Time series from textual instructions for causal relations discovery (Causal relations dataset)
[research data]
Yordanova, Kristina
1985-
VerfasserIn
aut
software, multimedia
Computerdaten
Forschungsdaten
gw
2015
monographic
XA-DE
Rostock
University
publisher
2015
Rostock
University Library
distributor
eng
1 Online-Ressource
One aspect of ontology learning methods is the discovery of relations in textual data. One kind of such relations are causal relations. Our aim is to discover causations described in texts such as recipes and manuals. There is a lot of research on causal relations discovery that is based on grammatical patterns. These patterns are, however, rarely discovered in textual instructions (such as recipes) with short and simple sentence structure. Therefore we use time series to discover causal relations. To do that, each word of interest in the text is converted into time series that represent how often and in which time stamp this word appears in the text. Then a time series analysis can be applied to discover causal relations.<eng>
Kristina Yordanova
004
http://purl.uni-rostock.de/rosdok/id00000117
https://doi.org/10.1371/journal.pone.0109381
Frank Krüger, Martin Nyolt, Kristina Yordanova, Albert Hein, and Thomas Kirste. Computational state space models for activity and intention recognition. a feasibility study. PLoS ONE, 9(11):e109381, 11 2014.
Kristina Yordanova. Discovering causal relations in textual instructions. In Recent Advances in Natural Language Processing, Hissar, Bulgaria, September 2015.
10.18453/rosdok_id00000117
1135716588
rda
DE-627
190925
1677718749
20251226T180531.0
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