2018
ACL
ACL 2018
Constraining MGbank: Agreement, L-Selection and Supertagging in Minimalist Grammars
Abstract
AbstractThis paper reports on two strategies that have been implemented for improving the efficiency and precision of wide-coverage Minimalist Grammar (MG) parsing. The first extends the formalism presented in Torr and Stabler (2016) with a mechanism for enforcing fine-grained selectional restrictions and agreements. The second is a method for factoring computationally costly null heads out from bottom-up MG parsing; this has the additional benefit of rendering the formalism fully compatible for the first time with highly efficient Markovian supertaggers. These techniques aided in the task of generating MGbank, the first wide-coverage corpus of Minimalist Grammar derivation trees.
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Interdisciplinary Bridge
— Artificial Intelligence and Computer Science and Interdisciplinary and Machine Learning and Mathematics & Optimization
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Trend Setter
— Formal Languages
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Keyword Pioneer
— parsing efficiency
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio