Improving Implicit Semantic Role Labeling by Predicting Semantic Frame Arguments
Table 2: A comparison on F1 scores (%). 2010: Gerber and Chai (2010), 2013: Laparra and Rigau (2013), 2016: Best model from Schenk and Chiarcos (2016), 2017: Our best model (Model 2).
['[BOLD] Predicate', 'Baseline', '2010', '2013', '2016', '2017']
Our system obtains relatively high scores (> 50%) on three predicates including “sale”, “plan” and “loss”. These three are the most frequent predicates (among the 10 defined in the nominal iSRL dataset) in the CoNLL 2009 training data – they occur 1016, 318 and 275 times in verbal forms, respectively. In contrast, irregular predicates such as “bid” or “loan” usually have low performance. This is possibly caused by the dependence of our PRNSFM on the performance of the explicit semantic role labeling system on verbal predicates. Comparing against Model 2, this yields p=0.28 for \newcitegerber:2010 , p=0.46 for \newciteimpar, and most importantly p=0.058 for \newciteDBLP:conf/naacl/SchenkC16.