A Linguistically-Based Approach to Detect Causality Relations in Unrestricted Text

We present an unsupervised linguistically-based approach to discourse relations recognition,  which uses publicly available resources like manually annotated corpora (Discourse Graph  Bank, Penn Discourse TreeBank, RST-DT), as well as empirically derived data from “causally” annotated lexica like LCS, to produce a rule-based algorithm. In our approach we use  the subdivision of Discourse Relations into four subsets – CONTRAST, CAUSE, CONDITION,  ELABORATION, proposed by [1] in their paper where they report results obtained with a  machine-learning approach from a similar experiment against which we compare our results.  Our approach is fully symbolic and is partially derived from the system called GETARUNS,  for text understanding, adapted to a specific task: recognition of Discourse Causal Relations  in free text. We show that in order to achieve better accuracy both in the general task and in  the specific one, semantic information needs to be used besides syntactic structural information. Our approach outperforms results reported in previous papers [2].