Structured Interpretation of Temporal Relations
This work addresses the challenge of temporal relation modeling in natural language processing, offering a more consistent framework for annotation and computational tasks, though it is incremental in improving existing methods.
The authors tackled the problem of inconsistent and incomplete modeling of temporal relations by proposing a novel annotation approach that structures events and time expressions into a dependency tree, achieving stable and high inter-annotator agreement on a corpus of 235 documents across news and narratives.
Temporal relations between events and time expressions in a document are often modeled in an unstructured manner where relations between individual pairs of time expressions and events are considered in isolation. This often results in inconsistent and incomplete annotation and computational modeling. We propose a novel annotation approach where events and time expressions in a document form a dependency tree in which each dependency relation corresponds to an instance of temporal anaphora where the antecedent is the parent and the anaphor is the child. We annotate a corpus of 235 documents using this approach in the two genres of news and narratives, with 48 documents doubly annotated. We report a stable and high inter-annotator agreement on the doubly annotated subset, validating our approach, and perform a quantitative comparison between the two genres of the entire corpus. We make this corpus publicly available.