  In this paper, we propose three novel models to enhance word embedding by
implicitly using morphological information. Experiments on word similarity and
syntactic analogy show that the implicit models are superior to traditional
explicit ones. Our models outperform all state-of-the-art baselines and
significantly improve the performance on both tasks. Moreover, our performance
on the smallest corpus is similar to the performance of CBOW on the corpus
which is five times the size of ours. Parameter analysis indicates that the
implicit models can supplement semantic information during the word embedding
training process.
