Increasing NLP Parsing Efficiency with Chunking

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 sign...

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Main Authors:	Mark Dáibhidh Anderson, David Vilares
Format:	Article
Language:	English
Published:	MDPI AG 2018-09-01
Series:	Proceedings
Subjects:	Parsing Syntax natural language processing NLP dependency parsing Chunking
Online Access:	http://www.mdpi.com/2504-3900/2/18/1160

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spelling	doaj-fc5dfdbba2f44091b46c6717fbf8320b2020-11-24T21:27:50ZengMDPI AGProceedings2504-39002018-09-01218116010.3390/proceedings2181160proceedings2181160Increasing NLP Parsing Efficiency with ChunkingMark Dáibhidh Anderson0David Vilares1FASTPARSE Lab, Departamento de Computación, University of A Coruña, Campus de Elviña, 15071 A Coruña, SpainFASTPARSE Lab, Departamento de Computación, University of A Coruña, Campus de Elviña, 15071 A Coruña, SpainWe 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.http://www.mdpi.com/2504-3900/2/18/1160ParsingSyntaxnatural language processingNLPdependency parsingChunking
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Mark Dáibhidh Anderson David Vilares
spellingShingle	Mark Dáibhidh Anderson David Vilares Increasing NLP Parsing Efficiency with Chunking Proceedings Parsing Syntax natural language processing NLP dependency parsing Chunking
author_facet	Mark Dáibhidh Anderson David Vilares
author_sort	Mark Dáibhidh Anderson
title	Increasing NLP Parsing Efficiency with Chunking
title_short	Increasing NLP Parsing Efficiency with Chunking
title_full	Increasing NLP Parsing Efficiency with Chunking
title_fullStr	Increasing NLP Parsing Efficiency with Chunking
title_full_unstemmed	Increasing NLP Parsing Efficiency with Chunking
title_sort	increasing nlp parsing efficiency with chunking
publisher	MDPI AG
series	Proceedings
issn	2504-3900
publishDate	2018-09-01
description	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.
topic	Parsing Syntax natural language processing NLP dependency parsing Chunking
url	http://www.mdpi.com/2504-3900/2/18/1160
work_keys_str_mv	AT markdaibhidhanderson increasingnlpparsingefficiencywithchunking AT davidvilares increasingnlpparsingefficiencywithchunking
_version_	1725973017525223424

Increasing NLP Parsing Efficiency with Chunking

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