Automatic Disambiguation of French Discourse Connectives
This work addresses the need for accurate discourse parsing in French NLP, but it is incremental as it adapts existing methods to a new language.
The paper tackled the problem of disambiguating French discourse connectives between discourse and non-discourse usage, achieving an accuracy of 94.2% by applying features originally developed for English.
Discourse connectives (e.g. however, because) are terms that can explicitly convey a discourse relation within a text. While discourse connectives have been shown to be an effective clue to automatically identify discourse relations, they are not always used to convey such relations, thus they should first be disambiguated between discourse-usage non-discourse-usage. In this paper, we investigate the applicability of features proposed for the disambiguation of English discourse connectives for French. Our results with the French Discourse Treebank (FDTB) show that syntactic and lexical features developed for English texts are as effective for French and allow the disambiguation of French discourse connectives with an accuracy of 94.2%.