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Source code and data for the PhD thesis "Linguistically-inspired neural coherence modeling": source code and data for the fives projects that compose the PhD Thesis "Linguistically-inspired neural coherence modeling"

This dataset contains source code and data used in the PhD thesis "Linguistically-Inspired Neural Coherence Modeling". The dataset is split into five repositories: StruSim: Source code to run experiments for Chapter 4 "Document Structure Similarity-Enhanced Coherence Modeling". C...

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Bibliographic Details
Main Author: Liu, Wei (Author)
Format: Database Research Data
Language:English
Published: Heidelberg Universität 2025-09-03
DOI:10.11588/DATA/ZBNUCG
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Forschungsdaten
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Online Access:Verlag, kostenfrei, Volltext: https://doi.org/10.11588/DATA/ZBNUCG
Verlag, kostenfrei, Volltext: https://heidata.uni-heidelberg.de/dataset.xhtml?persistentId=doi:10.11588/DATA/ZBNUCG
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Author Notes:Wei Liu
Description
Summary:This dataset contains source code and data used in the PhD thesis "Linguistically-Inspired Neural Coherence Modeling". The dataset is split into five repositories: StruSim: Source code to run experiments for Chapter 4 "Document Structure Similarity-Enhanced Coherence Modeling". ConnRel: Source code to run experiments for Chapter 5 "Annotation-inspired Implicit Discourse Relation Classification". Exp2Imp: Source code to run experiments for Chapter 6 "Explicit to Implicit Discourse Relation Classification". RelCoh: Source code to run experiments for Chapter 7 "Discourse Relation-Enhanced Coherence Modeling". EntyRelCoh: Source code to run experiments for Chapter 8 "Coherence Modeling Using Entities and Discourse Relations". The data used in the experiments can be downloaded from Linguistic Data Consortium (https://www.ldc.upenn.edu/): PDTB 2.0: https://catalog.ldc.upenn.edu/LDC2008T05 PDTB 3.0: https://catalog.ldc.upenn.edu/LDC2019T05 TOEFL Dataset: https://catalog.ldc.upenn.edu/LDC2014T06 GCDC: https://github.com/aylai/GCDC-corpus CoheSentia: https://github.com/AviyaMn/CoheSentia
Item Description:Gefördert durch die Klaus Tschira Founndation
Gesehen am 03.09.2025
Physical Description:Online Resource
DOI:10.11588/DATA/ZBNUCG

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