  Natural language processing models have attracted much interest in the deep
learning community. This branch of study is composed of some applications such
as machine translation, sentiment analysis, named entity recognition, question
and answer, and others. Word embeddings are continuous word representations,
they are an essential module for those applications and are generally used as
input word representation to the deep learning models. Word2Vec and GloVe are
two popular methods to learn word embeddings. They achieve good word
representations, however, they learn representations with limited information
because they ignore the morphological information of the words and consider
only one representation vector for each word. This approach implies that
Word2Vec and GloVe are unaware of the word inner structure. To mitigate this
problem, the FastText model represents each word as a bag of characters
n-grams. Hence, each n-gram has a continuous vector representation, and the
final word representation is the sum of its characters n-grams vectors.
Nevertheless, the use of all n-grams character of a word is a poor approach
since some n-grams have no semantic relation with their words and increase the
amount of potentially useless information. This approach also increases the
training phase time. In this work, we propose a new method for training word
embeddings, and its goal is to replace the FastText bag of character n-grams
for a bag of word morphemes through the morphological analysis of the word.
Thus, words with similar context and morphemes are represented by vectors close
to each other. To evaluate our new approach, we performed intrinsic evaluations
considering 15 different tasks, and the results show a competitive performance
compared to FastText.
