Source PaperYearLineSentence
W04-2412 2004 144
Two other groups applied Memory-Based Learning (MBL) (van den Bosch et al, 2004; Kouchnir, 2004)
W06-1622 2006 7
Many research efforts utilize machine learning (ML) approaches; such as support vector machines (Moschitti et al, 2004; Pradhan et al, 2004), percep trons (Carreras et al, 2004), the SNoW learning architecture (Punyakanok et al, 2004), EMbased clustering (Baldewein et al, 2004), transformation-based learning (Higgins, 2004), memory-based learning (Kouchnir, 2004), and induc tive learning (Surdeanu et al, 2003)
W06-1622 2006 12
Many existing SRL systems are also memory-based (Bosch et al, 2004;Kouchnir, 2004), implemented using TilMBL software (http://ilk.kub.nl/software.html) with advanced methods such as Feature Weighting, and so forth
W06-1622 2006 172
task (to label arguments by giving the known arguments) on the test data WSJ 21, increases F1:6.68 compared to the result of Kouchnir (2004) in the third row
W06-1622 2006 185
Illustration of results by PML with different methods on WSJ 24 with known arguments System Train Test Tasks P R F1 Lacc T Palmer (2005) W02-21 W23 BR+RL 68.60 57.80 62.74 81.70 3.785 PARA+PML W02-21 W23 BR+RL 71.24 70.79 71.02 88.77 0.941 Kouchnir (2004) W15-18 W21 RL 75.71 74.60 75.15 kNN W15-18 W21 RL 81.86 81.79 81.83 83.57 0.242 Table 12