Extracting cause-effect relations in Natural Language Text

The extraction of causal relations from English sentences is an important step for the improvement of many Natural Language Processing applications such as question answering, document summarization and, in particular, it enables the possibility to reason about the detected events. The automatic extraction of causal relations is also a very difficult task because the English presents some hard problems for the detection of causal relation. Indeed, there are few explicit lexico-syntactic patterns that are in exact correspondence with a causal relation while there is a huge number of cases that can evoke a causal relation not in a uniquely way. Most of the existing approaches for discovering causal relations are centered on the extraction of pairs of words without discriminating from causes and effects and, mainly, are focused on particular application domains. In this talk we will present a brief overview of the existing works on this topic and a novel approach which combines rule based and Machine Learning methodologies in order to identify in a sentence a set of word pairs that are in cause-effect relation. The rules are based on the relations in the dependency tree of the sentence and are supported by a set of lexico-syntactic patterns to detect the sentences that contain causal relations. The result of the rules is filtered using a statistical classifier trained with lexical, semantic and dependency features. The performances of our method on an independent domain testset will be discussed and compared with the ones of other existing methods.