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From Complex Sentences to a Formal Semantic Representation using Syntactic Text Simplification and Open Information Extraction

  • Book
  • © 2022

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Authors:
  1. Christina Niklaus

    1. Rehetobel, Switzerland

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Table of contents (18 chapters)

  1. Front Matter

    Pages i-xli
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  2. Background

    1. Front Matter

      Pages 1-1
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    2. Introduction

      • Christina Niklaus
      Pages 3-19

    3. Related Work

      • Christina Niklaus
      Pages 21-84

  3. Discourse-Aware Sentence Splitting

    1. Front Matter

      Pages 85-85
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    2. Introduction

      • Christina Niklaus
      Pages 87-89

    3. Subtask 1: Splitting into Minimal Propositions

      • Christina Niklaus
      Pages 91-103

    4. Subtask 2: Establishing a Semantic Hierarchy

      • Christina Niklaus
      Pages 105-110

    5. Transformation Patterns

      • Christina Niklaus
      Pages 111-141

    6. Transformation Process

      • Christina Niklaus
      Pages 143-159

    7. Sentence Splitting Corpus

      • Christina Niklaus
      Pages 161-163

    8. Summary

      • Christina Niklaus
      Pages 165-166

  4. Open Information Extraction

    1. Front Matter

      Pages 167-167
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    2. Introduction

      • Christina Niklaus
      Pages 169-170

    3. Subtask 3: Extracting Semantically Typed Relational Tuples

      • Christina Niklaus
      Pages 171-177

    4. Generating a Formal Semantic Representation

      • Christina Niklaus
      Pages 179-187

    5. Summary

      • Christina Niklaus
      Pages 189-190

  5. Evaluation

    1. Front Matter

      Pages 191-191
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    2. Experimental Setup

      • Christina Niklaus
      Pages 193-212

    3. Results and Discussion

      • Christina Niklaus
      Pages 213-273
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Keywords

About this book

This work presents a discourse-aware Text Simplification approach that splits and rephrases complex English sentences within the semantic context in which they occur. Based on a linguistically grounded transformation stage, complex sentences are transformed into shorter utterances with a simple canonical structure that can be easily analyzed by downstream applications. To avoid breaking down the input into a disjointed sequence of statements that is difficult to interpret, the author incorporates the semantic context between the split propositions in the form of hierarchical structures and semantic relationships, thus generating a novel representation of complex assertions that puts a semantic layer on top of the simplified sentences. In a second step, she leverages the semantic hierarchy of minimal propositions to improve the performance of Open IE frameworks. She shows that such systems benefit in two dimensions. First, the canonical structure of the simplified sentences facilitatesthe extraction of relational tuples, leading to an improved precision and recall of the extracted relations. Second, the semantic hierarchy can be leveraged to enrich the output of existing Open IE approaches with additional meta-information, resulting in a novel lightweight semantic representation for complex text data in the form of normalized and context-preserving relational tuples.

Authors and Affiliations

  • Rehetobel, Switzerland

    Christina Niklaus

About the author

About the author 
Christina Niklaus is an Assistant Professor in Computer Science at the University of St.Gallen with a focus on Data Science and NLP. 


Bibliographic Information

  • Book Title: From Complex Sentences to a Formal Semantic Representation using Syntactic Text Simplification and Open Information Extraction

  • Authors: Christina Niklaus

  • DOI: https://doi.org/10.1007/978-3-658-38697-9

  • Publisher: Springer Vieweg Wiesbaden

  • eBook Packages: Education, Education (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2022

  • Softcover ISBN: 978-3-658-38696-2Published: 30 August 2022

  • eBook ISBN: 978-3-658-38697-9Published: 29 August 2022

  • Edition Number: 1

  • Number of Pages: XLI, 318

  • Number of Illustrations: 48 b/w illustrations, 58 illustrations in colour

  • Topics: Computational Linguistics, Natural Language Processing (NLP), Professional Computing

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