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Creators/Authors contains: "Kim, Gene Louis"

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  1. ; (, Semantics and Linguistic Theory)
    null (Ed.)
    We propose a computational model for inducing full-fledged combinatory categorial grammars from behavioral data. This model contrasts with prior computational models of selection in representing syntactic and semantic types as structured (rather than atomic) objects, enabling direct interpretation of the modeling results relative to standard formal frameworks. We investigate the grammar our model induces when fit to a lexicon-scale acceptability judgment dataset – Mega Acceptability – focusing in particular on the types our model assigns to clausal complements and the predicates that select them. 
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    Full Text Available 
  2. ; ; ; (, Fourteenth Int. Conf. on Computational Semantics (IWCS 2021))
    null (Ed.)
    “Episodic Logic: Unscoped Logical Form” (EL-ULF) is a semantic representation capturing predicate-argument structure as well as more challenging aspects of language within the Episodic Logic formalism. We present the first learned approach for parsing sentences into ULFs, using a growing set of annotated examples. The results provide a strong baseline for future improvement. Our method learns a sequence-to-sequence model for predicting the transition action sequence within a modified cache transition system. We evaluate the efficacy of type grammar-based constraints, a word-to-symbol lexicon, and transition system state features in this task. Our system is availableat https://github.com/genelkim/ ulf-transition-parser. We also present the first official annotated ULF dataset at https://www.cs.rochester.edu/u/ gkim21/ulf/resources/. 
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    Accepted Manuscript Full Text Available
  3. ; (, Proceedings from Semantics and Linguistic Theory)
    null (Ed.)
    We propose a computational modeling framework for inducing combinatory categorial grammars from arbitrary behavioral data. This framework provides the analyst fine-grained control over the assumptions that the induced grammar should conform to: (i) what the primitive types are; (ii) how complex types are constructed; (iii) what set of combinators can be used to combine types; and (iv) whether (and to what) the types of some lexical items should be fixed. In a proof-of-concept experiment, we deploy our framework for use in distributional analysis. We focus on the relationship between s(emantic)-selection and c(ategory)-selection, using as input a lexicon-scale acceptability judgment dataset focused on English verbs’ syntactic distribution (the MegaAcceptability dataset) and enforcing standard assumptions from the semantics literature on the induced grammar. 
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  4. ; ; (, NaLoMa 2020: Natural Logic meets Machine Learning, Workshop at NASSLLI 2020, Brandeis University, Waltham MA,)
    null (Ed.)
    We present a method of making natural logic inferences from Unscoped Logical Form of Episodic Logic. We establish a correspondence between inference rules of scope-resolved Episodic Logic and the natural logic treatment by Sánchez Valencia (1991a), and hence demonstrate the ability to handle foundational natural logic inferences from prior literature as well as more general nested monotonicity inferences. 
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    Accepted Manuscript Full Text Available