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 propose an approach that makes use of time series to discover causal relations. We distinguish causal relations from correlation by assuming that one word causes another only if it precedes the second word temporally. To test the approach, we compared the discovered by our approach causal relations to those obtained through grammatical patterns in 20 textual instructions. The results showed that our approach has an average recall of 41% compared to 13% obtained with the grammatical patterns. Furthermore the discovered by the two approaches causal relations are usually disjoint. This indicates that the approach can be combined with grammatical patterns in order to increase the number of causal relations discovered in textual instructions.
Textual instructions however usually have a simple sentence structure where grammatical patterns are rarely discovered REF .
Discovering Causal Relations in Textual Instructions
RANLP
Computer Science