Lexical category acquisition is facilitated by uncertainty in distributional co-occurrences

Giovanni Cassani, Robert Grimm, Walter Daelemans, Steven Gillis

Research output: Contribution to journalArticleScientificpeer-review

Abstract

This paper analyzes distributional properties that facilitate the categorization of words into lexical categories. First, word-context co-occurrence counts were collected using corpora of transcribed English child-directed speech. Then, an unsupervised k-nearest neighbor algorithm was used to categorize words into lexical categories. The categorization outcome was regressed over three main distributional predictors computed for each word, including frequency, contextual diversity, and average conditional probability given all the co-occurring contexts. Results show that both contextual diversity and frequency have a positive effect while the average conditional probability has a negative effect. This indicates that words are easier to categorize in the face of uncertainty: categorization works best for words which are frequent, diverse, and hard to predict given the co-occurring contexts. This shows how, in order for the learner to see an opportunity to form a category, there needs to be a certain degree of uncertainty in the co-occurrence pattern.
Original languageEnglish
Article numbere0209449
Pages (from-to)1-36
Number of pages36
JournalPLoS ONE
Volume13
Issue number12
DOIs
Publication statusPublished - 2018
Externally publishedYes

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