A Hybrid Ontology Directed Feedback Selection Algorithm for Supporting Creative Problem Solving Dialogues
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 1750-1754.
Abstract
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.
@inproceedings{ijcai2007p283,
title = {A Hybrid Ontology Directed Feedback Selection Algorithm for Supporting Creative Problem Solving Dialogues},
author = {Wang, Hao-Chuan and Kumar, Rohit and Rosé, Carolyn Penstein and Li, Tsai-Yen and Chang, Chun-Yen},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {1750--1754},
year = {2007},
url = {https://ijcai.org/proceedings/2007/283},
}
TY - CPAPER TI - A Hybrid Ontology Directed Feedback Selection Algorithm for Supporting Creative Problem Solving Dialogues AU - Wang, Hao-Chuan AU - Kumar, Rohit AU - Rosé, Carolyn Penstein AU - Li, Tsai-Yen AU - Chang, Chun-Yen T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 1750 EP - 1754 UR - https://ijcai.org/proceedings/2007/283 ER -Copied
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