Part-of-speech (POS) tagging plays an important role in Natural Language
Processing (NLP). Its applications can be found in many NLP tasks such as named
entity recognition, syntactic parsing, dependency parsing and text chunking. In
the investigation conducted in this paper, we utilize the technologies of two
widely-used toolkits, ClearNLP and Stanford POS Tagger, as well as develop two
new POS taggers for Vietnamese, then compare them to three well-known
Vietnamese taggers, namely JVnTagger, vnTagger and RDRPOSTagger. We make a
systematic comparison to find out the tagger having the best performance. We
also design a new feature set to measure the performance of the statistical
taggers. Our new taggers built from Stanford Tagger and ClearNLP with the new
feature set can outperform all other current Vietnamese taggers in term of
tagging accuracy. Moreover, we also analyze the affection of some features to
the performance of statistical taggers. Lastly, the experimental results also
reveal that the transformation-based tagger, RDRPOSTagger, can run
significantly faster than any other statistical tagger.

本研究中，我们使用了 ClearNLP 和 Stanford POS Tagger 这两个广泛使用的工具包，并开发了两个新的越南语 POS 标记器，然后将它们与三个众所周知的越南标记器 JVnTagger、vnTagger 和 RDRPOSTagger 进行比较。通过系统比较，我们发现，利用新特征集构建的越南语标记器可以在标记准确性方面胜过所有其他现有的越南标记器。此外，研究结果还表明，基于转换的标记器 RDRPOSTagger 的运行速度明显快于任何其他统计标记器。