Abstract
Analogy is an integral part of human cognition. Consequently researchers would like to produce computational models of analogy to test implementations of theoretical claims. Such computational models have relied almost exclusively upon hand-coded representations, making the resulting models too dependent upon the modeler's choices, and thus difficult to interpret in isolation of the modeler. In an effort to combat this dependency, we present here a means of automatic encoding for the LISA model of analogy and inference.
| Original language | English (US) |
|---|---|
| Pages | 115-122 |
| Number of pages | 8 |
| State | Published - Jan 1 2017 |
| Event | 28th Modern Artificial Intelligence and Cognitive Science Conference, MAICS 2017 - Fort Wayne, United States Duration: Apr 28 2017 → Apr 29 2017 |
Other
| Other | 28th Modern Artificial Intelligence and Cognitive Science Conference, MAICS 2017 |
|---|---|
| Country | United States |
| City | Fort Wayne |
| Period | 4/28/17 → 4/29/17 |
Keywords
- Analogy
- Cognitive Psychology
- Computational Linguistics
- Natural Language Processing
ASJC Scopus subject areas
- Software
- Artificial Intelligence
Cite this
Wilner, S., & Hummel, J. E. (2017). Automating the encoding for the LISA model of analogy from raw text. 115-122. Paper presented at 28th Modern Artificial Intelligence and Cognitive Science Conference, MAICS 2017, Fort Wayne, United States.