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Description: Many languages lack devoted odor vocabularies, and no wide-ranging system of odor descriptions is agreed upon. Many prior studies of odor descriptions used pre-selected descriptors and odor ratings, making them unsuitable for understanding odor vocabulary in natural language. We present a data-driven approach that automatically identifies odor descriptors in English based on their degree of olfactory association, and derive their semantic organization from their distributions in natural texts, using a distributional-semantic word embedding model. We identify 243 descriptors that are much more strongly associated with olfaction than English words in general. We then derive the semantic organization of these olfactory descriptors, and find that it is captured by four clusters that we name Offensive, Malodorous, Fragrant and Edible. The semantic space derived from our model primarily differentiates descriptors in terms of pleasantness and edibility along which our four clusters are positioned, and is similar to a space derived from perceptual data. The semantic organization of odor vocabulary can thus be mapped using natural language data (e.g. online text), without the limitations of odor-perceptual data and pre-selected descriptors. Our method may thus facilitate research on olfaction, a sensory system known to often elude verbal description.