NJU-Parser: Achievements on Semantic Dependency Parsing

First Joint Conference on Lexical and Computational Semantics (*SEM), pages 519–523, Montr´eal, Canada, June 7-8, 2012. c⃝2012 Association for Computational Linguistics NJU-Parser: Achievements on Semantic Dependency Parsing Guangchao Tang1 Bin Li1,2 Shuaishuai Xu1 Xinyu Dai1 Jiajun Chen1 1 State Key Lab for Novel Software Technology, Nanjing University 2 Research Center of Language and Informatics, Nanjing Normal University Nanjing, Jiangsu, China {tanggc, lib, xuss, dxy, chenjj}@nlp.nju.edu.cn Abstract In this paper, we introduce our work on SemEval-2012 task 5: Chinese Semantic De- pendency Parsing. Our system is based on MSTParser and two effective methods are proposed: splitting sentence by punctuations and extracting last character of word as lemma. The experiments show that, with a combina- tion of the two proposed methods, our system can improve LAS about one percent and final- ly get the second prize out of nine participat- ing systems. We also try to handle the multi- level labels, but with no improvement. 1 Introduction Task 5 of SemEval-2012 tries to find approaches to improve Chinese sematic dependency parsing (SDP). SDP is a kind of dependency parsing. Cur- rently, there are many dependency parsers availa- ble, such as Eisner’s probabilistic dependency parser (Eisner, 1996), McDonald’s MSTParser (McDonald et al. 2005a; McDonald et al. 2005b) and Nivre’s MaltParser (Nivre, 2006). Despite of elaborate models, lots of problems still exist in dependency parsing. For example, sen- tence length has been proved to show great impact on the parsing performance. (Li et al., 2010) used a two-stage approach based on sentence fragment for high-order graph-based dependency parsing. Lack- ing of linguistic knowledge is also blamed. Three methods are promoted in this paper try- ing to improve the performance: splitting sentence by commas and semicolons, extracting last charac- ter of word as lemma and handling multi-level la- bels. Improvements could be achieved through the first two methods while not for the third. 2 Overview of Our System Our system is based on MSTParser which is one of the state-of-the-art parsers. MSTParser tries to ob- tain the maximum spanning tree of a sentence. For projective parsing task, it takes Eisner’s algorithm (Eisner, 1996) to get the dependency tree in O(n3) time. Meanwhile, Chu-Liu-Edmond’s algorithm (Chu and Liu, 1965) is applied for non-projective task, which takes O(n2) time. Three methods are adopted to MSTParser in our system: 1) Sentences are split into sub-sentences by commas and semicolons, for which there are two ways. Splitting sentences by all commas and semicolons is used in our primary system. In our contrast system, we use a classifier to determine whether a comma or semicolon can be used to split the sentence. In the primary and contrast system, the proto sentences and the sub- sentences are trained and tested separately and the outputs are merged in the end. 2) In a Chinese word, the last character usual- ly contains main sense or semantic class. We treat the last character of the word as word lemma and find it gets a slightly im- provement in the experiment. 3) An experiment trying …

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