The Vocabulary Problem in Human-System Communication

The Vocabulary Problem in Human-System Communication

November 1987 Volume 30 Number 11 | G. W. FURNAS, T. K. LANDAUER, L. M. GOMEZ, and S. T. DUMAIS
The paper "The Vocabulary Problem in Human-System Communication" by G. W. Furnas, T. K. Landauer, L. M. Gomez, and S. T. Duma addresses the issue of users' spontaneous word choice in computer applications, particularly in command entry and information retrieval. The authors found that the variability in word usage is surprisingly large, with two people favoring the same term with a probability less than 0.20. This variability limits the success of vocabulary-driven interaction design methodologies, such as using a single designer's favorite word, which can result in 80-90% failure rates. The paper proposes an optimal strategy of unlimited aliasing, where multiple access terms are provided for each object, to improve success rates by several folds. The authors conducted experiments and simulations to demonstrate the effectiveness of this approach, showing that it can achieve hit rates of 50-100% in the first three guesses for untutored queries. The paper also discusses the precision problem and suggests disambiguation methods to resolve it. Finally, it highlights the practicality and potential of adaptive indexing systems that can collect and use user-provided aliases on-site.The paper "The Vocabulary Problem in Human-System Communication" by G. W. Furnas, T. K. Landauer, L. M. Gomez, and S. T. Duma addresses the issue of users' spontaneous word choice in computer applications, particularly in command entry and information retrieval. The authors found that the variability in word usage is surprisingly large, with two people favoring the same term with a probability less than 0.20. This variability limits the success of vocabulary-driven interaction design methodologies, such as using a single designer's favorite word, which can result in 80-90% failure rates. The paper proposes an optimal strategy of unlimited aliasing, where multiple access terms are provided for each object, to improve success rates by several folds. The authors conducted experiments and simulations to demonstrate the effectiveness of this approach, showing that it can achieve hit rates of 50-100% in the first three guesses for untutored queries. The paper also discusses the precision problem and suggests disambiguation methods to resolve it. Finally, it highlights the practicality and potential of adaptive indexing systems that can collect and use user-provided aliases on-site.
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