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A Computational Model for Child Inferences of Word Meanings via Syntactic Categories for Different Ages and Languages
IEEE Transactions on Cognitive and Developmental Systems ( IF 5 ) Pub Date : 2020-09-01 , DOI: 10.1109/tcds.2018.2883048
Yuji Kawai , Yuji Oshima , Yuki Sasamoto , Yukie Nagai , Minoru Asada

Children exploit their morphosyntactic knowledge in order to infer the meanings of words. A recent behavioral study has reported developmental changes in word learning from three to five years of age, with respect to a child’s native language. To understand the computational basis of this phenomenon, we propose a model based on a hidden Markov model (HMM). The HMM acquires syntactic categories of given words as its hidden states, which are associated with observed features. Then, the model infers the syntactic category of a new word, which facilitates the selection of an appropriate visual feature. We hypothesize that using this model with different numbers of categories can replicate the manner in which children of different ages learn words. We perform simulation experiments in three native language environments (English, Japanese, and Chinese), which demonstrate that the model produces similar performances as the children in each environment. Allowing a larger number of categories means that the model can acquire a sufficient number of obvious categories, which results in the successful inference of visual features for novel words. In addition, cross-linguistic differences originating from the acquisition of language-specific syntactic categories are identified, i.e., the syntactic categories learned from English and Chinese corpora are relatively reliant on word orders, whereas the Japanese-trained model exploits morphological cues to infer the syntactic categories.

中文翻译:

儿童通过不同年龄和语言的句法类别推断词义的计算模型

儿童利用他们的形态句法知识来推断单词的含义。最近的一项行为研究报告了从 3 岁到 5 岁,就儿童的母语而言,单词学习的发展变化。为了理解这种现象的计算基础,我们提出了一个基于隐马尔可夫模型 (HMM) 的模型。HMM 获取给定单词的句法类别作为其隐藏状态,这些状态与观察到的特征相关联。然后,该模型推断新词的句法类别,这有助于选择合适的视觉特征。我们假设使用具有不同类别数量的模型可以复制不同年龄儿童学习单词的方式。我们在三种母语环境(英语、日语和中文)中进行模拟实验,这表明该模型在每个环境中产生与儿童相似的表现。允许更多的类别意味着模型可以获得足够数量的明显类别,从而成功推断出新词的视觉特征。此外,识别了源自语言特定句法类别习得的跨语言差异,即从英汉语料库中学习的句法类别相对依赖于词序,而日语训练的模型则利用形态线索来推断词序。句法范畴。这导致成功推断新词的视觉特征。此外,识别了源自语言特定句法类别习得的跨语言差异,即从英汉语料库中学习的句法类别相对依赖于词序,而日语训练的模型则利用形态线索来推断词序。句法范畴。这导致成功推断新词的视觉特征。此外,识别了源自语言特定句法类别习得的跨语言差异,即从英汉语料库中学习的句法类别相对依赖于词序,而日语训练的模型则利用形态线索来推断词序。句法范畴。
更新日期:2020-09-01
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