A Computational Model for Taxonomy-Based Word Learning Inspired by Infant Developmental Word Acquisition

2005 ◽  
Vol E88-D (10) ◽  
pp. 2389-2398 ◽  
Author(s):  
A. TOYOMURA
2009 ◽  
Vol 20 (5) ◽  
pp. 578-585 ◽  
Author(s):  
Michael C. Frank ◽  
Noah D. Goodman ◽  
Joshua B. Tenenbaum

Word learning is a “chicken and egg” problem. If a child could understand speakers' utterances, it would be easy to learn the meanings of individual words, and once a child knows what many words mean, it is easy to infer speakers' intended meanings. To the beginning learner, however, both individual word meanings and speakers' intentions are unknown. We describe a computational model of word learning that solves these two inference problems in parallel, rather than relying exclusively on either the inferred meanings of utterances or cross-situational word-meaning associations. We tested our model using annotated corpus data and found that it inferred pairings between words and object concepts with higher precision than comparison models. Moreover, as the result of making probabilistic inferences about speakers' intentions, our model explains a variety of behavioral phenomena described in the word-learning literature. These phenomena include mutual exclusivity, one-trial learning, cross-situational learning, the role of words in object individuation, and the use of inferred intentions to disambiguate reference.


2009 ◽  
Vol 364 (1536) ◽  
pp. 3755-3771 ◽  
Author(s):  
Prahlad Gupta ◽  
Jamie Tisdale

Word learning is studied in a multitude of ways, and it is often not clear what the relationship is between different phenomena. In this article, we begin by outlining a very simple functional framework that despite its simplicity can serve as a useful organizing scheme for thinking about various types of studies of word learning. We then review a number of themes that in recent years have emerged as important topics in the study of word learning, and relate them to the functional framework, noting nevertheless that these topics have tended to be somewhat separate areas of study. In the third part of the article, we describe a recent computational model and discuss how it offers a framework that can integrate and relate these various topics in word learning to each other. We conclude that issues that have typically been studied as separate topics can perhaps more fruitfully be thought of as closely integrated, with the present framework offering several suggestions about the nature of such integration.


2005 ◽  
Vol 36 (1) ◽  
pp. 5-16 ◽  
Author(s):  
Tim Brackenbury ◽  
Clifton Pye

Children with language impairments demonstrate a broad range of semantic difficulties, including problems with new word acquisition, storage and organization of known words, and lexical access/retrieval. Unfortunately, assessments of children’s semantic skills are often limited to measures of receptive and expressive vocabulary size. As a result, the semantic deficits of these children may not receive the attention they need. This article explores the word-learning, lexical storage, and lexical access skills of children with language impairments and the theories that account for their performance. Our review culminates with specific recommendations for speech-language pathologists to improve the breadth of their semantic assessments.


2012 ◽  
Vol 3 ◽  
Author(s):  
Afra Alishahi ◽  
Afsaneh Fazly ◽  
Judith Koehne ◽  
Matthew W. Crocker

2010 ◽  
Vol 34 (6) ◽  
pp. 1017-1063 ◽  
Author(s):  
Afsaneh Fazly ◽  
Afra Alishahi ◽  
Suzanne Stevenson

2021 ◽  
pp. 1-16
Author(s):  
Melissa RAJARAM

Abstract Multisyllabic words constitute a large portion of children's vocabulary. However, the relationship between phonological neighborhood density and English multisyllabic word learning is poorly understood. We examine this link in three, four and six year old children using a corpus-based approach. While we were able to replicate the well-accepted positive association between CVC word acquisition and neighborhood density, no similar relationship was found for multisyllabic words, despite testing multiple novel neighborhood measures. This finding raises the intriguing possibility that phonological organization of the mental lexicon may play a fundamentally different role in the acquisition of more complex words.


2021 ◽  
Author(s):  
Rachel Ka Ying Tsui ◽  
Ana Maria Gonzalez-Barrero ◽  
Esther Schott ◽  
Krista Byers-Heinlein

The acquisition of translation equivalents is often considered a special component of bilingual children’s vocabulary development, as bilinguals have to learn words that share the same meaning across their two languages. This study examined three contrasting accounts for bilingual children’s acquisition of translation equivalents relative to words that are first labels for a referent: the Avoidance Account whereby translation equivalents are harder to learn, the Preference Account whereby translation equivalents are easier to learn, and the Neutral Account whereby translation equivalents are similar to learn. To adjudicate between these accounts, Study 1 explored patterns of translation equivalent learning under a novel computational model — the Bilingual Vocabulary Model — which quantifies translation equivalent knowledge as a function of the probability of learning words in each language. Study 2 tested model-derived predictions against vocabulary data from 200 French–English bilingual children aged 18–33 months. Results showed a close match between the model predictions and bilingual children’s patterns of translation equivalent learning. At smaller vocabulary sizes, data matched the Preference Account, while at larger vocabulary sizes they matched the Neutral Account. Our findings show that patterns of translation equivalent learning emerge predictably from the word learning process, and reveal a qualitative shift in translation equivalent learning as bilingual children develop and learn more words.


2006 ◽  
Vol 27 (4) ◽  
pp. 564-568 ◽  
Author(s):  
Prahlad Gupta

The proposals that (a) nonword repetition and word learning both rely on phonological storage and (b) both are multiply determined are two of the major foci of Gathercole's (2006) Keynote Article, which marshals considerable evidence in support of each. In my view, the importance of these proposals cannot be overstated: these two notions go to the heart of the relationship between nonword repetition and word learning. Indeed, they figure prominently in the approach that my colleagues and I have taken to studying that relationship (e.g., Gupta, 2006; Gupta, Lipinski, Abbs, & Lin, 2005; Gupta & MacWhinney, 1997). An important aspect of our approach has been the attempt to construct a computational model that can simulate performance in a nonword repetition task and in a word learning task, the rationale being that a computational model that achieved this would constitute a proposal about the processing mechanisms that may underlie the relationship. In this Commentary, I describe how our computational work offers a concrete way of thinking about how nonword repetition and word learning may rely on phonological storage, and about how these abilities may be multiply determined. Such computational work is, I suggest, a valuable tool in further investigating the important relationship that has been revealed by Gathercole's influential work, and that is analyzed in the Keynote Article.


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