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Proceedings 2018, 2(18), 1160;

Increasing NLP Parsing Efficiency with Chunking

FASTPARSE Lab, Departamento de Computación, University of A Coruña, Campus de Elviña, 15071 A Coruña, Spain
Presented at the XoveTIC Congress, A Coruña, Spain, 27–28 September 2018.
Author to whom correspondence should be addressed.
Published: 19 September 2018
(This article belongs to the Proceedings of XoveTIC Conference 2018)
PDF [480 KB, uploaded 26 September 2018]


We introduce a “Chunk-and-Pass” parsing technique influenced by a psycholinguistic model, where linguistic information is processed not word-by-word but rather in larger chunks of words. We present preliminary results that show that it is feasible to compress linguistic data into chunks without significantly diminishing parsing performance and potentially increasing the speed.
Keywords: Parsing; Syntax; natural language processing; NLP; dependency parsing; Chunking Parsing; Syntax; natural language processing; NLP; dependency parsing; Chunking
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Anderson, M.D.; Vilares, D. Increasing NLP Parsing Efficiency with Chunking. Proceedings 2018, 2, 1160.

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