Abstract

Categorization is a central capability of human cognition, and a number of theories have been developed to account for properties of categorization. Even though many tasks in semantics also involve categorization of some kind, theories of categorization do not play a major role in contemporary research in computational linguistics. This paper follows the idea that embedding-based models of semantics lend themselves well to being formulated in terms of classical categorization theories. The benefit is a space of model families that enables (a) the formulation of hypotheses about the impact of major design decisions, and (b) a transparent assessment of these decisions. We instantiate this idea on the task of frame-semantic frame identification. We define four models that cross two design variables: (a) the choice of prototype vs. exemplar categorization, corresponding to different degrees of generalization applied to the input; and (b) the presence vs. absence of a fine-tuning step, corresponding to generic vs. task-adaptive categorization. We find that for frame identification, generalization and task-adaptive categorization both yield substantial benefits. Our prototype-based, fine-tuned model, which combines the best choices for these variables, establishes a new state of the art in frame identification.

Highlights

  • Categorization is the process of forming categories and assigning objects to them, and is a central capability of human cognition (Murphy, 2002)

  • We focus on the first step of frame-semantic parsing called frame identification or frame assignment, where an occurrence of a predicate in context is labeled with its FrameNet frame

  • Regarding the impact of the exemplar and prototype dimensions that we introduced in Section 3, we find that the exemplar model does worse overall than the prototype model in both configurations

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Summary

Introduction

Categorization is the process of forming categories and assigning objects to them, and is a central capability of human cognition (Murphy, 2002). Frame Semantics is a theory of semantics that groups predicates in terms of the situations that they describe and their relevant participants (Fillmore, 1982) These situations, or scenarios, are formalized in terms of frames, conceptual categories which have a set of lexical units that evoke the situation, and a set of frame elements that categorize the participants and that are expected to be realized linguistically. FrameNet provides sentence annotations that mark, for each lexical unit, the frame that is evoked as well as its frame elements in running text This annotated corpus has sparked a lot of interest in computational linguistics, and the prediction of frame-semantic structures (frames and frame elements) has become known as (frame-)semantic parsing (Gildea and Jurafsky, 2002; Das et al, 2014)

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