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In recent years, natural language processing (NLP) has become one of the mostimportant areas with various applications in human's life. As the mostfundamental task, the field of word embedding still requires more attention andresearch. Currently, existing works about word embedding are focusing onproposing novel embedding algorithms and dimension reduction techniques onwell-trained word embeddings. In this paper, we propose to use a novel jointsignal separation method - JIVE to jointly decompose various trained wordembeddings into joint and individual components. Through this decompositionframework, we can easily investigate the similarity and difference amongdifferent word embeddings. We conducted extensive empirical study on word2vec,FastText and GLoVE trained on different corpus and with different dimensions.We compared the performance of different decomposed components based onsentiment analysis on Twitter and Stanford sentiment treebank. We found that bymapping different word embeddings into the joint component, sentimentperformance can be greatly improved for the original word embeddings with lowerperformance. Moreover, we found that by concatenating different componentstogether, the same model can achieve better performance. These findings providegreat insights into the word embeddings and our work offer a new of generatingword embeddings by fusing.

Blind signal decomposition of various word embeddings based on join and individual variance explained