The vocabulary problem in human-system communication

By G. Furnas, T. Landauer, L. Gomez, Susan T. Dumais. 1987

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In Human-Computer Interaction

Citations in the corpus

Open-vocabulary Object Retrieval (2014, background): “Clearly, we need visual recognition methods which accommodate the full range of natural language and situationspecific lexical biases when resolving users’ references to objects [21].”
Concept-Based Information Retrieval Using Explicit Semantic Analysis (2011, background): “Thus, these nonexpert users were increasingly faced with what was described as the vocabulary problem [Furnas et al. 1987].”
Semantic Matching in Search (2014, background): “, on average 80-90% of the times, two people will name the same concept with different representations [67].”
Disfluency in Swedish human–human and human–machine travel booking dialogues (2004, background): “Furnas et al., 1987). Consequently, it is crucial to present the task to the users/subjects in ways that do not affect the linguistic behavior of the subjects in unwanted ways. It has been shown that written instructions tend to govern the linguistic behavior of the users (MacDermid & Goldstein 1996”
Construct Overlap Between Employee Engagement and Job Satisfaction: A Function of Semantic Equivalence? (2015, background): “…excluding noncontent words may affect LSA results as they may help inform the latent semantic structure of a corpus of text (cf., Bellegarda 1998; Rehder et al. 1998) Construct Overlap Between Employee Engagement and Job… 1153 meanings than searchers tried or indexers indexed (Furnas et al. 1987).”
Can social bookmarking improve web search? (2008, background): “This leads to the well studied “vocabulary problem” (see [8], [3]), whereby users have many different types of terminology for the same resources.”
Probabilistic query expansion using query logs (2002, background): “[4] Deerwester, S., Dumai, S.T., Furnas, G.W., Landauer, T.K. and Harshman, R. 1990.”
Human computing and machine understanding of human behavior: a survey (2006, background): “In contrast to the linguistic part of a spoken message (what has been said) [20], the nonlinguistic part of it (how it has been said) carries important information about the speaker’s affective state [29] and attitude [48].”
A Deep Look into Neural Ranking Models for Information Retrieval (2019, background): “Such heterogeneity leads to the critical vocabulary mismatch problem [44, 45].”
A Survey of Automatic Query Expansion in Information Retrieval (2012, background): “In Proceedings of the Methods and Applications of Semantic Indexing Workshop at the 7th International Conference on Terminology and Knowledge Engineering.”
HT06, tagging paper, taxonomy, Flickr, academic article, to read (2006, background): “These systems may offer a way to overcome the Vocabulary Problem – first articulated by George Furnas et al in [8] – where different users use different terms to describe the same things (or actions).”