Ling7800/CSCI 7000: Computational Lexical Semantics

Spring 2017

Martha Palmer
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

Module 2: Available Computational Lexicons - Chap 2

Module 3: Machine Learning

Module 4: Sentence Representations

Module 5: Term Project Paper Presentations

Module 6: Representations of Events

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.
Mirza, Paramita. 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 Processing•ĄšIJCNLP 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 Martha Palmer. The Revised Arabic PropBank. Poster in the Proceedings of the Linguistic Annotation Workshop, held in conjunction with ACL-2010.. July 15-16, 2010, Uppsala, Sweden.
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, at CMU

    Background in Ontologies

    Description Logic, including CLASSIC and OWL