Automatic Mapping of French Discourse Connectives to PDTB Discourse Relations
This work addresses the need for automated discourse analysis tools in French natural language processing, though it is incremental as it builds on existing translation methods.
The paper tackled the problem of automatically mapping French discourse connectives to PDTB discourse relations by exploiting phrase tables from statistical machine translation, resulting in the creation of ConcoLeDisCo, a lexicon that achieved a recall of 0.81 and an Average Precision of 0.68 for Concession and Condition relations when evaluated against LEXCONN.
In this paper, we present an approach to exploit phrase tables generated by statistical machine translation in order to map French discourse connectives to discourse relations. Using this approach, we created ConcoLeDisCo, a lexicon of French discourse connectives and their PDTB relations. When evaluated against LEXCONN, ConcoLeDisCo achieves a recall of 0.81 and an Average Precision of 0.68 for the Concession and Condition relations.