Verb learning and the early development of sentence comprehension

描述（由申请人提供）：儿童使用句法来理解句子和学习动词；这是语法引导。我们提出了一个关于句法自举起源的结构映射解释。因此，孩子们一开始就有一种非习得的偏见，即把句子中的名词和事件中的参与者角色一一对应起来。鉴于这种偏见，孩子们发现句子中名词的数量本身就有意义。在之前的资助期内，我们测试了该帐户的关键预测，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了句法结构的部分表征，使幼儿能够识别动词，并在简单句中解释新颖的及物动词和不及物动词。这项研究提出了一个问题，即句法自助如何超越“数名词”，扩展到动词和句子的真正复杂性。我们关注两个数据源：分布学习和话语结构。首先，我们提出分布式学习创建关于动词的概率性句法语义组合知识。这种组合知识，也被称为动词偏差，允许句法自举，并且从开发的早期就用于在线帮助识别句子的结构和词汇内容，指导句法分析。其次，我们提出语篇连续性的倾向通过允许学习者收集来增加对动词学习的语言支持

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.