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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(More)
Phrase pattern recognition (phrase chunking) refers to automatic approaches for identifying predefined phrase structures in a stream of text. Support vector machines (SVMs)-based methods had shown excellent performance in many sequential text pattern recognition tasks such as protein name finding, and noun phrase (NP)-chunking. Even though they yield very(More)
In Chinese, most of the language processing starts from word segmentation and part-of-speech (POS) tagging. These two steps tokenize the word from a sequence of characters and predict the syntactic labels for each segmented word. In this paper , we present two distinct sequential tagging models for the above two tasks. The first word segmentation model was(More)
Data-driven learning based on shift reduce parsing algorithms has emerged dependency parsing and shown excellent performance to many Tree-banks. In this paper, we investigate the extension of those methods while considerably improved the runtime and training time efficiency via L 2-SVMs. We also present several properties and constraints to enhance the(More)
Classification is an important and well-known technique in the field of machine learning, and the training data will significantly influence the classification accuracy. However, the training data in real-world applications often are imbalanced class distribution. It is important to select the suitable training data for classification in the imbalanced(More)