Since many real-world concepts are associated with colour, for example danger with red, linguistic information is often complimented with the use of appropriate colours in information visualization and product marketing. Yet, there is no comprehensive resource that captures concept-colour associations. We present a method to create a large word-colour association lexicon by crowdsourcing. We focus especially on abstract concepts and emotions to show that even though they cannot be physically visualized, they too tend to have strong colour associations. Finally, we show how word-colour associations manifest themselves in language, and quantify usefulness of co-occurrence and polarity cues in automatically detecting colour associations.
The relation between language and cognition has received considerable attention over the years, mainly on answering whether language impacts thought, and if so, to what extent. Experiments with colour categories have been used both to show that language has an effect on thought @cite_17 @cite_1 and that it does not @cite_14 . However, that line of work does not explicitly deal with word--colour associations. In fact, we did not find any other academic work that gathered large word--colour associations. There is, however, a commercial endeavor---Cymbolism http: www.cymbolism.com about .
@cite_14 @cite_1 @cite_17
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Since many real-world concepts are associated with colour, for example danger with red, linguistic information is often complimented with the use of appropriate colours in information visualization and product marketing. Yet, there is no comprehensive resource that captures concept-colour associations. We present a method to create a large word-colour association lexicon by crowdsourcing. We focus especially on abstract concepts and emotions to show that even though they cannot be physically visualized, they too tend to have strong colour associations. Finally, we show how word-colour associations manifest themselves in language, and quantify usefulness of co-occurrence and polarity cues in automatically detecting colour associations. Abstract Documents in computer-readable form can be used to provide information about other documents, i.e. those they cite. To do this efficiently requires procedures for computer recognition of citing statements. This is not easy, especially for multi-sentence citing statements. Computer recognition procedures have been developed which are accurate to the following extent: 73 of the words in statements selected by computer procedures as being citing statements are words which are correctly attributable to the corresponding documents. The retrieval effectiveness of computer-recognized citing statements was tested in the following way. First, for eight retrieval requests in inorganiic chemistry, average recall by search of Chemical Abstracts Service indexing and Chemical Abstracts abstract text words was found to be 50 . Words from citing statements referring to the papers to be retrieved were then added to the index terms and abstract words as additional access points, and searching was repeated. Average recall increased to 70 . Only words from citing statements published within a year of the cited papers were used. The retrieval effect of citing statement words alone (published within a year) without index or abstract terms was the following: average recall was 40 . When just the words of the titles of the cited papers were added to those citing statement words, average recall increased to 50 . We investigate to what extent bag of visual words models can be used to distinguish categories which have significant visual similarity. To this end we develop and optimize a nearest neighbour classifier architecture, which is evaluated on a very challenging database of flower images. The flower categories are chosen to be indistinguishable on colour alone (for example), and have considerable variation in shape, scale, and viewpoint. We demonstrate that by developing a visual vocabulary that explicitly represents the various aspects (colour, shape, and texture) that distinguish one flower from another, we can overcome the ambiguities that exist between flower categories. The novelty lies in the vocabulary used for each aspect, and how these vocabularies are combined into a final classifier. The various stages of the classifier (vocabulary selection and combination) are each optimized on a validation set. Results are presented on a dataset of 1360 images consisting of 17 flower species. It is shown that excellent performance can be achieved, far surpassing standard baseline algorithms using (for example) colour cues alone.