Abstract
We report results of experiments which build and refine models of rhetorical-semantic relations such as Cause and Contrast. We adopt the approach of Marcu and Echihabi (2002), using a small set of patterns to build relation models, and extend their work by refining the training and classification process using parameter optimization, topic segmentation and syntactic parsing. Using human-annotated and automatically-extracted test sets, we find that each of these techniques results in improved relation classification accuracy.
| Original language | English |
|---|---|
| Pages | 428-435 |
| Number of pages | 8 |
| State | Published - 2007 |
| Event | Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics, NAACL HLT 2007 - Rochester, NY, United States Duration: Apr 22 2007 → Apr 27 2007 |
Conference
| Conference | Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics, NAACL HLT 2007 |
|---|---|
| Country/Territory | United States |
| City | Rochester, NY |
| Period | 04/22/07 → 04/27/07 |
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