Word Senses as Clusters of Meaning Modulations: A Computational Model of Polysemy
Word Senses as Clusters of Meaning Modulations: A Computational Model of Polysemy
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DOI：
10.1111/cogs.12955
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发表时间：
2021-04-01
影响因子：
2.5
通讯作者：
Joanisse, Marc F.
中科院分区：
文献类型：
--
作者：
Li, Jiangtian;Joanisse, Marc F.
自然语言中的大多数词都是多义的，也就是说，它们在不同的语境中有着相关但不同的含义。这种形式到意义的一对多映射对理解词义是如何学习、表示和处理的提出了挑战。以前的工作集中在解决方案中，多个静态的语义表示被链接到一个单词的形式，未能捕捉重要的概括多义词是如何使用的，特别是，多义词的意义的分级性质，以及多义词使用的灵活性和规律性。我们提供了一个新的观点，多义词是如何表示和处理，重点是如何意义调制的上下文。我们的理论是在一个循环神经网络中实现的，该网络通过接触大量有代表性的英语语料库来学习分布信息。意义的集群来自于模型如何处理单个单词的形式。与语义学的分布理论相一致，我们认为词义是从不同词的上下文中概括出来的，多义词是由上下文调制的多个词义组成的。我们验证我们的研究结果对人类注释的语料库的多义词侧重于多义的意义个性化的等级，灵活性和规律性，以及离线感觉相关性评级和在线句子处理的行为发现。研究结果从理论和计算的角度对一词多义是如何在语境中产生的提供了新的见解。
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.