Relations
- May 2 Ontologies and Event Ontologies
- May 4 CLASS CANCELLED
Possbile Additional Papers on Events:
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.
Rei Ikuta and Martha Palmer, (2014)
Challenges of Adding Causation to Richer Event Descriptions,
Proceedings of the 2nd Events Workshop, held in conjunction with ACL 2014, Baltimore, MD.
Hogenboom, F., Frasincar, F., Kaymak, U., & de Jong, F. (2011).
An overview of event extraction from text.
In Workshop on Detection, Representation, and Exploitation of Events in the Semantic Web (DeRiVE 2011)
at ISWC 2011 (Vol. 779, pp. 48-57)
McClosky, D., Surdeanu, M., & Manning, C. D. (2011).
Event extraction as dependency parsing.
In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics:
Human Language Technologies-Volume 1 (pp. 1626-1635).
Okamoto, M., & Kikuchi, M. (2009). Discovering Volatile Events in Your Neighborhood: Local-Area Topic Extraction from Blog Entries. In Information Retrieval Technology (pp. 181-192).
Hung, S. H., Lin, C. H., & Hong, J. S. (2010). Web mining for event-based commonsense knowledge using lexico-syntactic pattern matching and semantic role labeling. Expert Systems with Applications, 37(1), 341-347.
Advanced topics: possible term projects and/or post-class readings for interest:
Li, Qi, Heng Ji, and Liang Huang.
Joint Event Extraction via Structured Prediction with Global Features. In ACL (1), pp. 73-82. 2013.
Extracting Temporal and Causal Relations between Events. In ACL 2014
Marc Brysbaert, Amy Beth Warriner, and Victor Kuperman. 2013.
Concreteness ratings for 40 thousand
generally known English word lemmas. Behavior research methods, pages 1-8.
Felix Hill and Anna Korhonen. 2014.
Concreteness and subjectivity as dimensions of lexical meaning.
In the Proceedings of ACL 2014
David R. Dowty, 1986,
The effects of aspectual class on the temporal structure of discourse: semantics or pragmatics? Linguistics and Philosophy,
February 1986, Volume 9, Issue 1, pp 37-61
Ye, Y., & Zhang, Z. (2005).
Tense tagging for verbs in cross-lingual context: A case study. In Natural Language ProcessingĄšIJCNLP 2005 (pp. 885-895). Springer Berlin Heidelberg.
van der Plas, Lonneke, Marianna Apidianaki, and Chenhua Chen.
"Global methods for cross-lingual semantic role and predicate labelling." Proceedings of COLING. 2014.
Lee Becker, Wayne Ward, Sarel van Vuuren, and Martha Palmer.
DISCUSS: A dialogue move taxonomy layered over semantic representations. In IWCS 2011: The 9th International Conference on Computational Semantics, Oxford, England, January 2011.
Yoav Artzi, Dipanjan Das and Slav Petrov, 2014,
Learning Compact Lexicons for CCG Semantic Parsing.
In the Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Qatar.
Zaghouani, Wajdi, Mona Diab, Aous Mansouri, Sameer Pradhan and
The Revised Arabic PropBank. Poster in the Proceedings
of the Linguistic Annotation Workshop, held in conjunction
with ACL-2010.. July 15-16, 2010, Uppsala,
Shumin Wu, Jinho D. Choi, Martha Palmer,
Detecting Cross-lingual Semantic Similarity Using Parallel PropBanks,
Proceedings of the 9th Conference of the Association for Machine Translation in the Americas (AMTA'10), Denver, CO, 2010. (poster)
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,
Background in Ontologies
Description Logic, including