Adina Williamsimage-001

You can view my CV here (updated 8/1/17).

I am PhD candidate in Linguistics at New York University.  I specialize in semantics, syntax, and neurolinguistics, using theoretical linguistic insights as the basis for hypotheses about the processing of language in the brain. I work in the NYU Neuroscience of Language Lab (NELLab) with Liina Pylkkänen on the neural bases of the semantic processing of argument structure. When not looking at pretty brains, I do theoretical work on the syntax-semantics interface at the overlap of the Semantics Group, the Syntax Research Group, and the Morphology Reading Group, where I work on the semantics of inflectional morphology (mainly viewpoint aspect and its connection to adpositional meaning in Mandarin Chinese, and number and definiteness in American English).  I also do applied Machine Learning work with ML² Group in the NYU Center for Data Science.

I recently released the Multi-genre Natural Language Inference (MultiNLI) corpus with Sam Bowman‘s group.  This corpus is a crowd-sourced collection of sentence pairs (433k) annotated with textual entailment information for use in NLP and Machine Learning applications. It is the basis of shared task associated with the RepEval 2017 Workshop at EMNLP in Copenhagen. The manuscript describing the data and giving some baselines is available here; a manuscript detailing the results of the shared task is available here.

 

Brief Summary of Interests

Broadly:

  • Syntax-Semantics Interface
  • Experimental Approaches to Linguistics

Narrowly:

  • Brain Basis of Syntactic and Semantic Processing
  • Argument Structure and Event Structure
  • Natural language understanding of textual entailment
    • How NLP methods can aid in the investigation of neurolinguistic questions
  • (Morpho)Syntax and Semantics of Mandarin Chinese
  • Semantics of Inflectional Morphology
    • Number Interpretation, Marking, and Countability
    • Prepositional and Verbal Aspect
    • Bare Singulars and Weak Definites
  • Computational methods (NLP, machine learning)