“are written”, “published”, and “read”, they are “by an author”, “from a publisher”, “for a reader”, and “on a subject”, they “have pages” and “chapters”, and they “are in libraries”. When compared with speaker-generated descriptions, the property-based concept representations produced by Strudel showed to be reasonable both quantitatively and qualitatively.

Murphy et al. [-@murphy2012learning] present a technique called Non-Negative Sparse Embedding (NNSE) for learning interpretable distributional semantic models. They define interpretability from the point of view of cognitive plausibility, stating that a word representation is interpretable if each of its dimensions is semantically coherent. They measure this coherence through the *word intrusion detection* task, in which, for each dimension, a set is created containing its top five words and an *intruder* word. The sets are then presented to human evaluators who need to identify the intruder. A high precision in this task means the dimension is interpretable because the human evaluator can easily name the category it is representing and pick out the word that is not a member of this category, i.e., the intruder. One example of such sets is the one composed of the words {“bathroom”, “closet”, “attic”, “balcony”, “quickly”, “toilet”}; here it is easy to name the 