  Historically, the Natural Language Processing area has been given too much
attention by many researchers. One of the main motivation beyond this interest
is related to the word prediction problem, which states that given a set words
in a sentence, one can recommend the next word. In literature, this problem is
solved by methods based on syntactic or semantic analysis. Solely, each of
these analysis cannot achieve practical results for end-user applications. For
instance, the Latent Semantic Analysis can handle semantic features of text,
but cannot suggest words considering syntactical rules. On the other hand,
there are models that treat both methods together and achieve state-of-the-art
results, e.g. Deep Learning. These models can demand high computational effort,
which can make the model infeasible for certain types of applications. With the
advance of the technology and mathematical models, it is possible to develop
faster systems with more accuracy. This work proposes a hybrid word suggestion
model, based on Naive Bayes and Latent Semantic Analysis, considering
neighbouring words around unfilled gaps. Results show that this model could
achieve 44.2% of accuracy in the MSR Sentence Completion Challenge.
