Publication:

Automatic Annotation and Assessment of Syntactic Structures in Law Texts Combining Rule-Based and Statistical Methods

Date

Date

Date
2016
Dissertation

Citations

Citation copied

Sugisaki, K. (2016). Automatic Annotation and Assessment of Syntactic Structures in Law Texts Combining Rule-Based and Statistical Methods. (Dissertation, University of Zurich) https://doi.org/10.5167/uzh-126637

Abstract

Abstract

Abstract

In this thesis, I investigate and develop methods for automatically analyzing and assessing German syntactic structures in domain-specific texts. As domain-specific texts, I use Swiss German-language law texts. The automatic annotation of syntactic structures has long been studied in the research on natural language processing. Supervised statistical methods are regarded as state-of-the-art parsing methods, which are accurate but biased by the type of text. Consequently, the accuracy of statistical parsers decreases if they are used o

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343 since deposited on 2016-10-14
340last week
Acq. date: 2025-11-12

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315 since deposited on 2016-10-14
313last week
Acq. date: 2025-11-12

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Additional indexing

Creators (Authors)

  • Sugisaki, Kyoko

Institution

Institution

Institution

Faculty

Faculty

Faculty
Faculty of Arts

Item Type

Item Type

Item Type
Dissertation

Referees

  • Volk, Martin
  • Schneider, Gerold
  • Kübler, Sandra

Dewey Decimal Classifikation

Dewey Decimal Classifikation

Dewey Decimal Classifikation

Language

Language

Language
English

Publication date

Publication date

Publication date
2016-02

Date available

Date available

Date available
2016-10-14

OA Status

OA Status

OA Status
Green

Metrics

Downloads

343 since deposited on 2016-10-14
340last week
Acq. date: 2025-11-12

Views

315 since deposited on 2016-10-14
313last week
Acq. date: 2025-11-12

Citations

Citations

Citation copied

Sugisaki, K. (2016). Automatic Annotation and Assessment of Syntactic Structures in Law Texts Combining Rule-Based and Statistical Methods. (Dissertation, University of Zurich) https://doi.org/10.5167/uzh-126637

Green Open Access
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