The current leading perspective on temporal information As a first approach towards this goal, in this paper, extraction regards three phases: (1) a temporal we propose several initial time-line models in entity recognition phase, extracting events this paradigm, that directly predict - in a linear (blue boxes in Figure 1) and their attributes, and extracting fashion - start points and durations for each entity, temporal expressions (green boxes), and using text with annotated temporal entities as input normalizing their values to dates or durations, (2) (shown in Figure 1). The predicted start points and a relation extraction phase, where temporal links durations constitute a relative time-line, i.e. a total (TLinks) among those entities, and between events order on entity start and end points. The time-line and the document-creation time (DCT) are found is relative, as start and duration values cannot (yet) (arrows in Figure 1, left). And (3), construction of a be mapped to absolute calender dates or durations time-line (Figure 1, right) from the extracted temporal expressed in seconds. It represents the relative links, if they are temporally consistent. Much temporal order and inclusions that temporal entities research concentrated on the first two steps, but have with respect to each other by the quantitative very little research looks into step 3, time-line construction, start and end values of the entities. Relative which is the focus of this work.

Time is deeply woven into how people perceive, and communicate about the world. Almost unconsciously, we provide our language utterances with temporal cues, like verb tenses, and we can hardly produce sentences without such cues. 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, the process of combining different temporal cues into a coherent temporal view, plays a central role in temporal information extraction. This article presents a comprehensive survey of the research from the past decades on temporal reasoning for automatic temporal information extraction from text, providing a case study on the integration of symbolic reasoning with machine learning-based information extraction systems.