The NTNU System at SemEval-2017 Task 10: Extracting Keyphrases and Relations from Scientific Publications Using Multiple Conditional Random Fields
This study describes the design of the NTNU system for the ScienceIE task at the SemEval 2017 workshop. We use self-defined feature templates and multiple conditional random fields with extracted features to identify keyphrases along with categorized labels and their relations from scientific publications. A total of 16 teams participated in evaluation scenario 1 (subtasks A, B, and C), with only 7 teams competing in all sub-tasks. Our best micro-averaging F1 across the three subtasks is 0.23, ranking in the middle among all 16 submissions.
PDF AbstractTasks
Datasets
Add Datasets
introduced or used in this paper
Results from the Paper
Submit
results from this paper
to get state-of-the-art GitHub badges and help the
community compare results to other papers.
Methods
No methods listed for this paper. Add
relevant methods here
