RI: Small: Scalable Algorithms for Learning to Recover Logical Form from Natural Language

RI：小型：用于学习从自然语言恢复逻辑形式的可扩展算法

• 批准号: 1115966
• 负责人: Luke Zettlemoyer
• 金额: $ 30万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2014-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1115966&HistoricalAwards=false
• 关键词: RI; Small; Scalable; Algorithms; Learning

A key aim in Natural Language Processing is to robustly map from natural language sentences to formal representations of their underlying meaning. Recent work has addressed this problem by learning semantic parsers given sentences paired with logical meaning representations. The goal of this project is to develop models and learning algorithms for recovering lexical structure, in the context mapping sentences to logical form. This work is inspired by linguistic theories of the lexicon, but directly motivated by the limitations observed in current, state-of-the-art learning algorithms.The central hypothesis is that a new probabilistic learning approach for lexical generalization can simultaneous achieve the goals of (1) language-independent learning, (2) robustness when analyzing natural, unedited text, and (3) requiring reduced data annotation effort, in a computationally efficient manner that will scale to large learning problems. The approach under development induces a Combinatory Categorial Grammar (CCG), that is modified to replace the traditional, explicit list of lexical items in the lexicon with a distribution over lexical items that allows for significant generalization in the construction of possible syntactic and semantic structures for given input words. Modifying the CCG lexicon in this manner greatly increases the potential to generalize from the available training data without sacrificing the scalability that comes from working within an established grammar formalism for which efficient learning and parsing algorithms have been developed. This work will have impact at the algorithmic level and through applications, including advanced natural language interfaces to databases for non-technical users.

自然语言处理的一个关键目的是将自然语言句子鲁棒性映射到其基本含义的形式表示。最近的工作通过学习语义解析器以及逻辑含义表示的句子来解决这个问题。该项目的目的是在上下文映射句子中开发模型和学习算法，以恢复词汇结构。 This work is inspired by linguistic theories of the lexicon, but directly motivated by the limitations observed in current, state-of-the-art learning algorithms.The central hypothesis is that a new probabilistic learning approach for lexical generalization can simultaneous achieve the goals of (1) language-independent learning, (2) robustness when analyzing natural, unedited text, and (3) requiring reduced data annotation effort, in a computationally有效的方式将扩展到大型学习问题。开发方法引起了组合性分类语法（CCG），该语法（CCG）被修改为替代词汇中传统的，明确的词汇项目清单，并在词汇项目上分布，可以在构建可能的输入单词的可能的句法和语义结构中进行重大概括。以这种方式修改CCG词典可以大大增加从可用的培训数据中概括的潜力，而无需牺牲在已建立的语法形式主义中起作用的可扩展性，从而为有效的学习和解析算法开发了。这项工作将在算法级别和应用程序中产生影响，包括针对非技术用户数据库的高级自然语言接口。