  Communication has become increasingly dynamic with the popularization of
social networks and applications that allow people to express themselves and
communicate instantly. In this scenario, distributed representation models have
their quality impacted by new words that appear frequently or that are derived
from spelling errors. These words that are unknown by the models, known as
out-of-vocabulary (OOV) words, need to be properly handled to not degrade the
quality of the natural language processing (NLP) applications, which depend on
the appropriate vector representation of the texts. To better understand this
problem and finding the best techniques to handle OOV words, in this study, we
present a comprehensive performance evaluation of deep learning models for
representing OOV words. We performed an intrinsic evaluation using a benchmark
dataset and an extrinsic evaluation using different NLP tasks: text
categorization, named entity recognition, and part-of-speech tagging. Although
the results indicated that the best technique for handling OOV words is
different for each task, Comick, a deep learning method that infers the
embedding based on the context and the morphological structure of the OOV word,
obtained promising results.
