Easy-First Chinese POS Tagging and Dependency Parsing

Proceedings of COLING 2012: Technical Papers, pages 1731–1746, COLING 2012, Mumbai, December 2012. Easy-First Chinese POS Tagging and Dependency Parsing Ji Ma, Tong Xiao, Jingbo Zhu, Feiliang Ren Natural Language Processing Laboratory Northeastern University, China majineu@outlook.com, zhujingbo@mail.neu.edu.cn, xiaotong@mail.neu.edu.cn, renfeiliang@ics.neu.edu.cn ABSTRACT The easy-first non-directional dependency parser has demonstrated its advantage over transition based dependency parsers which parse sentences from left to right. This work investigates easy-first method on Chinese POS tagging, dependency parsing and joint tagging and dependency parsing. In particular, we generalize the easy-first dependency parsing algorithm to a general framework and apply this framework to Chinese POS tagging and dependency parsing. We then propose the first joint tagging and dependency parsing algorithm under the easy-first framework. We train the joint model with both supervised objective and additional loss which only relates to one of the individual tasks (either tagging or parsing). In this way, we can bias the joint model towards the preferred task. Experimental results show that both the tagger and the parser achieve state-of-the-art accuracy and runs fast. And our joint model achieves tagging accuracy of 94.27 which is the best result reported so far. KEYWORDS: dependency parsing, POS tagging, perceptron, easy-first 1731 1 Introduction To sequential labelling problems, such as POS tagging or incremental parsing, traditional approaches 1) decompose the input sequence into several individual items each of which corresponds to a token of the input sequence; 2) predict these items in a fixed left-to-right (or right-to-left) order (Collins 2002; Ratnaparkhi 1996). The drawback of such fixed order approach is that when predicting one item, only the labels on the left side can be used while the labels on the right side is still unavailable. (Goldberg and Elhadad, 2010) proposed the easy-first dependency parsing algorithm to relax the fixed left-to-right order and to incorporate more structural features from both sides of the attachment point. Comparing with a deterministic transition based parser which parses the sentence left-to-right, their deterministic easy-first parser achieves significant better accuracy. The key idea behind their parsing algorithm is to always process the easy attachments in the early stages. The hard ones are delayed to late stages until more structural features on both sides of the attachment point are accessible so as to make more informed decision. In this work, we further generalize the algorithm of (Goldberg and Elhadad, 2010) to a general sequential labelling framework. We apply this framework to Chinese POS tagging and dependency parsing which has never been studied under the easy-first framework before. One characteristic of Chinese dependency parsing is that parsing performance can be dramatically affected by the quality of POS tags of the input sentence (Li et al., 2010). Recent work (Li et al., 2010; Hatori et al., 2011; Bohnet and Nivre, 2012) empirically verified that solving POS tagging and dependency parsing jointly can boost the performance of both the two tasks. To further improve tagging and parsing accuracy, we also solve the two tasks jointly. While previous joint methods are either graph-based or transition-based algorithm, in …

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