Abstract
Detecting and understanding temporal expressions are key tasks in natural language processing (NLP), and are important for event detection and information retrieval. In the existing approaches, temporal semantics are typically represented as discrete ranges or specific dates, and the task is restricted to text that conforms to this representation. We propose an alternate paradigm: that of distributed temporal semantics—where a probability density function models relative probabilities of the various interpretations. We extend SUTime, a state-of-the-art NLP system to incorporate our approach, and build definitions of new and existing temporal expressions. A worked example is used to demonstrate our approach: the estimation of the creation time of photos in online social networks (OSNs), with a brief discussion of how the proposed paradigm relates to the point- and interval-based systems of time. An interactive demonstration, along with source code and datasets, are available online.
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Notes
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- 3.
- 4.
Using the endpoint described at: http://www.flickr.com/services/api/flickr.photos.search.html.
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Using the endpoint described at: http://www.flickr.com/services/api/flickr.photos.getInfo.html.
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\(tm\_year(x)\) and related gnuplot functions were useful for this [17, p. 27].
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- 8.
We introduced an ‘X-GNUPlot-Function’ attribute on the TIMEX3 element for this purpose.
- 9.
The time when the photo was uploaded to the web, shown as the ‘posted’ attribute of the ‘dates’ element, see: http://www.flickr.com/services/api/flickr.photos.getInfo.html.
- 10.
See the ‘created_time’ field at: https://developers.facebook.com/docs/reference/api/photo/.
- 11.
The “weekend”, and precisely when it starts, is a good example of this. Readers will be able to imagine many different possible interpretations of the word.
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Blamey, B., Crick, T., Oatley, G. (2013). ‘The First Day of Summer’: Parsing Temporal Expressions with Distributed Semantics. In: Bramer, M., Petridis, M. (eds) Research and Development in Intelligent Systems XXX. SGAI 2013. Springer, Cham. https://doi.org/10.1007/978-3-319-02621-3_29
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