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Janis Pagel Nils Reiter Ina Rsiger Sarah Schulz A Unied Text Annotation Workow for Diverse Goals Discovering phenomena not covered by a theory Strengthening denitions in a theory Often confused categories might be overlapping or

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A Unied Text Annotation Workow for Diverse Goals

Janis Pagel Nils Reiter Ina Rösiger Sarah Schulz

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Why do we annotate? Empirical validation of theories Discovering phenomena not covered by a theory Strengthening denitions in a theory Often confused categories might be overlapping or at least unclear Uncovering implicit assumptions Data creation Manually annotated data can be analysed Which categories are how frequent in what context? Automatic tools can be evaluated How well do machines do this task? Supervised tools can be trained

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 2

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Why do we annotate? Empirical validation of theories Discovering phenomena not covered by a theory Strengthening denitions in a theory Often confused categories might be overlapping or at least unclear Uncovering implicit assumptions Data creation Manually annotated data can be analysed Which categories are how frequent in what context? Automatic tools can be evaluated How well do machines do this task? Supervised tools can be trained

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 2

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Why do we annotate? Empirical validation of theories Discovering phenomena not covered by a theory Strengthening denitions in a theory Often confused categories might be overlapping or at least unclear Uncovering implicit assumptions Data creation Manually annotated data can be analysed Which categories are how frequent in what context? Automatic tools can be evaluated How well do machines do this task? Supervised tools can be trained

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 2

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Analog annotation … Ideas attached to spans of text Sometimes fuzzy text spans

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 3

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…and digital annotation Explicit assignment of categories to text spans Text spans are explicitly bounded (begin, end)

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 4

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Circles

Annotation (Circle) Well known in Computational Linguis- tics Automation-centered “Enrich with knowledge”

Hovy and Lavid (2010) Pustejovsky and Stubbs (2012)

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 5

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Circles

Annotation (Circle) Well known in Computational Linguis- tics Automation-centered “Enrich with knowledge”

Hovy and Lavid (2010) Pustejovsky and Stubbs (2012)

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 5

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Circles

Annotation (Circle) Well known in Computational Linguis- tics Automation-centered “Enrich with knowledge”

Hovy and Lavid (2010) Pustejovsky and Stubbs (2012)

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 5

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Circles

Hermeneutic Circle Well known in Humanities Interpretation-centered “Retrieve knowledge”

Gius and Jacke (2017)

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 6

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Circles

Hermeneutic Circle Well known in Humanities Interpretation-centered “Retrieve knowledge”

Gius and Jacke (2017)

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 6

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Workow

Theoretical notion Data (Proto) annotation guidelines Annotation Anno- tated text/ corpus Analysis Automation Interpretation

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 7

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Workow

Theoretical notion Data (Proto) annotation guidelines Annotation Anno- tated text/ corpus Analysis Automation Interpretation

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 7

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Workow

Theoretical notion Data (Proto) annotation guidelines Annotation Anno- tated text/ corpus Analysis Automation Interpretation

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 7

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Workow

Theoretical notion Data (Proto) annotation guidelines Annotation Anno- tated text/ corpus Analysis Automation Interpretation

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 7

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Workow

Theoretical notion Data (Proto) annotation guidelines Annotation Anno- tated text/ corpus Analysis Automation Interpretation

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 7

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Workow

Theoretical notion Data (Proto) annotation guidelines Annotation Anno- tated text/ corpus Analysis Automation Interpretation

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 7

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Workow

Theoretical notion Data (Proto) annotation guidelines Annotation Anno- tated text/ corpus Analysis Automation Interpretation

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 7

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Workow

Theoretical notion Data (Proto) annotation guidelines Annotation Anno- tated text/ corpus Analysis Automation Interpretation

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 7

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Workow

Theoretical notion Data (Proto) annotation guidelines Annotation Anno- tated text/ corpus Analysis Automation Interpretation

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 7

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Workow

Theoretical notion Data (Proto) annotation guidelines Annotation Anno- tated text/ corpus Analysis Automation Interpretation

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 7

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Goals of Annotation

Exploratory Mostly note-taking Semi-organized Humanities centered Ideally completely free of presuppositions Examples Pliny (Bradley, 2008) 3DH (Kleymann, Meister, and Stange, 2018)

Theoretical notion Data (Proto) annotation guidelines Annotation Anno- tated text/ corpus Analysis Automation Interpretation

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 8

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Goals of Annotation

Exploratory Mostly note-taking Semi-organized Humanities centered Ideally completely free of presuppositions Examples Pliny (Bradley, 2008) 3DH (Kleymann, Meister, and Stange, 2018)

Theoretical notion Data (Proto) annotation guidelines Annotation Anno- tated text/ corpus Analysis Automation Interpretation

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 8

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Goals of Annotation

Exploratory Mostly note-taking Semi-organized Humanities centered Ideally completely free of presuppositions Examples Pliny (Bradley, 2008) 3DH (Kleymann, Meister, and Stange, 2018)

Theoretical notion Data (Proto) annotation guidelines Annotation Anno- tated text/ corpus Analysis Automation Interpretation

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 8

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Goals of Annotation

Exploratory Mostly note-taking Semi-organized Humanities centered Ideally completely free of presuppositions Examples Pliny (Bradley, 2008) 3DH (Kleymann, Meister, and Stange, 2018)

Theoretical notion Data (Proto) annotation guidelines Annotation Anno- tated text/ corpus Analysis Automation Interpretation

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 8

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Goals of Annotation

Exploratory Mostly note-taking Semi-organized Humanities centered Ideally completely free of presuppositions Examples Pliny (Bradley, 2008) 3DH (Kleymann, Meister, and Stange, 2018)

Theoretical notion Data (Proto) annotation guidelines Annotation Anno- tated text/ corpus Analysis Automation Interpretation

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 8

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Goals of Annotation

Exploratory Mostly note-taking Semi-organized Humanities centered Ideally completely free of presuppositions Examples Pliny (Bradley, 2008) 3DH (Kleymann, Meister, and Stange, 2018)

Theoretical notion Data (Proto) annotation guidelines Annotation Anno- tated text/ corpus Analysis Automation Interpretation

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 8

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Goals of Annotation

Exploratory Mostly note-taking Semi-organized Humanities centered Ideally completely free of presuppositions Examples Pliny (Bradley, 2008) 3DH (Kleymann, Meister, and Stange, 2018)

Theoretical notion Data (Proto) annotation guidelines Annotation Anno- tated text/ corpus Analysis Automation Interpretation

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 8

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Pliny

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 9

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3DH

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 10

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Goals of Annotation

Conceptualizing Sharpen theoretic notions Find disagreements

  • f

theoretical claims

  • n

concrete texts Examples heureCLÉA (Bögel et al., 2015) QuaDramA (Rösiger, Schulz, and Reiter, 2018)

Theoretical notion Data (Proto) annotation guidelines Annotation Anno- tated text/ corpus Analysis Automation Interpretation

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 11

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Goals of Annotation

Conceptualizing Sharpen theoretic notions Find disagreements

  • f

theoretical claims

  • n

concrete texts Examples heureCLÉA (Bögel et al., 2015) QuaDramA (Rösiger, Schulz, and Reiter, 2018)

Theoretical notion Data (Proto) annotation guidelines Annotation Anno- tated text/ corpus Analysis Automation Interpretation

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 11

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Goals of Annotation

Conceptualizing Sharpen theoretic notions Find disagreements

  • f

theoretical claims

  • n

concrete texts Examples heureCLÉA (Bögel et al., 2015) QuaDramA (Rösiger, Schulz, and Reiter, 2018)

Theoretical notion Data (Proto) annotation guidelines Annotation Anno- tated text/ corpus Analysis Automation Interpretation

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 11

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Goals of Annotation

Conceptualizing Sharpen theoretic notions Find disagreements

  • f

theoretical claims

  • n

concrete texts Examples heureCLÉA (Bögel et al., 2015) QuaDramA (Rösiger, Schulz, and Reiter, 2018)

Theoretical notion Data (Proto) annotation guidelines Annotation Anno- tated text/ corpus Analysis Automation Interpretation

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 11

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Goals of Annotation

Conceptualizing Sharpen theoretic notions Find disagreements

  • f

theoretical claims

  • n

concrete texts Examples heureCLÉA (Bögel et al., 2015) QuaDramA (Rösiger, Schulz, and Reiter, 2018)

Theoretical notion Data (Proto) annotation guidelines Annotation Anno- tated text/ corpus Analysis Automation Interpretation

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 11

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Goals of Annotation

Conceptualizing Sharpen theoretic notions Find disagreements

  • f

theoretical claims

  • n

concrete texts Examples heureCLÉA (Bögel et al., 2015) QuaDramA (Rösiger, Schulz, and Reiter, 2018)

Theoretical notion Data (Proto) annotation guidelines Annotation Anno- tated text/ corpus Analysis Automation Interpretation

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 11

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Goals of Annotation

Conceptualizing Sharpen theoretic notions Find disagreements

  • f

theoretical claims

  • n

concrete texts Examples heureCLÉA (Bögel et al., 2015) QuaDramA (Rösiger, Schulz, and Reiter, 2018)

Theoretical notion Data (Proto) annotation guidelines Annotation Anno- tated text/ corpus Analysis Automation Interpretation

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 11

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Goals of Annotation

Conceptualizing Sharpen theoretic notions Find disagreements

  • f

theoretical claims

  • n

concrete texts Examples heureCLÉA (Bögel et al., 2015) QuaDramA (Rösiger, Schulz, and Reiter, 2018)

Theoretical notion Data (Proto) annotation guidelines Annotation Anno- tated text/ corpus Analysis Automation Interpretation

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 11

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Goals of Annotation

Conceptualizing Sharpen theoretic notions Find disagreements

  • f

theoretical claims

  • n

concrete texts Examples heureCLÉA (Bögel et al., 2015) QuaDramA (Rösiger, Schulz, and Reiter, 2018)

Theoretical notion Data (Proto) annotation guidelines Annotation Anno- tated text/ corpus Analysis Automation Interpretation

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 11

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heureCLÉA (Catma)

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 12

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QuaDramA

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 13

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Goals of Annotation

Explicating Structure text in an ob- servable way Helps interpretation Examples Nantke and Schlupkothen (2018)

Theoretical notion Data (Proto) annotation guidelines Annotation Anno- tated text/ corpus Analysis Automation Interpretation

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 14

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Goals of Annotation

Explicating Structure text in an ob- servable way Helps interpretation Examples Nantke and Schlupkothen (2018)

Theoretical notion Data (Proto) annotation guidelines Annotation Anno- tated text/ corpus Analysis Automation Interpretation

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 14

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Goals of Annotation

Explicating Structure text in an ob- servable way Helps interpretation Examples Nantke and Schlupkothen (2018)

Theoretical notion Data (Proto) annotation guidelines Annotation Anno- tated text/ corpus Analysis Automation Interpretation

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 14

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Goals of Annotation

Explicating Structure text in an ob- servable way Helps interpretation Examples Nantke and Schlupkothen (2018)

Theoretical notion Data (Proto) annotation guidelines Annotation Anno- tated text/ corpus Analysis Automation Interpretation

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 14

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Goals of Annotation

Explicating Structure text in an ob- servable way Helps interpretation Examples Nantke and Schlupkothen (2018)

Theoretical notion Data (Proto) annotation guidelines Annotation Anno- tated text/ corpus Analysis Automation Interpretation

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 14

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Goals of Annotation

Explicating Structure text in an ob- servable way Helps interpretation Examples Nantke and Schlupkothen (2018)

Theoretical notion Data (Proto) annotation guidelines Annotation Anno- tated text/ corpus Analysis Automation Interpretation

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 14

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Nantke and Schlupkothen: Modeling Complex Intertextual Relations

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 15

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Nantke and Schlupkothen: Modeling Complex Intertextual Relations

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 16

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Goals of Annotation

Automation-oriented Create input data for au- tomatic learning proce- dures Usually uses existing the-

  • retic notions

NLP/CL centered Examples GRAIN (Schweitzer et al., 2018) Schulz and Kuhn (2016)

Theoretical notion Data Annotation guidelines Annotation Anno- tated text/ corpus Analysis Automation Interpretation

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 17

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Goals of Annotation

Automation-oriented Create input data for au- tomatic learning proce- dures Usually uses existing the-

  • retic notions

NLP/CL centered Examples GRAIN (Schweitzer et al., 2018) Schulz and Kuhn (2016)

Theoretical notion Data Annotation guidelines Annotation Anno- tated text/ corpus Analysis Automation Interpretation

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 17

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Goals of Annotation

Automation-oriented Create input data for au- tomatic learning proce- dures Usually uses existing the-

  • retic notions

NLP/CL centered Examples GRAIN (Schweitzer et al., 2018) Schulz and Kuhn (2016)

Theoretical notion Data Annotation guidelines Annotation Anno- tated text/ corpus Analysis Automation Interpretation

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 17

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Goals of Annotation

Automation-oriented Create input data for au- tomatic learning proce- dures Usually uses existing the-

  • retic notions

NLP/CL centered Examples GRAIN (Schweitzer et al., 2018) Schulz and Kuhn (2016)

Theoretical notion Data Annotation guidelines Annotation Anno- tated text/ corpus Analysis Automation Interpretation

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 17

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Goals of Annotation

Automation-oriented Create input data for au- tomatic learning proce- dures Usually uses existing the-

  • retic notions

NLP/CL centered Examples GRAIN (Schweitzer et al., 2018) Schulz and Kuhn (2016)

Theoretical notion Data Annotation guidelines Annotation Anno- tated text/ corpus Analysis Automation Interpretation

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 17

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Goals of Annotation

Automation-oriented Create input data for au- tomatic learning proce- dures Usually uses existing the-

  • retic notions

NLP/CL centered Examples GRAIN (Schweitzer et al., 2018) Schulz and Kuhn (2016)

Theoretical notion Data Annotation guidelines Annotation Anno- tated text/ corpus Analysis Automation Interpretation

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 17

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POS

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 18

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Discussion Annotation in DH Helps disciplines with high theoretic focus Supports intersubjectivity Dierent goals enforce dierent tools Not entirely clear how tools’ functionality and annotation goals interrelate How to treat true disagreements? (Gius and Jacke, 2017) CL usually requires unambiguous annotation data Humanities deal with ambiguity of concepts (dierent interpretations) How to measure these wanted disagreements? Representation problems Machine learning problems

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 19

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Discussion Annotation in DH Helps disciplines with high theoretic focus Supports intersubjectivity Dierent goals enforce dierent tools Not entirely clear how tools’ functionality and annotation goals interrelate How to treat true disagreements? (Gius and Jacke, 2017) CL usually requires unambiguous annotation data Humanities deal with ambiguity of concepts (dierent interpretations) How to measure these wanted disagreements? Representation problems Machine learning problems

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 19

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Discussion Annotation in DH Helps disciplines with high theoretic focus Supports intersubjectivity Dierent goals enforce dierent tools Not entirely clear how tools’ functionality and annotation goals interrelate How to treat true disagreements? (Gius and Jacke, 2017) CL usually requires unambiguous annotation data Humanities deal with ambiguity of concepts (dierent interpretations) How to measure these wanted disagreements? Representation problems Machine learning problems

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 19

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Conclusion Workow Goals Exploratory Conceptualizing Explicating Automation-oriented Example projects and areas Pliny (Tool) 3DH (Tool) heureCLÉA (Narratology) QuaDramA (Coreference) Nantke and Schlupkothen (Inter- textuality) GRAIN (Part-of-Speech tagging)

Theoretical notion Data (Proto) annotation guidelines Annotation Anno- tated text/ corpus Analysis Automation Interpretation

Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 20

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Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz Institute for Natural Language Processing (IMS), University of Stuttgart eMail {pageljs,reiterns,roesigia,schulzsh}@ims.uni-stuttgart.de Phone +49-711-685 813 {89,54,30,94} Website

http://www.ims.uni-stuttgart.de/

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References I

Bögel, Thomas et al. (2015). “Collaborative Text Annotation Meets Machine Learning: heureCLÉA, a Digital Heuristic of Narrative” . In: DHCommons 1. Bradley, John (2008). “Thinking about interpretation: Pliny and scholarship in the humanities” . In: Literary and Linguistic Computing 23.3, pp. 263–279. DOI: 10.1093/llc/fqn021. URL: http://dx.doi.org/10.1093/llc/fqn021. Gius, Evelyn and Janina Jacke (2017). “The Hermeneutic Prot of Annotation: On Preventing and Fostering Disagreement in Literary Analysis” . In: International Journal of Humanities and Arts Computing 11.2, pp. 233–254. DOI: 10.3366/ijhac.2017.0194. URL: https://doi.org/10.3366/ijhac.2017.0194. Hovy, Eduard and Julia Lavid (2010). “Towards a ‘Science’ of Corpus Annotation: A New Methodological Challenge for Corpus Linguistics” . In: International Journal of Translation Studies 22.1, pp. 13–36. Kleymann, Rabea, Jan Christoph Meister, and Jan-Erik Stange (Feb. 2018). “Perspektiven kritischer Interfaces für die Digital Humanities im 3DH-Projekt” . In: Book of Abstracts of DHd

  • 2018. Cologne, Germany.

Nantke, Julia and Frederik Schlupkothen (2018). “Zwischen Polysemie und Formalisierung: Mehrstuge Modellierung komplexer intertextueller Relationen als Annäherung an ein ,literarisches’ Semantic Web” . In: Proceedings of DHd. Pustejovsky, James and Amber Stubbs (2012). Natural Language Annotation for Machine Learning: A Guide to Corpus-Building for Applications. Sebastopol, Boston, Farnham: O’Reilly

  • Media. ISBN: 9781449306663.

Rösiger, Ina, Sarah Schulz, and Nils Reiter (2018). “Towards Coreference for Literary Text: Analyzing Domain-Specic Phenomena” . In: Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature. Santa Fe, USA. Schulz, Sarah and Jonas Kuhn (2016). “Learning from Within? Comparing PoS Tagging Approaches for Historical Text. ” . In: LREC. Ed. by Nicoletta Calzolari et al. European Language Resources Association (ELRA). URL: http://dblp.uni-trier.de/db/conf/lrec/lrec2016.html#SchulzK16. Schweitzer, Katrin et al. (2018). “German Radio Interviews: The GRAIN Release of the SFB732 Silver Standard Collection” . In: Proceedings of the 11th International Conference on Language Resources and Evaluation. LREC 2018. Janis Pagel, Nils Reiter, Ina Rösiger, Sarah Schulz, Institute for Natural Language Processing (IMS), University of Stuttgart: Unied Annotation Workow 22