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Towards an annotation framework for incremental scope specification update

Conference paper
Authors Asad Sayeed
Published in CLASP Papers in Computational Linguistics, Proceedings of the Conference on Logic and Machine Learning in Natural Language (LaML 2017), Gothenburg, 12–13 June 2017 / Simon Dobnik and Shalom Lappin (eds.)
ISSN 2002-9764
Publisher University of Gothenburg
Place of publication Gothenburg
Publication year 2017
Published at Department of Philosophy, Linguistics and Theory of Science
Language en
Links hdl.handle.net/2077/54911
Keywords language, logic, machine learning, computational linguistics, language technology, artificial intelligence, semantics, corpora, corpus annotation, quantifier scope
Subject categories Computational linguistics, Linguistics, Cognitive science

Abstract

This position paper outlines a framework for accommodating formal models of incremental scope processing in probabilistic terms through corpus annotation. Theories of scope ambiguity resolution encompass a number of overlapping and potentially conflicting strategies, including underspecification, quantifier raising, and employment of world-knowledge. Considering the conflicting evidence for the specific roles that these strategies play and the relationships between them, it may be possible to mediate their relationships through a probabilistic model that includes weights for each factor at each time step. This, however, requires the development of corpus resources. Here we propose the representation of scope specification operations as “decision tags” in future scope corpus annotation efforts.

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