A Hybrid Ontology Directed Feedback Selection Algorithm for Supporting Creative Problem Solving Dialogues
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.
Cite
Text
Wang et al. "A Hybrid Ontology Directed Feedback Selection Algorithm for Supporting Creative Problem Solving Dialogues." International Joint Conference on Artificial Intelligence, 2007.Markdown
[Wang et al. "A Hybrid Ontology Directed Feedback Selection Algorithm for Supporting Creative Problem Solving Dialogues." International Joint Conference on Artificial Intelligence, 2007.](https://mlanthology.org/ijcai/2007/wang2007ijcai-hybrid/)BibTeX
@inproceedings{wang2007ijcai-hybrid,
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 = {International Joint Conference on Artificial Intelligence},
year = {2007},
pages = {1750-1755},
url = {https://mlanthology.org/ijcai/2007/wang2007ijcai-hybrid/}
}