Abstract
Temporal Information Processing is a subfield of Natural Language Processing, valuable in many tasks like Question Answering and Summarization. Temporal Information Processing is broadened, ranging from classical theories of time and language to current computational approaches for Temporal Information Extraction. This later trend consists on the automatic extraction of events and temporal expressions. Such issues have attracted great attention especially with the development of annotated corpora and annotations schemes mainly TimeBank and TimeML. In this paper, we give a survey of Temporal Information Extraction from Natural Language texts.
Highlights
Information Extraction is gaining increased attention by researchers who seek to acquire knowledge from huge amount of natural language contents
Dynamic facts as temporal information have been neglected, time is a crucial dimension in any information space [1]
The main purpose of this paper is to present a survey of the Temporal Information Extraction domain
Summary
Information Extraction is gaining increased attention by researchers who seek to acquire knowledge from huge amount of natural language contents. Dynamic facts as temporal information have been neglected, time is a crucial dimension in any information space [1]. This limitation can be explained by the complexity of such task. The classical techniques used to extract named entities and events from textual contents are unable to identify the temporal relations between events or to infer the chronological ordering of these events. Such processes require a grater effort to analyze how temporal information is conveyed in real texts, especially when temporal information is implicitly expressed
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