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Dependency Parsing for Chinese Social Media Text
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In this thesis, we investigate dependency parsing for Chinese social media text. In order to investigate our research questions, we conduct several parsing experiments on standard Chinese and social media data both with UUParser and UDPipe, to assess parser performance on social media data, and to improve this performance over baselines. The main contributions of this study are as follows: first, the annotation of data sets for Chinese social media data with or without manual annotation; second, the dependency parsing of social media data can be improved by providing more relevant training data. In addition, difference between domains of training data and test data can hurt performance for both parsers.

Place, publisher, year, edition, pages
2019. , p. 32
National Category
Language Technology (Computational Linguistics)
Identifiers
URN: urn:nbn:se:uu:diva-384556OAI: oai:DiVA.org:uu-384556DiVA, id: diva2:1320946
Subject / course
Language Technology
Educational program
Master Programme in Language Technology
Supervisors
Examiners
Available from: 2019-06-12 Created: 2019-06-06 Last updated: 2019-06-12Bibliographically approved

Open Access in DiVA

The full text will be freely available from 2020-06-15 12:00
Available from 2020-06-15 12:00

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Department of Linguistics and Philology
Language Technology (Computational Linguistics)

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
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  • Other locale
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Output format
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