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| Content Provider | ACM Digital Library |
|---|---|
| Author | Zhu, Jingbo Xiao, Tong Zhu, Muhua |
| Copyright Year | 2011 |
| Abstract | Treebanks are valuable resources for syntactic parsing. For some languages such as Chinese, we can obtain multiple constituency treebanks which are developed by different organizations. However, due to discrepancies of underlying annotation standards, such treebanks in general cannot be used together through direct data combination. To enlarge training data for syntactic parsing, we focus in this article on the challenge of unifying standards of disparate treebanks by automatically converting one treebank (source treebank) to fit a different standard which is exhibited by another treebank (target treebank). We propose to convert a treebank in two sequential steps which correspond to the part-of-speech level and syntactic structure level (including tree structures and grammar labels), respectively. Approaches used in both levels can be unified as an informed decoding procedure, where information derived from original annotation in a source treebank is used to guide the conversion conducted by a POS tagger (or a parser in the syntactic structure level) trained on a target treebank. We take two Chinese treebanks as a case study, and experiments on these two treebanks show significant improvements in conversion accuracy over baseline systems, especially in situations where a target treebank is small in size. |
| Starting Page | 1 |
| Ending Page | 24 |
| Page Count | 24 |
| File Format | |
| ISSN | 15300226 |
| e-ISSN | 15583430 |
| DOI | 10.1145/2002980.2002982 |
| Volume Number | 10 |
| Issue Number | 3 |
| Journal | ACM Transactions on Asian Language Information Processing (TALIP) |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2011-09-01 |
| Publisher Place | New York |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Chinese POS tagging Chinese syntactic parsing Informed decoding Treebank conversion |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Science |
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