| dc.contributor | IJCAI | en_US |
| dc.contributor | 資科系 | - |
| dc.creator (作者) | H. C. Wang;R. Kumar;C. Roseacute;李蔡彥;C. Y. Chang | en_US |
| dc.creator (作者) | Li, Tsai-Yen | - |
| dc.date (日期) | 2007-01 | en_US |
| dc.date.accessioned | 9-Jan-2009 16:46:06 (UTC+8) | - |
| dc.date.available | 9-Jan-2009 16:46:06 (UTC+8) | - |
| dc.date.issued (上傳時間) | 9-Jan-2009 16:46:06 (UTC+8) | - |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/23818 | - |
| dc.description.abstract (摘要) | We evaluate a new hybrid language processing approach designed for interactive applications that maintain an interaction with users over multiple turns. Specifically, we describe a method for using a simple topic hierarchy in combination with a standard information retrieval measure of semantic similarity to reason about the selection of appropriate feedback in response to extended language inputs in the context of an interactive tutorial system designed to support creative problem solving. Our evaluation demonstrates the value of using a machine learning approach that takes feedback from experts into account for optimizing the hierarchy based feedback selection strategy. | - |
| dc.format | application/ | en_US |
| dc.language | en | en_US |
| dc.language | en-US | en_US |
| dc.language.iso | en_US | - |
| dc.relation (關聯) | Proceedings of International Joint Conference on Artificial Intelligence (IJCAI2007) | en_US |
| dc.title (題名) | Hydrangea: A Hybrid Ontology Directed Feedback Generation Algorithm for Supporting Creative Problem Solving Dialogues | en_US |
| dc.type (資料類型) | conference | en |
