Annotating Temporal Dependency Graphs via Crowdsourcing
| Autor: | Haoling Qiu, Bonan Min, Nianwen Xue, Jiarui Yao |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Scheme (programming language)
Dependency (UML) Computer science business.industry 02 engineering and technology Temporal annotation Crowdsourcing computer.software_genre Graph Data set Annotation 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Representation (mathematics) Completeness (statistics) computer Natural language processing computer.programming_language |
| Zdroj: | EMNLP (1) |
| DOI: | 10.18653/v1/2020.emnlp-main.432 |
| Popis: | We present the construction of a corpus of 500 Wikinews articles annotated with temporal dependency graphs (TDGs) that can be used to train systems to understand temporal relations in text. We argue that temporal dependency graphs, built on previous research on narrative times and temporal anaphora, provide a representation scheme that achieves a good trade-off between completeness and practicality in temporal annotation. We also provide a crowdsourcing strategy to annotate TDGs, and demonstrate the feasibility of this approach with an evaluation of the quality of the annotation, and the utility of the resulting data set by training a machine learning model on this data set. The data set is publicly available. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|