Abstract
Biomedical literature is a large and rich source of information for various applications. Text mining tools aim at extracting information from the literature in an efficient manner since processing scientific texts is a complex task given the formal and highly specialized language. Text mining tools tackle these challenges using different approaches, such as rule-based methods and machine learning algorithms including deep learning. This document overviews the current biomedical text mining tools by describing their approaches, tasks (e.g., Named Entity Recognition, Relation Extraction, Event Extraction, Question Answering), available corpora, toolkits and applications, and community challenges.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
