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Back to the Future: Logic and Machine Learning

Conference contribution
Authors Simon Dobnik
John D. Kelleher
Published in Conference on Logic and Machine Learning in Natural Language (LaML)
Publication year 2017
Published at Department of Philosophy, Linguistics and Theory of Science
Language en
Links clasp.gu.se/news-events/conference-...
Keywords language technology, computational linguistic, logic, formal approaches, structure learning, data driven approaches, machine learning, deep learning, spatial language, dialogue
Subject categories Computational linguistics, Linguistics, Cognitive science


In this paper we argue that since the beginning of the natural language processing or computational linguistics there has been a strong connection between logic and machine learning. First of all, there is something logical about language or linguistic about logic. Secondly, we argue that rather than distinguishing between logic and machine learning, a more useful distinction is between top-down approaches and data-driven approaches. Examining some recent approaches in deep learning we argue that they incorporate both properties and this is the reason for their very successful adoption to solve several problems within language technology.

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