Deep learning models for representing out-of-vocabulary words

Understand

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.