Next Article in Journal
Channel Covariance Identification in FDD Massive MIMO Systems
Previous Article in Journal
A Vehicle Routing Problem with Periodic Replanning
Article Menu
Issue 18 (XoveTIC Conference 2018) cover image

Article Versions

Export Article

Open AccessExtended Abstract
Proceedings 2018, 2(18), 1160; https://doi.org/10.3390/proceedings2181160

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
PDF [480 KB, uploaded 26 September 2018]

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
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).
SciFeed

Share & Cite This Article

MDPI and ACS Style

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

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Proceedings EISSN 2504-3900 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top