Humans can flexibly extend word usages across different grammatical classes, a phenomenon known as word class conversion. Noun-to-verb conversion, or denominal verb (e.g., to Google a cheap flight), is one of the most prevalent forms of word class conversion. However, existing natural language processing systems are impoverished in interpreting and generating novel denominal verb usages. Previous work has suggested that novel denominal verb usages are comprehensible if the listener can compute the intended meaning based on shared knowledge with the speaker. Here we explore a computational formalism for this proposal couched in frame semantics. We present a formal framework, Noun2Verb, that simulates the production and comprehension of novel denominal verb usages by modeling shared knowledge of speaker and listener in semantic frames. We evaluate an incremental set of probabilistic models that learn to interpret and generate novel denominal verb usages via paraphrasing. We show that a model where the speaker and listener cooperatively learn the joint distribution over semantic frame elements better explains the empirical denominal verb usages than state-of-the-art language models, evaluated against data from 1) contemporary English in both adult and child speech, 2) contemporary Mandarin Chinese, and 3) the historical development of English. Our work grounds word class conversion in probabilistic frame semantics and bridges the gap between natural language processing systems and humans in lexical creativity.
翻译:人类可以灵活地扩展不同语系类的文字用法, 这是一种被称为文字类转换的现象。 名词到动词转换, 或书名动词( 例如, 谷歌是一种廉价的飞行) 是最普遍的文字类转换形式之一。 但是, 现有的自然语言处理系统在解读和生成新颖的语系动词用法方面贫乏。 先前的工作表明, 如果听众能够根据与演讲者共享的知识来计算预想的含义, 语言使用法是可以理解的。 在这里, 我们探索了这个提案的计算形式, 以框架语义表达法的形式表达。 我们提出了一个正式的框架, Noun2Verb, 通过在语系框架中模拟演讲者和听众共享知识, 来模拟新颖的语言动词流动动动动动动动动动动动词的用法的制作和理解。 我们评估了一套递增的概率模型, 来学习解释和生成基于与演讲者共享的知识的语系使用。 我们展示了一种模式, 演讲者和听众在当代语系结构框架要素中使用的当代语言结构结构结构结构,, 和英语结构结构框架, 解释我们历史语言结构学系 学系 学系 学系 学系, 。