论文标题
蒙塔古语法诱导
Montague Grammar Induction
论文作者
论文摘要
我们提出了一个计算建模框架，用于从任意行为数据中诱导组合性分类语法。该框架提供了分析师对诱导的语法应符合的假设的细粒度控制：（i）原始类型是什么； （ii）如何构建复杂类型； （iii）可以使用哪种组合器来组合类型； （iv）（以及什么）是否应修复某些词汇项目的类型。在概念验证实验中，我们将框架部署在分配分析中。我们专注于S（Emantic）选择与C（Ategory）选择之间的关系，使用作为输入的词典级可接受性判断数据集，这些判断数据集专注于英语动词的语法分布（MegaAcceptibality DataSet）和从诱导的语法上的语法文献中实施标准假设。
We propose a computational modeling framework for inducing combinatory categorial grammars from arbitrary behavioral data. This framework provides the analyst fine-grained control over the assumptions that the induced grammar should conform to: (i) what the primitive types are; (ii) how complex types are constructed; (iii) what set of combinators can be used to combine types; and (iv) whether (and to what) the types of some lexical items should be fixed. In a proof-of-concept experiment, we deploy our framework for use in distributional analysis. We focus on the relationship between s(emantic)-selection and c(ategory)-selection, using as input a lexicon-scale acceptability judgment dataset focused on English verbs' syntactic distribution (the MegaAcceptability dataset) and enforcing standard assumptions from the semantics literature on the induced grammar.
