Affiliations: [a] Faculty of Computer Science and Software Engineering, Innopolis University, st. Universitetskaya, 1, Innopolis, Republic of Tatarstan, Russian Federation
| [b] Linguistic Research and Education Center, Research Laboratory ‘Intellectual Technologies of Text Management’, Kazan Federal University, 2, Kazan, the Republic of Tatarstan, Russian Federation
Correspondence:
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Corresponding author. Vladimir Ivanov, Faculty of Computer Science and Software Engineering, Innopolis University, st. Universitetskaya, 1, Innopolis, Republic of Tatarstan, 420500, Russian Federation. E-mail: [email protected].
Abstract: Creation of dictionaries of abstract and concrete words is a well-known task. Such dictionaries are important in several applications of text analysis and computational linguistics. Usually, the process of assembling of concreteness scores for words begins with a lot of manual work. However, the process can be automated significantly using information from large corpora. In this paper we combine two datasets: a dictionary with concreteness scores of 40,000 English words and the GoogleBooks Ngram dataset, in order to test the following hypothesis: in text concrete words tend to occur with more concrete words, than with abstract words (and inverse: abstract words tend to occur with more abstract words, than with concrete words). Using the hypothesis, we proposed a method for automatic evaluation concreteness scores of words using a small amount of initial markup.
Keywords: Concreteness of words, bigrams, dictionary
