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Extended Abstract

Increasing NLP Parsing Efficiency with Chunking †

by
Mark Dáibhidh Anderson
* and
David Vilares
FASTPARSE Lab, Departamento de Computación, University of A Coruña, Campus de Elviña, 15071 A Coruña, Spain
*
Author to whom correspondence should be addressed.
†
Presented at the XoveTIC Congress, A Coruña, Spain, 27–28 September 2018.
Proceedings 2018, 2(18), 1160; https://doi.org/10.3390/proceedings2181160
Published: 19 September 2018
(This article belongs to the Proceedings of XoveTIC Congress 2018)
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Abstract

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

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MDPI and ACS Style

Anderson, M.D.; Vilares, D. Increasing NLP Parsing Efficiency with Chunking. Proceedings 2018, 2, 1160. https://doi.org/10.3390/proceedings2181160

AMA Style

Anderson MD, Vilares D. Increasing NLP Parsing Efficiency with Chunking. Proceedings. 2018; 2(18):1160. https://doi.org/10.3390/proceedings2181160

Chicago/Turabian Style

Anderson, Mark Dáibhidh, and David Vilares. 2018. "Increasing NLP Parsing Efficiency with Chunking" Proceedings 2, no. 18: 1160. https://doi.org/10.3390/proceedings2181160

APA Style

Anderson, M. D., & Vilares, D. (2018). Increasing NLP Parsing Efficiency with Chunking. Proceedings, 2(18), 1160. https://doi.org/10.3390/proceedings2181160

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