Abstract
This article addresses the causal structure of events described by verbs: whether an event happens spontaneously or it is caused by an external causer. We automatically estimate the likelihood of external causation of events based on the distribution of causative and anticausative uses of verbs in the causative alternation. We train a Bayesian model and test it on a monolingual and on a bilingual input. The performance is evaluated against an independent scale of likelihood of external causation based on typological data. The accuracy of a two-way classification is 85% in both monolingual and bilingual setting. On the task of a three-way classification, the score is 61% in the monolingual setting and 69% in the bilingual setting.
Highlights
Present in human thinking, causality is encoded in language in various ways
We automatically estimate the likelihood of external causation of events based on the distribution of causative and anticausative uses of verbs in the causative alternation
Computational approaches to causality are mostly concerned with automatic extraction of causal schemata (Michotte, 1963; Tversky and Kahneman, 1982; Gilovich et al, 1985) from spontaneously produced texts based on linguistic encoding
Summary
Present in human thinking, causality is encoded in language in various ways. Computational approaches to causality are mostly concerned with automatic extraction of causal schemata (Michotte, 1963; Tversky and Kahneman, 1982; Gilovich et al, 1985) from spontaneously produced texts based on linguistic encoding. A key to success in this endeavour is understanding how human language encodes causality. Linguistic expressions of causality, such as causative conjunctions, verbs, morphemes, and constructions, are highly ambiguous, encoding the real-world causality, and the structure of discourse, as well as speakers’ attitudes (Moeschler, 2011; Zufferey, 2012). Causality judgements are hard to elicit in an annotation project. This results in a low inter-annotator agreement and makes the evaluation of automatic systems difficult (Bethard, 2007; Grivaz, 2012)
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