Reference Entry10.1093/oxfordhb/9780198845003.013.10
Children’s Use of Syntax In Word Learning
Jeffrey Lidz
- 07 Jan 2022
pp 355-377
3
TL;DR: The authors investigated the role of syntax in guiding the acquisition of word meaning and found that children can use the syntactic distribution of a word as evidence for its meaning and discuss the principles of grammar that license such inferences.
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Abstract: This chapter investigates the role that syntax plays in guiding the acquisition of word meaning. It reviews data that reveal how children can use the syntactic distribution of a word as evidence for its meaning and discusses the principles of grammar that license such inferences. We delineate the role of thematic linking generalizations in the acquisition of action verbs, arguing that children use specific links between subject and agent and between object and patient to guide initial verb learning. In the domain of attitude verbs, we show that children’s knowledge of abstract links between subclasses of attitude verbs and their syntactic distribution enable learners to identify the meanings of their initial attitude verbs, such as think and want. Finally, we show that syntactic bootstrapping effects are not limited to verb learning but extend across the lexicon.
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Citations
Stochastic LLMs do not Understand Language: Towards Symbolic, Explainable and Ontologically Based LLMs
TL;DR: This paper suggests in this paper applying the effective bottom-up strategy in a symbolic setting resulting in symbolic, explainable, and ontologically grounded language models.
Lexicalization in the developing parser
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TL;DR: The authors showed that children encode the syntactic distributions of particular verbs and use those distributions to make predictions, but they do not assume that these can be used for verbs in general, and they take away children's ability to rely on lexically specific knowledge, as in the current study.
Stochastic LLMs do not Understand Language: Towards Symbolic, Explainable and Ontologically Based LLMs
Walid S. Saba
TL;DR: LLMs lack factual accuracy, subsymbolic nature and fail to make correct inferences in various linguistic contexts. Symbolic, explainable and ontologically grounded LLMs are proposed as an alternative.
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