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Word Representations and Machine Learning Models for Implicit Sense Classification in Shallow Discourse Parsing
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

CoNLL 2015 featured a shared task on shallow discourse parsing. In 2016, the efforts continued with an increasing focus on sense classification. In the case of implicit sense classification, there was an interesting mix of traditional and modern machine learning classifiers using word representation models. In this thesis, we explore the performance of a number of these models, and investigate how they perform using a variety of word representation models. We show that there are large performance differences between word representation models for certain machine learning classifiers, while others are more robust to the choice of word representation model. We also show that with the right choice of word representation model, simple and traditional machine learning classifiers can reach competitive scores even when compared with modern neural network approaches. 

Place, publisher, year, edition, pages
2017. , p. 43
Keywords [en]
machine learning, neural networks, word representations, word embeddings, distributional semantic models, word vectors, discourse parsing, shallow discourse parsing
Keywords [sv]
maskininlärning, neurala nätverk, ordrepresentationer, distributionella semantiska modeller, diskursparsning
National Category
Language Technology (Computational Linguistics)
Identifiers
URN: urn:nbn:se:uu:diva-325876OAI: oai:DiVA.org:uu-325876DiVA, id: diva2:1117288
Subject / course
Language Technology
Educational program
Master Programme in Language Technology
Supervisors
Examiners
Available from: 2017-06-28 Created: 2017-06-28 Last updated: 2018-01-13Bibliographically approved

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Language Technology (Computational Linguistics)

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • 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