This sub-topic focuses on innovations in the field of machine learning and highlights, in particular, natural language processing (NLP). Machine learning refers to processes in which an automated system can learn from data, rather than following a pre-specified set of rules, and in many cases can predict outcomes relating to the learned process. NLP uses machine learning to extract information or derive meaning from human language (written or spoken) or to generate human language.
Examples of relevant technical fields within machine learning include (but are not limited to): supervised machine learning; semi-supervised machine learning; unsupervised machine learning; neural networks; artificial intelligence (of which machine learning is a sub-category); machine learning algorithms - e.g., decision tree learning; robot learning; pattern recognition; image recognition. Examples of technical fields within NLP include (but are not limited to): parsing; named entity recognition; data extraction from text; natural language understanding; natural language generation; automatic summarization; machine translation; analysis of structured or unstructured text; speech recognition; speech analysis; and speech processing. Applications across both technical fields include (but are not limited to): improvements in human-computer interaction - e.g., computers anticipating users’ needs; automated manufacturing; machine vision; robotic control systems; cyber-physical control systems; sentiment analysis; the analysis of online commentary; automated medical diagnosis; stock market analysis; and translation services (including speech-to-speech translation).