Abstract
This paper presents our solution for CoNLL 2008 shared task that jointly parses syntactic and semantic dependencies. The Maximum Entropy (ME) classifier has been selected to train the data used in this system. Also the Mutual Information (MI) model was utilized into feature selection of dependency labeling. Results show that the MI model allows the system to get better performance and less training hours.
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.
