Multilingual Deterministic Dependency Parsing Framework using Modified Finite Newton Method Support Vector Machines

In this paper, we present a three-step multilingual dependency parser based on a deterministic shift-reduce parsing algorithm. Different from last year, we separate the root-parsing strategy as sequential labeling task and try to link the neighbor word dependences via a near neighbor parsing. The outputs of the root and neighbor parsers were encoded as features for the shift-reduce parser. In addition, the learners we used for the two parsers and the shift-reduce parser are quite different (conditional random fields and the modified finite-Newton method support vector machines). We found that our method could benefit from the two-preprocessing stages. To speed up training, in this year, we employ the MFN-SVM (modified finite-Newton method support vector machines) which can be learned in linear time. The experimental results show that our method achieved the middle rank over the 23 teams. We expect that our method could be further improved via well-tuned parameter validations for different languages.