Students and faculty are currently working on diverse projects in computational phonetics, phonology, syntax, and semantics.
Neurolinguistics & Psycholinguistics
John Hale • Shohini Bhattasali • Jixing Li
Using various different models from computational linguistics, we study the cognitive neuroscience of language. Through naturalistic speech comprehension data from fMRI studies, we are investigating different linguistic questions such as comparing compositional meaning to frozen meaning, semantic coherence vs. incoherence, binding theory & pronoun resolution among other topics.
Jacob Collard • John Hale • Mats Rooth
This project explores the learnability of various syntactic formalisms such as Categorial Grammars and Dependency Grammars and attempts to provide a more naturalistic algorithm for learning syntax that does not rely on extensively annotated structures, but rather on inferences that can be made from basic knowledge. We are also investigating how learned grammars compare to engineered grammars and to claims made in theoretical syntax.
Mats Rooth • Simone Harmath-de Lemos • Shohini Bhattasali
In this project we train a finite state model to detect prosodic cues in a speech corpus. We are specifically interested in detecting stress cues in Brazilian Portuguese and Bengali and finding empirical evidence for current theoretical views.
- Shohini Bhattasali, Murielle Fabre, John Hale. (2018) Processing MWEs: Neurocognitive Bases of Verbal MWEs and Lexical Cohesiveness within MWEs. Proceedings of the 14th Workshop on Multiword Expressions (COLING 2018).
- Jixing Li, Murielle Fabre, Wen-Ming Luh, John Hale. (2018) Modeling Brain Activity Associated with Pronoun Resolution in English and Chinese. Proceedings of NAACL Workshop on Computational Models of Reference, Anaphora, and Coreference (CRAC 2018).
- Jacob Collard. (2018) A Naturalistic Inference Learning Algorithm. Linguistic Society of America (LSA 2018).
- Shohini Bhattasali, John Hale, Christophe Pallier, Jonathan R. Brennan, Wen-Ming Luh, R. Nathan Spreng. (2018) Differentiating Phrase Structure Parsing and Memory Retrieval in the Brain. Proceedings of the Society for Computation in Linguistics (SCiL 2018).
- Mats Rooth. (2017) Finite-state intensional semantics. 12th International Conference on Computational Semantics (IWCS 2017).
- Matthew Nelson, Imen El Karoui, Kristof Giber, Xiaofang Yang, Laurent Cohen, Hilda Koopman, Sydney S. Cash, Lionel Naccache, John Hale, Christophe Pallier, Stanislas Dehaune. (2017) Neurophysiological dynamics of phrase-structure building during sentence processing. Proceedings of the National Academy of Sciences.
- Matthew Nelson, Stanislas Dehaene, Christophe Pallier, and John Hale. (2017). Entropy Reduction correlates with Temporal Lobe Activity. Proceedings of the 7th Workshop on Cognitive Modelling and Computational Linguistics (CMCL 2017).
- Jacob Collard. (2016) Inferring Necessary Categories in CCG. 9th International Conference on the Logical Aspects of Computational Linguistics (LACL 2016).
- Jonathan Howell, Mats Rooth, and Michael Wagner. (2016). Acoustic classification of focus: on the web and in the lab. Doi 1813/42538
- Jonathan R. Brennan, Edward P. Stabler, Sarah E. Van Wagenen, Wen-Ming Luh, and John T. Hale. (2016) Abstract linguistic structure correlates with temporal activity during naturalistic comprehension. Brain and Language 157, 81-94.
- John Hale. (2016). Information-theoretical complexity metrics." Language and Linguistic Compass
- Jixing Li, Jonathan Brennan, Adam Mahar, and John Hale. (2016). Temporal lobes as combinatory engines for both form and meaning. Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity (CL4LC 2016).
- John T. Hale, David E. Lutz, Wenming Luh, and Jonathan R. Brennan. (2015). Modeling fMRI time courses with linguistic structure at various grain sizes. Proceedings of CMCL 2015.
- Shohini Bhattasali, Jeremy Cytryn, Elana Feldman, and Joonsuk Park. (2015). Automatic identification of rhetorical questions. Proceedings of the ACL 2015.
LING 4424: Computational Linguistics
Introduction to computational linguistics. Possible topics include syntactic parsing
using functional programming, logic-based computational semantics, and finite state
modeling of phonology and phonetics.
LING 4429/6429: Grammar Formalisms
This course introduces different ways of "formalizing" linguistic analyses, with
examples from natural language syntax. Students learn to identify recurrent themes in
generative grammar, seeing how alternative conceptualizations lead to different analytical
trade-offs. Using distinctions such as rule vs constraint, transformational vs. monostratal
and violable vs. inviolable, students emerge better able to assess others' work in a variety
of formalisms, and better able to deploy formalism in their own analyses.
LING 4485/6485: Topics in Computational Linguistics
Current topics in computational linguistics. Recent topics include computational models
for Optimality Theory and finite state models.
LING 2264: Language, Mind, and Brain
An introduction to neurolinguistics, this course surveys topics such as aphasia,
hemispheric lateralization and speech comprehension as they are studied via neuroimaging,
intracranial recording and other methods. A key focus is the relationship between these data,
linguistic theories, and more general conceptions of the mind.
English 97, BankBaseline, and PF Linear Expansion: Please contact Mats Rooth.
The Cornell Conditional Probability Calculator (CCPC): Please contact email@example.com.
DeepParse 2.2, DepPrint 1.1, NegraToConfig: Please contact Marisa Boston.
Computation lexicon of Modern Greek annotated with POS and lemma, Newspaper corpus of Modern Greek:Please contact Effie Georgala.
Natural Language Processing group
Cognitive Science program
Association of Computational Linguistics
Cornell Linguistics Circle