In newspaper articles, emails, and other texts, it is natural to reference dates in relation to the publication date. For example, you will commonly see phrases like “yesterday” and “two weeks ago.” However, if you read an article a year after it was published, these timeframes are not immediately clear. When analyzing articles using natural language processing (NLP) techniques, relative dates referenced in an article will not be interpreted properly, if at all, since there is no context for the models. Thus, in order to effectively use tools such as event extraction and question-answering engines, relative references need to be…
State-of-the-art NLP made easy with AdaptNLP
Natural Language Processing (NLP) has advanced significantly since 2018, when ULMFiT and Google’s release of the BERT language model approached human-level performance on a range of use cases. Since then, several models, such as XLM, GPT-2, XLNet, and ALBERT, have been released in quick succession, each improving on its predecessors. While these state-of-the-art models can solve human-level, language-based tasks on large volumes of unstructured text for certain use cases, getting a handle on what to use, when to use it, and how to use it can be a challenge. At Novetta, we explored what it would take to streamline the…
