Abstract
In this paper, we examine the impact of employing contextual, structural information from a tree-structured document set to derive a language model. Our results show that this information significantly improves the accuracy of the resultant model.
| Original language | English |
|---|---|
| Title of host publication | The 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL HLT 2016) |
| Subtitle of host publication | Proceedings of the Conference, June 12-17, 2016, San Diego, California, USA |
| Editors | Ani Nenkova, Owen Rambow |
| Place of Publication | Stroudsburg, PA |
| Publisher | Association for Computational Linguistics (ACL) |
| Pages | 762-766 |
| Number of pages | 5 |
| ISBN (Print) | 9781941643914 |
| Publication status | Published - 2016 |
| Event | North American Association for Computational Linguistics 2016 - Sheraton San Diego Hotel & Marina, San Diego, United States of America Duration: 12 Jun 2016 → 17 Jun 2016 Conference number: 15th http://naacl.org/naacl-hlt-2016/ |
Conference
| Conference | North American Association for Computational Linguistics 2016 |
|---|---|
| Abbreviated title | NAACL HLT 2016 |
| Country/Territory | United States of America |
| City | San Diego |
| Period | 12/06/16 → 17/06/16 |
| Internet address |