Abstract
Coreference resolution is an important task in natural language processing which aims to group the mention pairs referring to a single entity. In the biomedical domain, it significantly poses some unique challenges. In this work, we make use of both hand-crafted features and neural word embedding based features to solve the task of coreference resolution on a standard benchmark biomedical coreference dataset, i.e the BioNLP-2011 Protein Coreference data. Experimental results show that the neural model performs significantly better in terms of mention-referent linking when compared to the hand-crafted feature-based coreference resolution approaches.
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.
