主题联合词向量模型

Topic Combined Word Vector Model

当前大部分的词向量模型针对一个单词只能生成一个向量,由于单词的多义性,使用同一个向量表达不同语境下的同一个单词是不准确的。对此,提出一种新的词向量模型。使用潜狄利克雷特分布和神经网络对单词进行训练,得到单词及其主题的向量,并对两者进行线性变换得到最终的词向量。实验结果表明,该模型的准确度高于现有多向量模型。

Currently,most word vector models can build only one vector for a single word.Due to word’s polysemy,it is incorrect to use one vector representing a same word under different context.This paper proposes a new word vector model.It uses latent dirichlet distribution and neural networks to train words to obtain word vectors and corresponding topic vectors.And then it applies linear transformations on them to build the final word vectors.Experimental results show that the accuracy of proposed model is high compared with current multi-vector models.