Temporal cues can refer to various components of the event intervals: the entire interval, but also just its start, end, or duration. Example sentence: Antibiotics were started on 6/2 and continued for 10 days. Respiratory status improved up til the last 2 days.

The type of information given for each component can also differ: it can be absolute, or relative to other intervals.

Temporal information provided through language is generally underspecified. Therefore, world knowledge about the typical order of events, their durations, and even typical time between events may be needed to properly interpret the temporal meaning that is to be conveyed.

Temporal cues

AIA is based on a set of thirteen mutually exclusive basic interval relations that can be assigned to any pair of definite intervals where relative positions of start and end-points are known. If for two intervals x and y information about the relative positions of their start and end-points is incomplete, a general Allen relation can be assigned, expressed as the disjunction of all possible basic relations. The full AIA covers the powerset of all thirteen basic relations, comprising $2^{13}=8192$ relations.


TR framework: Allen’s interval algebra (AIA)

The most popular scheme is TimeML, used as the basis for many corpora, and for TempEval, a series of shared tasks on TIE evaluation that resulted in many of the existing TIE systems. The core TimeML concepts are event expressions and time expressions (timex).

In TimeML, events and timex expressions are temporally connected through interval relations, called temporal links (TLinks), approximately following Allen’s basic interval relations, consequently focusing on definite information.

Scheme for temporal annotation

A widely recognized problem in temporal annotation, is that annotators regularly miss temporal cues in the text, especially when annotating TLinks between pairs of events, as there are so many possible relations to annotate, and not all event-pairs are equally useful to annotate.

A frequently made TR-based preprocessing step is to calculate the transitive closure over the annotated relations, to obtain a more dense graph. The effect of temporal closure on performance for models that use no TR during prediction is ambivalent: it could improve or decrease improvement. When using a model that exploits TR also during prediction, data expansion through a temporal closure can be beneficial.

Annotation & Data Preprocessing

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

Temporal Information Extraction (TIE) is the process of automatically extracting temporal cues from text, with the goal to construct a (possibly underspecified) timeline of events from them.

TIE not only plays a major role in the general problem of natural language understanding (NLU), but is also used in many applications: information retrieval, question answering, and multidocument summarization, and has great potential in the clinical domain, for applications like patient timeline visualization, forecasting treatment effects, and patient selection for clinical trials.

University of Michigan - Ann Arbor

Extracting temporal cues from text, and constructing a global temporal view about the order of described events is a major challenge of automatic natural language understanding. 

Temporal reasoning (TR), the process of combining different temporal cues into a coherent temporal view, plays a central role in temporal information extraction.

Temporal Reasoning

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.

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