Deep learning (DL) is an emerging concept in the field of artificial intelligence, expanding its scope from machine learning to other areas of computer science. Mainly, DL proliferates its development to natural language processing (NLP), specifically computational linguistics (CL). This paper discusses both DL and CL.

The author presents the addressing notes of a workshop and his contributions to CL through DL. This talk comprises five topics: the DL tsunami, the success of DL, why CL need not worry, DL of language, and scientific questions that connect CL and DL. The author includes 15 examples expressing his understanding about DL using three mixed noun-verb statuses, four nondiscrete statuses, two hedging adverbial modifiers, two ambiguous forms, three unambiguous hedging modifiers, and one workshop discussion note. Further, he stresses over-using distributed representations and DL in lieu of neural networks within the scope of NLP.

In order to elaborate on the current understanding of DL, the author provides links to seven websites in the footnotes, apart from 15 references in the text. This paper is an interesting read for beginners and professionals working in the area of DL, focusing on NLP and CL. The author’s concepts on DL enticing CL make reading this paper worthwhile.