The crux of information systems is efficient storage and access to useful data by users. This paper is an overview of work that has advanced the use of such systems in recent years, primarily in machine learning, and specifically, deep learning methods. Situating progress in terms of classical pattern recognition techniques for text, we review computational methods to process spoken and written data. Digital assistants such as Siri, Cortana, and Google Now exploit large language models and encoder-only transformer-based systems such as BERT. Practical tasks include Machine Translation, Information Retrieval, Text Summarization, Question-Answering, Sentiment Analysis, Natural Language Generation, Named Entity Recognition, and Relation Extraction. Issues to be covered include: post-training through alignment, parsing, and Reinforcement Learning.
Douglas O’Shaughnessy (Thu,) studied this question.