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

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 catego...

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Bibliographic Details
Published in:PloS one 2018-12, Vol.13 (12), p.e0209449-e0209449
Main Authors: Cassani, Giovanni, Grimm, Robert, Daelemans, Walter, Gillis, Steven
Format: Article
Language:English
Subjects:
Algorithms
Biology and Life Sciences
Child
Child-directed speech
Children & youth
Classification
Cognition & reasoning
Conditional probability
Experimental psychology
Humans
Hypotheses
Language acquisition
Language Development
Learning - physiology
Linguistics
Models, Psychological
Neurosciences
Parts of speech
People and Places
Phonology
Physical Sciences
Physiological aspects
Regression analysis
Research and Analysis Methods
Semantics
Social Sciences
Speech - physiology
Speech perception
Studies
Uncertainty
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0209449