Predicting Age of Acquisition for Children's Early Vocabulary in Five Languages Using Language Model Surprisal

可预测性 具体性 购置年龄 词汇 计算机科学 按频率列出的单词列表 词(群论) 名词 语言学 自然语言处理 人工智能 心理学 认知心理学 认知 判决 数学 统计 神经科学 哲学
作者
Eva Portelance,Yuguang Duan,Michael C. Frank,Gary Lupyan
出处
期刊:Cognitive Science [Wiley]
卷期号:47 (9)
标识
DOI:10.1111/cogs.13334
摘要

What makes a word easy to learn? Early-learned words are frequent and tend to name concrete referents. But words typically do not occur in isolation. Some words are predictable from their contexts; others are less so. Here, we investigate whether predictability relates to when children start producing different words (age of acquisition; AoA). We operationalized predictability in terms of a word's surprisal in child-directed speech, computed using n-gram and long-short-term-memory (LSTM) language models. Predictability derived from LSTMs was generally a better predictor than predictability derived from n-gram models. Across five languages, average surprisal was positively correlated with the AoA of predicates and function words but not nouns. Controlling for concreteness and word frequency, more predictable predicates and function words were learned earlier. Differences in predictability between languages were associated with cross-linguistic differences in AoA: the same word (when it was a predicate) was produced earlier in languages where the word was more predictable.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
森山完成签到,获得积分10
刚刚
Rsoup发布了新的文献求助10
1秒前
赶due小天才完成签到,获得积分10
1秒前
丰富的小蚂蚁完成签到,获得积分10
1秒前
2秒前
顾矜应助Gao采纳,获得10
2秒前
李健应助立羽采纳,获得10
3秒前
Jerome发布了新的文献求助10
3秒前
Sumzzzz发布了新的文献求助10
4秒前
SciGPT应助寒冷猕猴桃采纳,获得10
4秒前
科研通AI2S应助My采纳,获得10
4秒前
molihuakai应助强强强强去采纳,获得10
4秒前
4秒前
NPC-CBI完成签到,获得积分10
4秒前
5秒前
5秒前
芒果Mango发布了新的文献求助10
5秒前
CipherSage应助steplight采纳,获得10
6秒前
yu发布了新的文献求助10
8秒前
大个应助王贺帅采纳,获得10
9秒前
zhangxun发布了新的文献求助10
9秒前
9秒前
PUHAHA完成签到,获得积分10
10秒前
mao完成签到,获得积分10
13秒前
LL完成签到 ,获得积分10
13秒前
汉堡包应助spz采纳,获得10
14秒前
在水一方应助科研通管家采纳,获得10
14秒前
Hello应助科研通管家采纳,获得10
14秒前
14秒前
Hermen应助科研通管家采纳,获得10
14秒前
14秒前
大气傲之应助科研通管家采纳,获得10
14秒前
打打应助科研通管家采纳,获得10
14秒前
14秒前
李爱国应助科研通管家采纳,获得10
14秒前
15秒前
高贵的平松完成签到,获得积分10
15秒前
15秒前
ee发布了新的文献求助10
15秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
PowerCascade: A Synthetic Dataset for Cascading Failure Analysis in Power Systems 2000
Picture this! Including first nations fiction picture books in school library collections 1500
Signals, Systems, and Signal Processing 610
Unlocking Chemical Thinking: Reimagining Chemistry Teaching and Learning 555
CLSI M100 Performance Standards for Antimicrobial Susceptibility Testing 36th edition 400
Cancer Targets: Novel Therapies and Emerging Research Directions (Part 1) 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6361045
求助须知:如何正确求助?哪些是违规求助? 8174905
关于积分的说明 17220283
捐赠科研通 5416017
什么是DOI,文献DOI怎么找? 2866116
邀请新用户注册赠送积分活动 1843351
关于科研通互助平台的介绍 1691365