Verb learning and the early development of sentence comprehension

动词学习和句子理解的早期发展

• 批准号: 8837036
• 负责人: CYNTHIA L FISHER
• 金额: $ 30.18万
• 依托单位: UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-04-10 至 2019-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8837036
• 关键词: Accounting; Address; Behavior; Child; Comprehension; Computer Simulation; Cues; Data; Data Sources; Detection; Development; Environment; Event; Feedback; Funding; Health; Individual; Instruction; Knowledge; Label; Language; Language Delays; Language Development; Lead; Learning; Light; Linguistics; Link; Maps; Measures; Memory; Modeling; Natural Language Processing; Ocular Fixation; Participant; Play; Process; Protocols documentation; Research; Risk; Role; Scientific Advances and Accomplishments; Semantics; Speech; Structure; System; Testing; Toddler; Uncertainty; Variant; Work; abstracting; base; combinatorial; computer studies; early experience; expectation; experience; improved; lexical; novel; remediation; research study; scale up; syntax; theories; word learning

DESCRIPTION (provided by applicant): Children use syntax to understand sentences and to learn verbs; this is syntactic bootstrapping. We proposed a Structure Mapping account of the origins of syntactic bootstrapping. On this account, children begin with an unlearned bias toward one-to-one mapping between nouns in sentences and participant-roles in events. Given this bias, children find the number of nouns in a sentence inherently meaningful. In the previous funding period, we tested key predictions of this account, and found strong evidence for structure-mapping. Identifying the set of nouns in sentences yields a partial representation of syntactic structure that allows toddlers to identify verbs, and to interpret novel transitive and intransitive verbs in simple sentences. The proposed research asks how syntactic bootstrapping moves beyond 'counting the nouns', scaling up to the true complexity of verbs and sentences. We focus on two data-sources: distributional learning and discourse structure. First, we propose that distributional learning creates probabilistic syntactic-semantic combinatorial knowledge about verbs. This combinatorial knowledge, also known as verb bias, permits syntactic bootstrapping, and from early in development is used online to help identify the structure and lexical content of sentences, guiding syntactic analysis. Second, we propose that a bias toward discourse continuity increases linguistic support for verb learning by allowing learners to collect evidence for arguments across nearby sentences. Verb bias guides this process, by cuing children to seek referents for missing arguments in the discourse context. To investigate these proposals we combine experiments with toddlers and preschoolers, and a computational model based on systems for automatic semantic role labeling. Experiments with children assess comprehension of familiar and invented verbs in sentences, by measuring children's visual fixations to relevant scenes or objects. Project 1 explores toddlers' encoding of syntactic-semantic combinatorial facts about verbs from listening experience. Project 2 explores toddlers' use of discourse context to guide sentence interpretation, constrained by verb bias. Project 3 asks to what extent verb bias in preschoolers changes with new distributional learning. In Project 4 we develop our computational model to investigate the same processes. Results from experiments with children constrain the features we equip the model to detect; we use the model to test the consequences of our claims for learning from corpora of natural child-directed speech. This combination of experimental and computational studies will advance scientific knowledge about how children learn their native languages, and guide the development of new, robust learning protocols that will be of use in automatic natural language processing. The proposed research will help us to understand how children learn the words and syntax of their native languages; such research will contribute to the detection and remediation of language delays, and to language pedagogy.

描述（由申请人提供）：儿童使用句法来理解句子和学习动词;这是句法引导。我们提出了一个结构映射解释句法自举的起源。因此，儿童开始时，对句子中的名词和事件中的参与者角色之间的一对一映射有一种未习得的偏见。由于这种偏见，孩子们发现一个句子中名词的数量本身就有意义。在上一个融资期，我们测试了该账户的关键预测，并发现了结构映射的有力证据。识别句子中的名词集合产生句法结构的部分表示，使幼儿能够识别动词，并在简单的句子中解释新颖的及物动词和不及物动词。拟议的研究询问句法自举如何超越“计算名词”，扩大到动词和句子的真正复杂性。我们专注于两个数据源：分布式学习和话语结构。首先，我们提出，分布式学习创建概率句法语义组合知识的动词。这种组合知识，也被称为动词偏见，允许句法自举，并从开发的早期开始在线使用，以帮助识别句子的结构和词汇内容，指导句法分析。第二，我们认为，偏向语篇连续性可以通过允许学习者收集信息来增加对动词学习的语言支持 在相邻句子之间的论点的证据。动词偏向引导这一过程，通过提示儿童在话语语境中为缺失的论元寻找所指。为了研究这些建议，我们结合联合收割机实验幼儿和学龄前儿童，和一个计算模型的基础上系统的自动语义角色标注。儿童实验通过测量儿童对相关场景或物体的视觉注视来评估对句子中熟悉和发明动词的理解。项目1探讨幼儿对来自听力经验的动词句法语义组合事实的编码。项目2探讨幼儿使用话语语境来指导句子解释，受动词偏见的约束。项目3询问学龄前儿童的动词偏见在多大程度上随着新的分布学习而改变。在项目4中，我们开发了我们的计算模型来研究相同的过程。从儿童的实验结果约束的功能，我们装备的模型来检测，我们使用该模型来测试我们的索赔的后果，从语料库的自然儿童导向的语音学习。这种实验和计算研究的结合将推进关于儿童如何学习母语的科学知识，并指导开发新的，强大的学习协议，这些协议将用于自动自然语言处理。拟议的研究将有助于我们了解儿童如何学习母语的单词和句法;这样的研究将有助于发现和补救语言延迟，并有助于语言教学。