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Ranking concrete and abstract words using Google Books Ngram data

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dc.contributor.author Ivanov V.
dc.contributor.author Solovyev V.
dc.date.accessioned 2021-02-25T20:37:42Z
dc.date.available 2021-02-25T20:37:42Z
dc.date.issued 2020
dc.identifier.issn 1064-1246
dc.identifier.uri https://dspace.kpfu.ru/xmlui/handle/net/162083
dc.description.abstract © 2020 - IOS Press and the authors. All rights reserved. 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.
dc.relation.ispartofseries Journal of Intelligent and Fuzzy Systems
dc.subject bigrams
dc.subject Concreteness of words
dc.subject dictionary
dc.title Ranking concrete and abstract words using Google Books Ngram data
dc.type Article
dc.relation.ispartofseries-issue 2
dc.relation.ispartofseries-volume 39
dc.collection Публикации сотрудников КФУ
dc.relation.startpage 2229
dc.source.id SCOPUS10641246-2020-39-2-SID85091090630

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  • Публикации сотрудников КФУ Scopus [22633]
    Коллекция содержит публикации сотрудников Казанского федерального (до 2010 года Казанского государственного) университета, проиндексированные в БД Scopus, начиная с 1970г.

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