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Transition-Based Dependency Parsing with Neural Networks
Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems.
2017 (English)Independent thesis Basic level (degree of Bachelor), 10,5 credits / 16 HE creditsStudent thesis
Abstract [en]

Dependency parsing is important in contemporary speech and language processing systems. Current dependency parsers typically use the multi-class perceptron machine learning component, which classifies based on millions of sparse indicator features, making developing and maintaining these systems expensive and error-prone. This thesis aims to explore whether replacing the multi-class perceptron component with an artificial neural network component can alleviate this problem without hurting performance, in terms of accuracy and efficiency. A simple transition-based dependency parser using the artificial neural network (ANN) as the classifier is written in Python3 and the same program with the classifier replaced by a multi-class perceptron component is used as a baseline. The results show that the ANN dependency parser provides slightly better unlabeled attachment score with only the most basic atomic features, eliminating the need for complex feature engineering. However, it is about three times slower and the training time required for the ANN is significantly longer.

Place, publisher, year, edition, pages
2017. , p. 10
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:liu:diva-138596ISRN: LIU-IDA/LITH-EX-G--17/011--SEOAI: oai:DiVA.org:liu-138596DiVA, id: diva2:1111936
Subject / course
Computer Programming
Presentation
2017-06-08, A37, Linköpings Universitet, Linköping, 17:09 (Swedish)
Supervisors
Examiners
Available from: 2017-07-03 Created: 2017-06-19 Last updated: 2018-01-13Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf