Results: The results of this project were summarised in the presentation and explained in detail in the final documentation. In the second half of the course we will apply these models to natural language processing tasks, including question answering, text classification (including. Also free to use other deep learning libraries such as pytorch or mxnet. The language is python, using keras with tensoflow backend. The main focus of the project is to develop NER and optimize the performance for standard entities, which can be expanded for non-standard and insurance specific ones. The approach is very versatile and elegant in the sense that it can tackle many different aspects of Natural Language Understanding, such as sense disambiguation, entity linking and co-reference resolution. In chapter 2, gain a clear understanding of how deep learning works in general and what it brings to the field of NLP. Semantic word embeddings such as word2vec have been used as the basis (namely the first layer) of such deep learning systems. In Deep Learning for Natural Language Processing, author Stephan Raaijmakers reveals the innovative techniques that achieve these state-of-the-art results. Deep learning models have been successfully applied to NER, which obviates the need for hand engineered features and still achieving state-of-the-art performance. Traditionally it has been tackled by supervised learning on a number of hand engineered linguistic features, which requires extensive linguistic expertise. Objective: This article methodically reviews the literature on deep learning (DL) for natural language processing (NLP) in the clinical domain, providing quantitative analysis to answer 3 research questions concerning methods, scope, and context of current research. Named Entity Recognition (NER) is a subtask of Information Extraction (IE), that seeks to locate and classify named entities in text into pre-defined categories such as persons, organizations, events, locations etc. This website offers an open and free introductory course on deep learning algorithms and popular architectures for contemporary Natural Language Processing (NLP). Scientific Lead: Oliver Mitevski, Mariia Stepanova.
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