A embedding model for text classification

Existing word embeddings learning algorithms only employ the contexts of words, but different text documents use words and their relevant parts of speech very differently. Based on the preceding assumption, in order to obtain appropriate word embeddings and further improve the effect of text classification, this paper studies in depth a representation of words combined with their parts of speech. First, using the parts of speech and context of words, a more expressive word embeddings can be obtained. Further, to improve the efficiency of look‐up tables, we construct a two‐dimensional table that is in the format to represent words in text documents. Finally, the two‐dimensional table and a Bayesian theorem are used for text classification. Experimental results show that our model has achieved more desirable results on standard data sets. And it has more preferable versatility and portability than alternative models.