Indirect associations in learning semantic and syntactic lexical relationships.
Indirect associations in learning semantic and syntactic lexical relationships.
复制标题
学习语义和句法词汇关系中的间接关联。
DOI：
10.1016/j.jml.2020.104153
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发表时间：
2020
影响因子：
4.3
通讯作者：
Reitter, D.
中科院分区：
文献类型：
--
作者：
Kelly, M.A.;Ghafurian M., West;Reitter, D.
分布式语义的计算模型（又名词嵌入）根据词与所有其他词的关系来表示词的含义。我们检查分布式模型中编码了哪些语法信息，并研究了间接关联的作用。分布模型对一级分离的单词之间的关联很敏感，例如“老虎”和“条纹”，或者二级分离的单词之间的关联，例如“翱翔”和“飞翔”。通过递归地将更高级别的表示添加到语义记忆的计算全息模型中，我们构建了一个对任意分离程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。四个分离度的单词关联也提高了模型构建符合语法的英语句子的能力。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。
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.