Language Resources and Evaluation

, Volume 49, Issue 3, pp 487–519 | Cite as

Vietnamese treebank construction and entropy-based error detection

  • Phuong-Thai Nguyen
  • Anh-Cuong Le
  • Tu-Bao Ho
  • Van-Hiep Nguyen
Original Paper


Treebanks, especially the Penn treebank for natural language processing (NLP) in English, play an essential role in both research into and the application of NLP. However, many languages still lack treebanks and building a treebank can be very complicated and difficult. This work has a twofold objective. Firstly, to share our results in constructing a large Vietnamese treebank (VTB) with three levels of annotation including word segmentation, part-of-speech tagging, and syntactic analysis. Major steps in the treebank construction process are described with particular regard to specific Vietnamese properties such as lack of word delimiter and isolation. Those properties make sentences highly syntactically ambiguous, and therefore it is difficult to ensure a high level of agreement among annotators. Various studies of Vietnamese syntax were employed not only to define annotations but also to systematically deal with ambiguities. Annotators were supported by automatic labelling tools, which are based on statistical machine learning methods, for sentence pre-processing and a tree editor for supporting manual annotation. As a result, an annotation agreement of around 90 % was achieved. Our second objective is to present our method for automatically finding errors and inconsistencies in treebank corpora and its application to the construction of the VTB. This method employs the Shannon entropy measure in a manner that the more reduced entropy the more corrected errors in a treebank. The method ranks error candidates by using a scoring function based on conditional entropy. Our experiments showed that this method detected high-error-density subsets of original error candidate sets, and that the corpus entropy was significantly reduced after error correction. The size of these subsets was only about one third of the whole set, while these subsets contained 80–90 % of the total errors. This method can also be applied to languages similar to Vietnamese.


Treebank Error detection Entropy 



This paper is supported by the project QGTĐ.12.21 funded by Vietnam National University, Hanoi. We would like to express special thanks to other members of the treebank development team Xuan-Luong Vu and Dr. Thi-Minh-Huyen Nguyen, and linguistic annotators Minh-Thu Dao, Thi-Minh-Ngoc Nguyen, Kim-Ngan Le, Mai-Van Nguyen for the effective cooperation. We also would like to express thanks to Assoc. Prof. Dinh Dien for his comments and discussions during the early stages of the treebank development.


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Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Phuong-Thai Nguyen
    • 1
  • Anh-Cuong Le
    • 1
  • Tu-Bao Ho
    • 2
  • Van-Hiep Nguyen
    • 3
  1. 1.University of Engineering and Technology, Vietnam National UniversityHanoiVietnam
  2. 2.Japan Advanced Institute of Science and TechnologyNomiJapan
  3. 3.Institute of LinguisticsVietnam Academy of Social SciencesHanoiVietnam

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