List of Contributors |
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xi | |
Introduction and Overview |
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1 | (20) |
Part One Foundational Components Of Storylines |
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21 | (122) |
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1 The Role of Event-Based Representations and Reasoning in Language |
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23 | (24) |
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23 | (2) |
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1.2 Introducing Situations and Events |
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25 | (8) |
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1.3 Modeling the Substructure of Events |
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33 | (5) |
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1.4 Enriching VerbNet with Event Dynamics |
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38 | (4) |
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42 | (5) |
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2 The Rich Event Ontology: Ontological Hub for Event Representations |
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47 | (20) |
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47 | (1) |
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48 | (8) |
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2.3 Semantic Role Labeling |
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56 | (5) |
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2.4 Conclusions, Gaps, and Future Work |
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61 | (6) |
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3 Decomposing Events and Storylines |
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67 | (20) |
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3.1 Introduction: Events within Stories and Events within Events |
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67 | (2) |
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3.2 Constructions as Well as Verbs Determine the Internal Structure of Events |
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69 | (2) |
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3.3 Time and Qualitative State (Change) |
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71 | (3) |
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74 | (4) |
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78 | (2) |
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80 | (2) |
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82 | (5) |
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4 Extracting and Aligning Timelines |
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87 | (19) |
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87 | (2) |
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89 | (6) |
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95 | (5) |
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4.4 Bringing It All Together |
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100 | (1) |
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101 | (5) |
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106 | (19) |
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106 | (2) |
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5.2 Modelling Causal Relations |
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108 | (2) |
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5.3 Causal Annotation in Natural Language Text |
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110 | (4) |
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5.4 Extracting Event Causality |
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114 | (3) |
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5.5 Causal Commonsense Discovery |
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117 | (3) |
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120 | (5) |
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6 A Narratology-Based Framework for Storyline Extraction |
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125 | (18) |
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125 | (1) |
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6.2 A Narratology-Grounded Framework for Storylines Identification |
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126 | (4) |
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6.3 From Theory to Data: Annotating Causelines and Storylines |
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130 | (4) |
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6.4 Validating Causelines and Extracting Storylines |
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134 | (3) |
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137 | (6) |
Part Two Connecting The Dots: Resources, Tools, And Representations |
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143 | (117) |
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7 The Richer Event Description Corpus for Event-Event Relations |
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145 | (18) |
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145 | (1) |
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7.2 A Comparison of Event Annotation Choices |
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146 | (9) |
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7.3 Long-Distance Relations in RED: Contains, Causality, and Coreference |
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155 | (1) |
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7.4 Studying RED Impact on Event Ordering |
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156 | (2) |
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158 | (5) |
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8 Low-Resource Event Extraction via Share-and-Transfer and Remaining Challenges |
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163 | (24) |
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163 | (3) |
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166 | (1) |
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8.3 Share: Construction of Common Semantic Space |
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167 | (8) |
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8.4 Transfer: From High- to Low-Resource Setting |
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175 | (2) |
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8.5 Transfer Learning Performance |
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177 | (1) |
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177 | (5) |
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8.7 Conclusions and Future Research Directions |
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182 | (5) |
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9 Reading Certainty across Sources |
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187 | (16) |
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187 | (5) |
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192 | (2) |
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194 | (1) |
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195 | (3) |
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198 | (2) |
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200 | (3) |
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10 Narrative Homogeneity and Heterogeneity in Document Categories |
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203 | (18) |
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10.1 Introduction: Narrative Schemas and Their Evaluations |
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203 | (2) |
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205 | (1) |
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10.3 Data and Schema Generation |
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206 | (2) |
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10.4 Evidence through NASTEA Task |
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208 | (5) |
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10.5 Evidence through Schema Stability |
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213 | (3) |
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216 | (1) |
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217 | (4) |
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11 Exploring Machine Learning Techniques for Linking Event Templates |
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221 | (19) |
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221 | (3) |
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224 | (1) |
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11.3 Event Similarity Metrics |
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225 | (5) |
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230 | (6) |
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236 | (4) |
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12 Semantic Storytelling: From Experiments and Prototypes to a Technical Solution |
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240 | (20) |
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12.1 Introduction: Technologies for Content Curation |
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240 | (2) |
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12.2 Semantic Storytelling: Selected Components |
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242 | (4) |
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12.3 Semantic Storytelling in Industry Use Cases |
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246 | (4) |
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12.4 Towards a Flexible and Robust Technology Solution for Semantic Storytelling |
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250 | (2) |
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252 | (2) |
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12.6 Conclusions and Future Work |
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254 | (6) |
Author Index |
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260 | |