Temporal Relation Classification
6 papers with code • 4 benchmarks • 5 datasets
Temporal Relation Classification is the task that is concerned with classifying the temporal relation between a pair of temporal entities (traditional events and temporal expressions). Initial approaches aimed to classify the temporal relation in thirteen relation types that were depicted by James Allen in his seminal work "Maintaining Knowledge about Temporal Intervals". However, due to the ambiguity in the annotation, recent corpora have been limiting the type of relations to a subset of those relations.
Notice that although Temporal Relation Classification can be thought of as a subtask of Temporal Relation Extraction, the two tasks can be morphed if one adds a label that indicates the absence of a temporal relation between the entities (e.g. "no_relation" or "vague") to Temporal Relation Classification.
In this work, we extend our classification model's task loss with an unsupervised auxiliary loss on the word-embedding level of the model.
Extracting temporal relations (e. g., before, after, and simultaneous) among events is crucial to natural language understanding.
To achieve this goal, our work addresses the problems of subevent relation extraction (SRE) and temporal event relation extraction (TRE) that aim to predict subevent and temporal relations between two given event mentions/triggers in texts.
All in all, these problems have limited the fair comparison between approaches and consequently, the development of temporal extraction systems.