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1.2 COMPUTER AND INFORMATION SCIENCE
Natural language processing (NLP) can be done using either top-down (theory driven) and bottom-up (data driven) approaches, which we call mechanistic and phenomenological respectively. The approaches are frequently considered to stand in opposition to each other. Examining some recent approaches in deep learning we argue that deep neural networks incorporate both perspectives and, furthermore, that leveraging this aspect of deep learning may help in solving complex problems within language technology, such as modelling language and perception in the domain of spatial cognition.
Dobnik, S. & Kelleher J.D. (2017)Modular Mechanistic Networks: On Bridging Mechanistic and Phenomenological Models with Deep Neural Networks in Natural Language Processing,CLASP Papers in Computational Linguistics Vol. 1: Proceedings of the Conference on Logic and Machine Learning in Natural Language (LaML 2017), 1-11,pp. Gothenburg, 12–13 June 2017, edited by Simon Dobnik and Shalom Lappin. ISSN: 2002-9764. URI: http://hdl.handle.net/2077/54911