11 Nivre ( 2008 ) reports that non-projective and pseudo-projective algorithms outperform the `` eager '' projective algorithm in MaltParser , but our training data did not contain any non-projective dependencies .
algorithms for deterministic incremental dependency parsing
Joakim Nivre
To recap , it has the following MaltParser attributes ( machine learning features ) : 4 word-form attributes , 7 POS tag attributes , and 5 deprel attributes ( some of which are not useful for the Nivre `` eager '' algorithm ) , totaling 16 attributes and two more for every new feature described in Section 4.3 and on ( e.g. , CASE ) . 11 Nivre ( 2008 ) reports that non-projective and pseudo-projective algorithms outperform the `` eager '' projective algorithm in MaltParser , but our training data did not contain any non-projective dependencies . The Nivre `` standard '' algorithm is also reported there to do better on Arabic , but in a preliminary experimentation , it did slightly worse than the `` eager '' one , perhaps due to the high percentage of right branching ( left headed structures ) in our Arabic training set -- an observation already noted in Nivre ( 2008 ) .
11 @@CITATION reports that non-projective and pseudo-projective algorithms outperform the `` eager '' projective algorithm in MaltParser , but our training data did not contain any non-projective dependencies .