Abstract Computational models of distributional semantics (a.k.a. word embeddings) represent a word’s meaning in terms of its relationships with all other words. We examine what grammatical information is encoded in distributional models and investigate the role of indirect associations. Distributional models are sensitive to associations between words at one degree of separation, such as ‘tiger’ and ‘stripes’, or two degrees of separation, such as ‘soar’ and ‘fly’. By recursively adding higher levels of representations to a computational, holographic model of semantic memory, we construct a distributional model sensitive to associations between words at arbitrary degrees of separation. We find that word associations at four degrees of separation increase the similarity assigned by the model to English words that share part-of-speech or syntactic type. Word associations at four degrees of separation also improve the ability of the model to construct grammatical English sentences. Our model proposes that human memory uses indirect associations to learn part-of-speech and that the basic associative mechanisms of memory and learning support knowledge of both semantics and grammatical structure.

中文翻译：

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学习语义和句法词汇关系中的间接关联

摘要 分布语义（又名词嵌入）的计算模型根据词与所有其他词的关系来表示词的含义。我们研究了在分布模型中编码了哪些语法信息，并研究了间接关联的作用。分布模型对处于一种分离度（例如“老虎”和“条纹”）或两个分离度（例如“翱翔”和“飞翔”）的单词之间的关联很敏感。通过递归地向语义记忆的计算全息模型添加更高级别的表示，我们构建了一个对任意分离度的单词之间的关联敏感的分布模型。我们发现四个分离度的单词关联增加了模型分配给共享词性或句法类型的英语单词的相似性。四个分离度的词关联也提高了模型构建语法英语句子的能力。我们的模型提出人类记忆使用间接关联来学习词性，并且记忆和学习的基本关联机制支持语义和语法结构的知识。