Improving Discourse Relation Projection to Build Discourse Annotated Corpora
Table 4: Precision (P) and recall (R) of the four ConcoDisco and the Naive-Grow-diag corpora against the CrowdFlower gold-standard dataset for DU/NDU labels and overall (OA).
['[BOLD] Corpus', '[BOLD] DU  [BOLD] P', '[BOLD] DU  [BOLD] R', '[BOLD] NDU  [BOLD] P', '[BOLD] NDU  [BOLD] R', '[BOLD] OA  [BOLD] P', '[BOLD] OA  [BOLD] R [head to column names]corpora-acc.csv']
To evaluate the performance of the four word-alignment models in the identification of the English translation of French candidate DCs, we compared the corpora generated by the models against the CrowdFlower gold-standard dataset. For example, while the ConcoDisco-Intersection achieves a higher overall precision than the Naive-Grow-diag (0.914 versus 0.815), its recall is lower (0.845 versus 0.955). Because the ConcoDisco corpora are much larger than existing discourse corpora (with around 5 million annotations), a higher precision is preferable in our case.