Exploiting Heterogeneous Treebanks for Parsing

12502

Authors

nan

Abstract

We address the issue of using heteroge- neous treebanks for parsing by breaking it down into two sub-problems, convert- ing grammar formalisms of the treebanks to the same one, and parsing on these homogeneous treebanks. First we pro- pose to employ an iteratively trained tar- get grammar parser to perform grammar formalism conversion, eliminating prede- fined heuristic rules as required in previ- ous methods. Then we provide two strate- gies to refine conversion results, and adopt a corpus weighting technique for parsing on homogeneous treebanks. Results on the Penn Treebank show that our conversion method achieves 42% error reduction over the previous best result. Evaluation on the Penn Chinese Treebank indicates that a converted dependency treebank helps con- stituency parsing and the use of unlabeled data by self-training further increases pars- ing f-score to 85.2%, resulting in 6% error reduction over the previous best result. 1

Keywords

nan

Full Content

Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP, pages 46–54, Suntec, Singapore, 2-7 August 2009. c⃝2009 ACL and AFNLP Exploiting Heterogeneous Treebanks for Parsing Zheng-Yu Niu, Haifeng Wang, Hua Wu Toshiba (China) Research and Development Center 5/F., Tower W2, Oriental Plaza, Beijing, 100738, China {niuzhengyu,wanghaifeng,wuhua}@rdc.toshiba.com.cn Abstract We address the issue of using heteroge- neous treebanks for parsing by breaking it down into two sub-problems, convert- ing grammar formalisms of the treebanks to the same one, and parsing on these homogeneous treebanks. First we pro- pose to employ an iteratively trained tar- get grammar parser to perform grammar formalism conversion, eliminating prede- fined heuristic rules as required in previ- ous methods. Then we provide two strate- gies to refine conversion results, and adopt a corpus weighting technique for parsing on homogeneous treebanks. Results on the Penn Treebank show that our conversion method achieves 42% error reduction over the previous best result. Evaluation on the Penn Chinese Treebank indicates that a converted dependency treebank helps con- stituency parsing and the use of unlabeled data by self-training further increases pars- ing f-score to 85.2%, resulting in 6% error reduction over the previous best result. 1 Introduction The last few decades have seen the emergence of multiple treebanks annotated with different gram- mar formalisms, motivated by the diversity of lan- guages and linguistic theories, which is crucial to the success of statistical parsing (Abeille et al., 2000; Brants et al., 1999; Bohmova et al., 2003; Han et al., 2002; Kurohashi and Nagao, 1998; Marcus et al., 1993; Moreno et al., 2003; Xue et al., 2005). Availability of multiple treebanks cre- ates a scenario where we have a treebank anno- tated with one grammar formalism, and another treebank annotated with another grammar formal- ism that we are interested in. We call the first a source treebank, and the second a target tree- bank. We thus encounter a problem of how to use these heterogeneous treebanks for target gram- mar parsing. Here heterogeneous treebanks refer to two or more treebanks with different grammar formalisms, e.g., one treebank annotated with de- pendency structure (DS) and the other annotated with phrase structure (PS). It is important to acquire additional labeled data for the target grammar parsing through exploita- tion of existing source treebanks since there is of- ten a shortage of labeled data. However, to our knowledge, there is no previous study on this is- sue. Recently there have been some works on us- ing multiple treebanks for domain adaptation of parsers, where these treebanks have the same grammar formalism (McClosky et al., 2006b; Roark and Bacchiani, 2003). Other related works focus on converting one grammar formalism of a treebank to another and then conducting studies on the converted treebank (Collins et al., 1999; Forst, 2003; Wang et al., 1994; Watkinson and Manand- har, 2001). These works were done either on mul- tiple treebanks with the same grammar formalism or on only one converted treebank. We see that their scenarios …

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Labeled Text

We address the issue of using heterogeneous treebanks for parsing by breaking it down into two sub-problems, converting grammar formalisms of the treebanks to the same one, and parsing on these homogeneous treebanks. First we propose to employ an iteratively trained target grammar parser to perform grammar formalism conversion, eliminating predefined heuristic rules as required in previous methods. Then we provide two strategies to refine conversion results, and adopt a corpus weighting technique for parsing on homogeneous treebanks. Results on the Penn Treebank show that our conversion method achieves 42% error reduction over the previous best result. Evaluation on the Penn Chinese Treebank indicates that a converted dependency treebank helps constituency parsing and the use of unlabeled data by self-training further increases parsing f-score to 85.2%, resulting in 6% error reduction over the previous best result. 1

Publication Details

Paper ID:
12502
Year:
2009
Publication:
TIPSTER
Publication Type:
conf
DOI:
nan