Department of Psychology, University of Toronto Scarborough.
Department of Psychology, The University of Western Ontario.
Cogn Sci. 2021 Apr;45(4):e12955. doi: 10.1111/cogs.12955.
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.
大多数自然语言中的单词都是多义词；也就是说，它们在不同的上下文中具有相关但不同的含义。这种从形式到意义的一一映射给理解单词含义的学习、表示和处理方式带来了挑战。之前的工作主要集中在将多个静态语义表示与单个单词形式相关联的解决方案上，而这种方法未能捕捉到关于多义词如何使用的重要概括；特别是多义词意义的渐进性，以及多义词使用的灵活性和规律性。我们提供了一种新的视角来表示和处理多义词，重点关注上下文如何调节意义。我们的理论是在一个通过接触大量具有代表性的英语语料库来学习分布信息的递归神经网络中实现的。从模型处理单个单词形式的方式中涌现出了意义的集群。与语义的分布理论一致，我们认为单词的含义是从不同单词标记的上下文泛化而来的，多义性是作为多个语境调节意义的集群出现的。我们针对多义性的人类注释语料库验证了我们的结果，重点关注多义性意义个体化的渐进性、灵活性和规律性，以及离线意义相关性评分和在线句子处理的行为发现。结果从理论和计算的角度为我们提供了关于多义性如何从语境处理单词意义中产生的新见解。
