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.

本文基于概率论的框架语义，提出了一种名词转动词的语言创造性计算形式，并通过多种语音实验验证了其优越性及可行性。