Exploiting Heterogeneous Annotations for Weibo Word Segmentation and POS Tagging

Abstract

This paper describes our system designed for the NLPCC 2015 shared task on Chinese word segmentation (WS) and POS tagging for Weibo Text. We treat WS and POS tagging as two separate tasks and use a cascaded approach. Our major focus is how to effectively exploit multiple heterogeneous data to boost performance of statistical models. This work considers three sets of heterogeneous data, i.e., Weibo ($$\textit{WB}$$, 10K sentences), Penn Chinese Treebank 7.0 ($$\textit{CTB7}$$, 50K), and People’s Daily ($$\textit{PD}$$, 280K). For WS, we adopt the recently proposed coupled sequence labeling to combine $$\textit{WB}$$, $$\textit{CTB7}$$, and $$\textit{PD}$$, boosting F1 score from $$93.76\%$$ (baseline model trained on only $$\textit{WB}$$) to $$95.58\%$$ ($$+1.82\%$$). For POS tagging, we adopt an ensemble approach combining coupled sequence labeling and the guide-feature based method, since the three datasets have three different annotation standards. First, we convert $$\textit{PD}$$ into the annotation style of $$\textit{CTB7}$$ based on coupled sequence labeling, denoted by $$\textit{PD}^{\textit{CTB}}$$. Then, we merge CTB7 and $$\textit{PD}^{\textit{CTB}}$$ to train a POS tagger, denoted by $$\textit{Tag}_{\textit{CTB7}+\textit{PD}^{\textit{CTB}}}$$, which is further used to produce guide features on $$\textit{WB}$$. Finally, the tagging F1 score is improved from 87.93% to 88.99% (+1.06%).