Semantic Compositionality through Recursive Matrix-Vector Spaces
Semantic Compositionality through Recursive Matrix-Vector Spaces
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发表时间：
2012-07
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通讯作者：
R. Socher;Brody Huval;Christopher D. Manning;A. Ng
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作者：
R. Socher;Brody Huval;Christopher D. Manning;A. Ng
单词向量空间模型在学习词汇信息方面非常成功。然而，他们无法捕捉较长短语的组成意义，这阻碍了他们对语言的更深入理解。我们引入了一个递归神经网络（RNN）模型，该模型可以学习任意句法类型和长度的短语和句子的组合向量表示。我们的模型为解析树中的每个节点分配了一个向量和一个矩阵：向量捕获成分的固有含义，而矩阵捕获它如何改变相邻单词或短语的含义。这个矩阵向量RNN可以学习命题逻辑和自然语言中运算符的含义。该模型在三个不同的实验中获得了最先进的性能：预测副词-形容词对的细粒度情感分布;对电影评论的情感标签进行分类;以及使用名词之间的句法路径对名词之间的语义关系进行分类，例如因果关系或主题信息。
Single-word vector space models have been very successful at learning lexical information. However, they cannot capture the compositional meaning of longer phrases, preventing them from a deeper understanding of language. We introduce a recursive neural network (RNN) model that learns compositional vector representations for phrases and sentences of arbitrary syntactic type and length. Our model assigns a vector and a matrix to every node in a parse tree: the vector captures the inherent meaning of the constituent, while the matrix captures how it changes the meaning of neighboring words or phrases. This matrix-vector RNN can learn the meaning of operators in propositional logic and natural language. The model obtains state of the art performance on three different experiments: predicting fine-grained sentiment distributions of adverb-adjective pairs; classifying sentiment labels of movie reviews and classifying semantic relationships such as cause-effect or topic-message between nouns using the syntactic path between them.