Most words in natural languages are polysemous; that is, they have related but different meanings in different contexts. This one‐to‐many mapping of form to meaning presents a challenge to understanding how word meanings are learned, represented, and processed. Previous work has focused on solutions in which multiple static semantic representations are linked to a single word form, which fails to capture important generalizations about how polysemous words are used; in particular, the graded nature of polysemous senses, and the flexibility and regularity of polysemy use. We provide a novel view of how polysemous words are represented and processed, focusing on how meaning is modulated by context. Our theory is implemented within a recurrent neural network that learns distributional information through exposure to a large and representative corpus of English. Clusters of meaning emerge from how the model processes individual word forms. In keeping with distributional theories of semantics, we suggest word meanings are generalized from contexts of different word tokens, with polysemy emerging as multiple clusters of contextually modulated meanings. We validate our results against a human‐annotated corpus of polysemy focusing on the gradedness, flexibility, and regularity of polysemous sense individuation, as well as behavioral findings of offline sense relatedness ratings and online sentence processing. The results provide novel insights into how polysemy emerges from contextual processing of word meaning from both a theoretical and computational point of view.

中文翻译：

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作为意义调制簇的词义：多义词的计算模型

自然语言中的大多数单词都是多义词；也就是说，它们在不同的上下文中具有相关但不同的含义。这种形式到意义的一对多映射对理解单词意义的学习、表示和处理方式提出了挑战。以前的工作集中在将多个静态语义表示链接到单个词形式的解决方案上，这未能捕捉到有关如何使用多义词的重要概括；尤其是多义词的分级性质，以及多义词使用的灵活性和规律性。我们提供了一种关于如何表示和处理多义词的新观点，重点关注上下文如何调节意义。我们的理论是在循环神经网络中实现的，该网络通过接触大量具有代表性的英语语料库来学习分布信息。意义集群从模型如何处理单个词形式中产生。为了与语义的分布理论保持一致，我们建议从不同单词标记的上下文中概括单词含义，多义性表现为多个上下文调制的含义集群。我们针对多义词的人工注释语料库验证我们的结果，重点关注多义词个性化的分级、灵活性和规律性，以及离线语义相关性评级和在线句子处理的行为发现。