Linguistic estimations of human attributes

Qualitative linguistic data provides unique, valuable information that can only come from human observers. Data fusion systems find it challenging to incorporate this "soft data" as they are primarily designed to analyze quantitative, hard-sensor data with consistent formats and qualified error characteristics. This research investigates how people produce linguistic descriptions of human physical attributes. Thirty participants were asked to describe seven actors' ages, heights, and weights in two naturalistic video scenes, using both numeric estimates and linguistic descriptors. Results showed that not only were a large number of linguistic descriptors used, but they were also used inconsistently. Only 10% of the 189 unique terms produced were used by four or more participants. Especially for height and weight, we found that linguistic terms are poor devices for transmitting estimated values due to the large and overlapping ranges of numeric estimates associated with each term. Future work should attempt to better define the boundaries of inclusion for more frequently used terms and to create a controlled language lexicon to gauge whether or not that improves the precision of natural language terms.