Kyle Wade GroveLinguistics, Cornell Universityemail: kwg33 "at" name-of-school "dot" edu |
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ActivitiesAt Cornell University, my work has examined psycholinguistic phenomena by building computational models of the types of difficulty humans encounter during structural language processing. I have been specifically interested in how statistical and grammatical knowledge are utilized in online sentence processing. I model this knowledge by building probabilistic multiple context free grammars (Nekanishi 1994) with rich lexical models. The dependent variables of interest constitute information theoretical metrics (Shannon, 1948) predicting what types of human sentence processing difficulty the user will encounter.   As of July 2012, I will be working as Director of Artificial Intelligence/Senior Computational Linguist with AskZiggy, Inc. in Sacramento, California. AskZiggy is a cross-platform voice-activated personal assistant and NLPaaS provider for mobile applications, and I will be working directly to enhance features, infrastructure, and recognition. Semisupervised Discriminative Machine LearningMy most recent work has been on how discriminative machine learning models for named entity recognition and domain identification can be built for novel domains and registers with a minimum of annotated corpora. Information Theoretic Metrics and Linking Theories of Cognitive DifficultyMy graduate work has attempted to compare/contrast the information theoretic metrics of surprisal (Hale 2001) and entropy both formally and empirically. I have been pursuing the hypothesis that surprisal and entropy map onto different types of psycholinguistic phenomena (respectively, surprising garden path continuations vs. center embedded structures and weak islands which stress the resource limitations of a limited parallelism sentence processor). Processing Verbal Structure in Garden Path Sentences
I have been examining the reduced relative clause (RRC) processing asymmetry, first reported by
Stevenson and Merlo (1997): |
AchievementsSurprisal Derives the Recent
Filler Heuristic in Mildly Context Sensitive Grammars.Poster,
10th Annual Conference on Tree Adjoining Grammars
and Related Formalisms, New Haven, CT. 6/11/10.
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States
MCFGCKY v. 1.0. doc:
web pdf
A chart parser written in OCaML, uses the Guillamin compiler to
translate MG to MCFG. Computes surprisals and entropies for arbitrary
probabilistic CFGs and MCFGs trained from treebank.
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Accomplishments
Why Unaccusatives Have It Easy: Processing Lexical Semantics
without Lexical Encoding. Penn Working Papers in
Linguistics. (PWPL) 17.1
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