利用自然语言处理加深对亲子互动过程和语言发展的理解

IF 1.7 3区 社会学 Q2 FAMILY STUDIES
Wonkyung Jang, Diane Horm, Kyong-Ah Kwon, Kun Lu, Ryan Kasak, Ji Hwan Park
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引用次数: 0

摘要

本研究旨在利用现代机器学习和自然语言处理算法分析亲子互动的细粒度过程。虽然许多研究使用音频样本来预测儿童的语言发展,但他们主要关注的是语言暴露的频率,而不是复杂的语义关系以及语境和学习者变异的影响。方法本研究使用语义网络算法,考察父母在用餐时间和玩玩具时进行语义相关对话时,儿童是否表现出更大的句法发展。此外,该研究还利用话题建模和词嵌入算法研究了玩具玩过程中会话话题的性别差异。来自语言和读写能力发展的家庭-学校研究语料库的数据,重点分析了62名儿童的子集。结果用餐时间语义网络聚类系数与幼儿句法发展呈正相关。此外,来自变形金刚和Word2Vec算法的双向编码器表示表明,男孩和女孩在玩玩具时具有不同的会话焦点,男孩更倾向于动作动词和身体活动,而女孩更倾向于社交和关系主题。这些发现强调了将语义相关的对话纳入日常生活以支持儿童句法发展的重要性。他们还强调需要有针对性的干预措施,考虑到亲子互动中的背景和性别差异。未来的研究应该利用人工智能(AI)驱动的语言处理来完善干预措施,加强家长的参与,并为促进公平的早期语言学习的政策提供信息。结论用餐时的语义相关对话显著促进了儿童的句法发展,玩具游戏时对话内容的性别差异反映了不同的语言关注点。这项研究证实并扩展了现有的文献,表明人工智能驱动的措施可以提供对儿童语言学习环境更细致和细致的理解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Leveraging natural language processing to deepen understanding of parent–child interaction processes and language development

Objective

The current study aimed to analyze the fine-grained processes of parent–child interactions using modern machine learning and natural language processing algorithms.

Background

Although many studies have used audio samples to predict children's language development, they have primarily focused on the frequency of language exposure rather than complex semantic relationships and the effects of context and learner variability.

Method

This study examined whether children exhibit greater syntactic development when parents engage in semantically relevant conversations during mealtime and toy play, using semantic network algorithms. Additionally, it investigated gender differences in conversational topics during toy play using topic modeling and word embedding algorithms. Data from the Home-School Study of Language and Literacy Development Corpus, focusing on a subset of 62 children, were analyzed.

Results

Key findings revealed the clustering coefficient for semantic networks during mealtime was positively associated with children's syntactic development. Furthermore, Bidirectional Encoder Representations from Transformers and Word2Vec algorithms showed that boys and girls had different conversational focuses during toy play, with boys gravitating toward action verbs and physical activities, and girls toward social and relational themes.

Implications

These findings highlight the importance of incorporating semantically relevant conversations into daily routines to support children's syntactic development. They also emphasize the need for tailored interventions that consider context and gender differences in parent–child interactions. Future research should leverage artificial intelligence (AI)-driven language processing to refine interventions, strengthen parent engagement, and inform policies that promote equitable early language learning.

Conclusion

Semantically relevant conversations during mealtime significantly enhanced children's syntactic development, and gender differences in conversational content during toy play reflected distinct linguistic focuses. This study confirms and extends existing literature, suggesting that AI-driven measures could provide a more granular and nuanced understanding of children's language learning environments.

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来源期刊
Family Relations
Family Relations Multiple-
CiteScore
3.40
自引率
13.60%
发文量
164
期刊介绍: A premier, applied journal of family studies, Family Relations is mandatory reading for family scholars and all professionals who work with families, including: family practitioners, educators, marriage and family therapists, researchers, and social policy specialists. The journal"s content emphasizes family research with implications for intervention, education, and public policy, always publishing original, innovative and interdisciplinary works with specific recommendations for practice.
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