Discovering Causal Relations in Textual Instructions

Proceedings of Recent Advances in Natural Language Processing, pages 714–720, Hissar, Bulgaria, Sep 7–9 2015. Discovering Causal Relations in Textual Instructions Kristina Yordanova University of Rostock kristina.yordanova@uni-rostock.de Abstract One aspect of ontology learning methods is the discovery of relations in textual data. One kind of such relations are causal re- lations. Our aim is to discover causations described in texts such as recipes and man- uals. There is a lot of research on causal relations discovery that is based on gram- matical patterns. These patterns are, how- ever, rarely discovered in textual instruc- tions (such as recipes) with short and sim- ple sentence structure. Therefore we pro- pose an approach that makes use of time series to discover causal relations. We dis- tinguish causal relations from correlation by assuming that one word causes another only if it precedes the second word tempo- rally. To test the approach, we compared the discovered by our approach causal re- lations to those obtained through gram- matical 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 ap- proaches causal relations are usually dis- joint. This indicates that the approach can be combined with grammatical patterns in order to increase the number of causal re- lations discovered in textual instructions. 1 Introduction and Motivation There is an increasing number of approaches and systems for ontology learning based on textual data partially because of the availability of web resources that are easily accessible on the inter- net (Wong et al., 2012). One problem these ap- proaches face is the discovery of relations in the data (Wong et al., 2012). One type of relations are the causal relations between text elements, that is, whether one word or phrase causes another. Most of the research regarding causal relations is cen- tred on the discovery of causal relations between topics (Radinsky et al., 2011; Kim et al., 2012; Li et al., 2010) or based on a large amount of textual data (Silverstein et al., 2000; Mani and Cooper, 2000; Girju, 2003). Moreover, the works usu- ally focus on the discovery of causal relations in rich textual data with complex sentence structure (Silverstein et al., 2000; Mani and Cooper, 2000; Girju, 2003). There is however little research on discovering causal relations in textual instructions that have short sentence length and simple struc- ture (Zhang et al., 2012). This can be explained with the fact that short sentences often do not con- tain any grammatical causal patterns, rather the re- lations are implicitly inferred by the reader. There is a large amount of web instructions available in the form of recipes, manuals, and tutorials1 that contain such simple structures. For example, in the sentence “Add the pork pieces, fry them for 2 minutes.” there is no explicit causal relation be- tween add and fry. However, we implicitly know, that without adding the pork pieces, we cannot fry them. This …

Content truncated to 500 words