Description: The term ‘out of vocabulary’ refers to words that are not included in the vocabulary of a natural language processing (NLP) model. This can pose a significant challenge for NLP systems, as the understanding and generation of text largely depend on the model’s ability to recognize and process words. When a model encounters a word outside its vocabulary, it may not be able to interpret it correctly, leading to errors in translation, sentiment analysis, or text generation. This phenomenon is particularly relevant in contexts where jargon, neologisms, or technical terms are used that have not been previously trained in the model. Managing out-of-vocabulary words is an active area of research in NLP, where methods are sought to expand the vocabulary of models or to effectively handle unknown words through techniques such as subword tokenization or the use of word embeddings. In summary, the concept of ‘out of vocabulary’ is crucial for understanding the limitations and challenges faced by NLP models in their interaction with human language.
