Natural language processing and information extraction
Abstract
This thesis presents original research in the subject of Machine Learning and more specifically in the fields of Natural Language Processing and Information Extraction. We focus on the following research problems which concern specific tasks in Natural Language Processing and Information Extraction: a) improving clinical decision making through Biomedical Entity Recognition, b) advancing Biomedical Argumentation Mining, c) efficient Language Modeling with distant contextual information and d) deploying Natural Language Processing applications in the real world. First, we present a series of novel architectures for refined Biomedical Entity Recognition, with specific focus in Evidence-Based Medicine entities. These semantically rich entities, which are more descriptive than generic biomedical entity types, offer useful insights in the treatment formulation process and are harder for Machine Learning models to identify. The incrementally proposed changes to the Deep Neural Network archi ...
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