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: 8697336
• 负责人: CYNTHIA L FISHER
• 金额: $ 31.01万
• 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/8697336
• 关键词: Accounting; Address; Behavior; Child; Comprehension; Computer Simulation; Cues; Data; Data Sources; Detection; Development; Environment; Event; Feedback; Funding; 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; public health relevance; remediation; research study; scale up; syntax; theories

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(申请人提供)：儿童使用句法理解句子和学习动词；这是句法自学。我们提出了句法自举起源的结构映射解释。在这一点上，孩子们一开始就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习得的偏见。鉴于这种偏见，孩子们会发现句子中名词的数量具有内在的意义。在上一个资助期，我们测试了这个账户的关键预测，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，这使得幼儿能够识别动词，并在简单的句子中解释新的及物动词和不及物动词。这项拟议中的研究提出的问题是，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。我们关注两个数据来源：分布式学习和语篇结构。首先，我们提出分布式学习产生关于动词的概率句法-语义组合知识。这种组合知识，也被称为动词偏见，允许句法自举，从发展的早期就在网上被用来帮助识别句子的结构和词汇内容，指导句法分析。其次，我们认为偏向语篇连续性的偏向通过允许学习者收集信息来增加对动词学习的语言支持 邻近句子中论点的证据。动词偏误通过提示儿童在语篇语境中寻找缺失论元的指代来指导这一过程。为了研究这些建议，我们结合了对幼儿和学龄前儿童的实验，以及基于自动语义角色标注系统的计算模型。儿童实验通过测量儿童对相关场景或物体的视觉凝视来评估对句子中熟悉的和虚构的动词的理解。项目1探索幼儿从听力经验中对动词的句法-语义组合事实进行编码的过程。项目2探索幼儿如何使用语篇语境来指导句子的理解，受动词偏向的制约。项目3询问学龄前儿童的动词偏向在多大程度上会随着新的分配学习而改变。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。儿童实验的结果限制了我们装备模型来检测的特征；我们使用该模型来测试我们声称从自然的儿童导向语音语料库中学习的结果。这种实验和计算研究的结合将促进关于儿童如何学习他们的母语的科学知识，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。这项研究将帮助我们了解儿童是如何学习母语的词汇和句法的；这项研究将有助于检测和纠正语言延迟，并有助于语言教学。