Chinese Part-of-speech Tagging Research Articles

Word‐Based Method for Chinese Part‐of‐Speech via Parallel and Adversarial Network

Chinese part-of-speech (POS) tagging is an essential task for Chinese downstream natural language processing tasks. The accuracy of the Chinese POS task will drop dramatically by word-based methods because of the segmentation errors and the word sparsity. Also, there are several Chinese POS tagging sets with different criteria. Some of them only have a small-scale annotated corpus and are hard to train. To this end, we propose a modified word-based transformer neural network architecture. Meanwhile, we utilize an adversarial transfer learning method that splits the architecture into shared and private parts. This work directly improves the ability of the word-based model, instead of adopting a joint character-based method. Extensive experiments show that our method achieves state-of-the-art performance on all datasets, and more importantly, our method improves performance effectively for the word-based Chinese sequence labeling task.

Joint Optimization for Chinese POS Tagging and Dependency Parsing

Dependency parsing has gained more and more interest in natural language processing in recent years due to its simplicity and general applicability for diverse languages. Previous work demonstrates that part-of-speech (POS) is an indispensable feature in dependency parsing since pure lexical features suffer from serious data sparseness problem. However, due to little morphological changes, Chinese POS tagging has proven to be much more challenging than morphology-richer languages such as English (94% vs. 97% on POS tagging accuracy). This leads to severe error propagation for Chinese dependency parsing. Our experiments show that parsing accuracy drops by about 6% when replacing manual POS tags of the input sentence with automatic ones generated by a state-of-the-art statistical POS tagger. To address this issue, this paper proposes a solution by jointly optimizing POS tagging and dependency parsing in a unique model. We propose for our joint models several dynamic programming based decoding algorithms which can incorporate rich POS tagging and syntactic features. Then we present an effective pruning strategy to reduce the search space of candidate POS tags, leading to significant improvement of parsing speed. Experimental results on two Chinese data sets, i.e. Penn Chinese Treebank 5.1 and Penn Chinese Treebank 7, demonstrate that our joint models significantly improve both the state-of-the-art tagging and parsing accuracies. Detailed analysis shows that the joint method can help resolve syntax-sensitive POS ambiguities <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\{{\ssr{NN}},{\ssr{VV}}\}$</tex> </formula> . In return, the POS tags become more reliable and helpful for parsing since the syntactic features are used in POS tagging. This is the fundamental reason for the performance improvement.

Chinese POS Disambiguation and Unknown Word Guessing with Lexicalized HMMs

This article presents a lexicalized HMM-based approach to Chinese part-of-speech (POS) disambiguation and unknown word guessing (UWG). In order to explore word-internal morphological features for Chinese POS tagging, four types of pattern tags are defined to indicate the way lexicon words are used in a segmented sentence. Such patterns are combined further with POS tags. Thus, Chinese POS disambiguation and UWG can be unified as a single task of assigning each known word to input a proper hybrid tag. Furthermore, a uniformly lexicalized HMM-based tagger also is developed to perform this task, which can incorporate both internal word-formation patterns and surrounding contextual information for Chinese POS tagging under the framework of HMMs. Experiments on the Peking University Corpus indicate that the tagging precision can be improved with efficiency by the proposed approach.