Two greedy approaches have been proposed to construct a graph of temporal relations: relations are added one-by-one, in a certain order, checking temporal consistency after each addition. The order can be natural reading order, or follow the score from a local classifier.

University of Michigan - Ann Arbor

There are various ways how integration of TR in training and prediction can improve TIE models: (1) ensure temporal consistency among predicted relations, and (2) to constrain the output space and improve prediction accuracy.

Integrate TR during training and prediction

Leeuwenberg, A., & Moens, M. F. (2019). A survey on temporal reasoning for temporal information extraction from text. Journal of Artificial Intelligence Research, 66, 341-380.

A Survey on Temporal Reasoning for Temporal Information Extraction from Text (Extended Abstract)

Greedy Inference

It first predicts the full graph with a local model, and detect and resolve inconsistencies afterwards, by removing conflicting edges based on confidence among others. Most works report a positive impact of this method, but differ in how they remove edges.

Post-Hoc Conflict Resolution

The sieve-based method is flexible in incorporation of both rule-based and machine learning components. This approach extract TLinks in consecutive phases by separate
model components (or sieves). Each sieve extracts TLinks, using the original input text, and the outputs from earlier sieves. TR is incorporated by taking a transitive closure after applying each sieve.

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