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Michele Filannino
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Discovery of temporal information is key for organising knowledge and therefore the task of extracting and representing temporal information from texts has received an increasing interest. In this paper we focus on the discovery of temporal footprints from encyclopaedic descriptions. Temporal footprints are time-line periods that are associated to the existence of specific concepts. Our approach relies on the extraction of date mentions and prediction of lower and upper bound- aries that define temporal footprints. We report on several experiments on persons’ pages from Wikipedia in order to illustrate the feasibility of the proposed methods.
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Human brain has evolved to master, among the others, the capacity of extracting flows of events out of a speech or a written text. This temporal sense, mainly unconscious, allows us to summarise, organise, remember and combine different pieces of information working out new insights and discoveries. The temporal dimension is an inescapable and easy truth for us, but enabling machines to fully deal with time is a challenging task. Computers are still incapable of detecting temporal incompatibilities, summarising workflows or identifying causes and consequences of facts. My research wants to answer the following questions: Can computers understand time? And what possibilities will that unlock?
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Discovery of temporal information is key for organising knowledge and therefore the task of extracting and representing temporal information from texts has received an increasing interest. In this paper we focus on the discovery of temporal footprints from encyclopaedic descriptions. Temporal footprints are time-line periods that are associated to the existence of specific concepts. Our approach relies on the extraction of date mentions and prediction of lower and upper bound- aries that define temporal footprints. We report on several experiments on persons’ pages from Wikipedia in order to illustrate the feasibility of the proposed methods.
Mining temporal footprints from Wikipedia
Mining temporal footprints from Wikipedia
Michele Filannino
Human brain has evolved to master, among the others, the capacity of extracting flows of events out of a speech or a written text. This temporal sense, mainly unconscious, allows us to summarise, organise, remember and combine different pieces of information working out new insights and discoveries. The temporal dimension is an inescapable and easy truth for us, but enabling machines to fully deal with time is a challenging task. Computers are still incapable of detecting temporal incompatibilities, summarising workflows or identifying causes and consequences of facts. My research wants to answer the following questions: Can computers understand time? And what possibilities will that unlock?
Can computers understand time?
Can computers understand time?
Michele Filannino
Paper presentation: D. N. Reshef et al., “Detecting Novel Associations in Large Data Sets,” Science, vol. 334, no. 6062, pp. 1518-1524, 2011.
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Mining temporal footprints from Wikipedia
Mining temporal footprints from Wikipedia
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Can computers understand time?
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Detecting novel associations in large data sets
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Temporal expressions identification in biomedical texts
My research taster project
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Nonlinear component analysis as a kernel eigenvalue problem
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