Arbitrary Phrase Identification using Linear Kernel with Mask Method

In this paper, we proposed an efficient and accurate text chunking system using linear SVM kernel and a new technique called mask method. Previous researches indicated that systems combination or external parsers can highlight the chunking performance. However the cost of constructing multiclassifiers is even higher than developing a single processor. Besides, the use of external resources will complicate the original tagging process. To remedy these problems, we employ richer features and propose a masked-based method to solve unknown word problem to enhance system performance. In this way, no external resources and complex heuristics are necessary for the chunking system. The experiments show that when training with the CoNLL-2000 chunking data set, our system achieves 94.12 in F(β) rate and 94.21 with SVM POS-tagger. Furthermore, our chunker is quite efficient since it adopts linear kernel SVM. The turn around tagging time on CoNLL-2000 testing data is less than 52 seconds.