Table 2.

Performance metrics for selected system submissions for subtask-2, baselines, and system ensembles. Micro-averaged precision, recall, and F₁-scores are shown for the definite intake (class 1) and possible intake (class 2) classes. The highest F₁-score over the evaluation dataset is shown in bold. Detailed discussions about the approaches can be found in the system description papers referenced (when available)

System/Team	Micro-averaged precision for classes 1 and 2	Micro-averaged recall for classes 1 and 2	Micro-averaged F1-score for classes 1 and 2
Baseline 1: Naïve Bayes	0.359	0.503	0.419
Baseline 2: SVMs	0.652	0.436	0.523
Baseline 3: Random Forest	0.628	0.487	0.549
InfyNLP44(Infosys Ltd)	0.725	0.664	0.693
UKNLP41(University of Kentucky)	0.701	0.677	0.689
NRC-Canada35	0.708	0.642	0.673
TJIIP (Tongji University, China)	0.691	0.641	0.665
TurkuNLP43(University of Turku)	0.701	0.630	0.663
CSaRUS-CNN50(Arizona State University)	0.709	0.604	0.652
NTTMU53(Multiple Universities, Taiwan)	0.690	0.554	0.614
Ensemble all: majority vote	0.736	0.657	0.694
Ensemble top 10: majority vote	0.726	0.679	0.702
Ensemble top 7: majority vote	0.724	0.673	0.697
Ensemble top 5: majority vote	0.723	0.667	0.694
Ensemble top submissions from top 5 teams: majority vote	0.727	0.673	0.699