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Abstract: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.
* CLASP Papers in Computational Linguistics Vol. 1: Proceedings of
the Conference on Logic and Machine Learning in Natural Language (LaML 2017).
ISSN: 2002-9764. URI: http://hdl.handle.net/2077/54911* 11 pages, 1 figure, Appears in CLASP Papers in Computational
Linguistics Vol. 1: Proceedings of the Conference on Logic and Machine
Learning in Natural Language (LaML 2017), pp. 1-11