Robust Temporal Processing of News

By Inderjeet Mani, George Wilson. Annual Meeting of the Association for Computational Linguistics, 2000

409 citations recorded by the source corpus.

We introduce an annotation scheme for temporal expressions, and describe a method for resolving temporal expressions in print and broadcast news. The system, which is based on both hand-crafted and machine-learnt rules, achieves an 83.2% accuracy (F-measure) against hand-annotated data. Some initial steps towards tagging event chronologies are also described.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/abf83a31db90c86483194fd1d1a188040b4b0773

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Citations in the corpus

Towards Decision Support and Goal Achievement: Identifying Action-Outcome Relationships From Social Media (2015, methodology): “To do this, we built a simple rule based system, similar to TempEx [29], that can recognize and resolve basic expressions of relative oﬀsets (“yesterday”,“next weekend”), as well as references to nearby days and dates (“Tuesday” and “Feb 10th”).”
Query based event extraction along a timeline (2004, background): “While it is a difficult problem to correctly extract the chronological order of events from free text [12], we attempt only to attach dates to sentences extracted from news articles.”
Temporal summaries of new topics (2001, background): “We expect that named entity tagging and possibly temporal expression normalization [22] may help match events and topics.”
Machine Learning of Temporal Relations (2006, methodology): “In terms of hand-coded approaches, (Mani and Wilson 2000) used a baseline method of blindly propagating TempEx time values to events based on proximity, obtaining 59.4% on a small sample of 8,505 words of text.”
Toponym resolution in text: annotation, evaluation and applications of spatial grounding (2007, background): “Whereas temporal grounding has received considerable attention in the recent Past [2, 3], robust spatial grounding has long been neglected.”
TimeML: Robust Specification of Event and Temporal Expressions in Text (2003, background): “There has recently been a renewed interest in temporal and event-based reasoning in language and text, particularly as applied to information extraction and reasoning tasks (cf. Mani and Wilson, 2000, ).”
Inferring Strategies for Sentence Ordering in Multidocument News Summarization (2002, background): “While there have been recent developments in disam-biguating temporal expressions and event ordering (Wiebe, O’Hara, Ohrstrom-Sandgren, & McKeever, 1998; Mani & Wilson, 2000; Filatova & Hovy, 2001), correlating events with the date on which they occurred is a hard task.”
A Bottom-Up Approach to Sentence Ordering for Multi-Document Summarization (2006, background): “Inferring temporal relations among events (Mani et al., 2003; Mani and Wilson, 2000) using implicit time references (such as tense”
Open domain event extraction from twitter (2012, methodology): “TempEx s high precision on Tweets can be explained by the fact that some temporal expressions are relatively unambiguous.”
SUTime: A library for recognizing and normalizing time expressions (2012, methodology): “GUTime is an extension of the TempEx tagger (Mani and Wilson, 2000), which is targeted for ACE TIMEX2.”