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Related Experiment Video

Updated: May 23, 2026

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Published on: December 11, 2016

Utility-based generation of referring expressions.

¹School of Informatics, Informatics Forum, University of Edinburgh, 10 Crichton Street, Edinburgh EH8 9AB, Scotland. m.guhe@ed.ac.uk

Topics in Cognitive Science

|April 13, 2012

Summary

This study models how people create descriptions in a task-oriented dialogue system (iMAP). A new model, adapted from an existing algorithm, better predicts human language use by focusing on feature utility.

Related Experiment Videos

Last Updated: May 23, 2026

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Published on: December 11, 2016

Area of Science:

Cognitive Science
Computational Linguistics
Artificial Intelligence

Background:

Referring expression generation is crucial for human-computer interaction in task-oriented dialogue systems.
Existing models, like the incremental algorithm, have limitations in explaining human language production.
The iMAP task presents unique challenges, such as unreliable color features for identifying referents.

Purpose of the Study:

To develop and evaluate two cognitive models for generating referring expressions in the iMAP task.
To compare a modified incremental algorithm with a feature-utility-based template model.
To understand how cognitive models can capture human adaptation in language use.

Main Methods:

Development of a general cognitive model based on Dale and Reiter's incremental algorithm, enhanced with feedback adaptation.
Development of a specialized template model prioritizing feature utility within the iMAP task context.
Comparison of model predictions against observed human behavior in the iMAP task.

Main Results:

The template model showed a higher correlation with empirical data compared to the adapted incremental model.
The adapted incremental model overpredicted distinguishing expressions and underpredicted overspecified ones.
Both models successfully predicted the observed decrease in color term usage and increase in useful feature term usage.

Conclusions:

A feature-utility-driven approach, as implemented in the template model, better simulates human referring expression production in the iMAP task.
Cognitive models need to account for task-specific properties and adaptive learning to accurately reflect human language behavior.
The findings have implications for designing more natural and effective human-computer dialogue systems.