This paper describes our system designed for the NLPCC 2016 shared task on word segmentation on micro-blog texts.
@cite_13: 
@cite_14: We address the issue of consuming heterogeneous annotation data for Chinese word segmentation and part-of-speech tagging. We empirically analyze the diversity between two representative corpora, i.e. Penn Chinese Treebank (CTB) and PKU's People's Daily (PPD), on manually mapped data, and show that their linguistic annotations are systematically different and highly compatible. The analysis is further exploited to improve processing accuracy by (1) integrating systems that are respectively trained on heterogeneous annotations to reduce the approximation error, and (2) re-training models with high quality automatically converted data to reduce the estimation error. Evaluation on the CTB and PPD data shows that our novel model achieves a relative error reduction of 11 over the best reported result in the literature.
@cite_4: We present a simple and effective framework for exploiting multiple monolingual treebanks with different annotation guidelines for parsing. Several types of transformation patterns (TP) are designed to capture the systematic annotation inconsistencies among different tree-banks. Based on such TPs, we design quasi-synchronous grammar features to augment the baseline parsing models. Our approach can significantly advance the state-of-the-art parsing accuracy on two widely used target tree-banks (Penn Chinese Treebank 5.1 and 6.0) using the Chinese Dependency Treebank as the source treebank. The improvements are respectively 1.37 and 1.10 with automatic part-of-speech tags. Moreover, an indirect comparison indicates that our approach also outperforms previous work based on treebank conversion.
@cite_7: 
@cite_8: In order to effectively utilize multiple datasets with heterogeneous annotations, this paper proposes a coupled sequence labeling model that can directly learn and infer two heterogeneous annotations simultaneously, and to facilitate discussion we use Chinese part-ofspeech (POS) tagging as our case study. The key idea is to bundle two sets of POS tags together (e.g. “[NN, n]”), and build a conditional random field (CRF) based tagging model in the enlarged space of bundled tags with the help of ambiguous labelings. To train our model on two non-overlapping datasets that each has only one-side tags, we transform a one-side tag into a set of bundled tags by considering all possible mappings at the missing side and derive an objective function based on ambiguous labelings. The key advantage of our coupled model is to provide us with the flexibility of 1) incorporating joint features on the bundled tags to implicitly learn the loose mapping between heterogeneous annotations, and 2) exploring separate features on one-side tags to overcome the data sparseness problem of using only bundled tags. Experiments on benchmark datasets show that our coupled model significantly outperforms the state-ofthe-art baselines on both one-side POS tagging and annotation conversion tasks. The codes and newly annotated data are released for non-commercial usage.1 ∗Correspondence author. http: hlt.suda.edu.cn zhli
@cite_2: 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 @math , 10K sentences, Penn Chinese Treebank 7.0 @math , 50K, and People's Daily @math , 280K. For WS, we adopt the recently proposed coupled sequence labeling to combine @math , @math , and @math , boosting F1 score from @math baseline model trained on only @math to @math @math . 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 @math into the annotation style of @math based on coupled sequence labeling, denoted by @math . Then, we merge CTB7 and @math to train a POS tagger, denoted by @math , which is further used to produce guide features on @math . Finally, the tagging F1 score is improved from 87.93 to 88.99 +1.06 .
@cite_5: Generally,statistical methods for Chinese Word Segmentation don't have good domain adaptability owing to the specific training corpus.In practice,domain dictionaries are more easily achieved than humanly annotated segmentation corpus,and it contains plenty of domain information.We propose an approach which integrates dictionary information into statistical models(i.e.,CRF model in this paper) to realize domain adaption for Chinese Word Segmentation.Experimental results show that our approach have good domain adaption.When the test corpus is identical to the domain of training corpus,the F-measure value increases 2 ;when test corpus is in a different domain of the training corpus,the F-measure value increases 6 .
@cite_15: Chinese word segmentation and part-ofspeech tagging (S&T) are fundamental steps for more advanced Chinese language processing tasks. Recently, it has attracted more and more research interests to exploit heterogeneous annotation corpora for Chinese S&T. In this paper, we propose a unified model for Chinese S&T with heterogeneous annotation corpora. We first automatically construct a loose and uncertain mapping between two representative heterogeneous corpora, Penn Chinese Treebank (CTB) and PKU’s People’s Daily (PPD). Then we regard the Chinese S&T with heterogeneous corpora as two “related” tasks and train our model on two heterogeneous corpora simultaneously. Experiments show that our method can boost the performances of both of the heterogeneous corpora by using the shared information, and achieves significant improvements over the state-of-the-art methods.
@cite_10: The large combined search space of joint word segmentation and Part-of-Speech (POS) tagging makes efficient decoding very hard. As a result, effective high order features representing rich contexts are inconvenient to use. In this work, we propose a novel stacked subword model for this task, concerning both efficiency and effectiveness. Our solution is a two step process. First, one word-based segmenter, one character-based segmenter and one local character classifier are trained to produce coarse segmentation and POS information. Second, the outputs of the three predictors are merged into sub-word sequences, which are further bracketed and labeled with POS tags by a fine-grained sub-word tagger. The coarse-to-fine search scheme is efficient, while in the sub-word tagging step rich contextual features can be approximately derived. Evaluation on the Penn Chinese Tree-bank shows that our model yields improvements over the best system reported in the literature.
@cite_11: Manually annotated corpora are valuable but scarce resources, yet for many annotation tasks such as treebanking and sequence labeling there exist multiple corpora with different and incompatible annotation guidelines or standards. This seems to be a great waste of human efforts, and it would be nice to automatically adapt one annotation standard to another. We present a simple yet effective strategy that transfers knowledge from a differently annotated corpus to the corpus with desired annotation. We test the efficacy of this method in the context of Chinese word segmentation and part-of-speech tagging, where no segmentation and POS tagging standards are widely accepted due to the lack of morphology in Chinese. Experiments show that adaptation from the much larger People's Daily corpus to the smaller but more popular Penn Chinese Treebank results in significant improvements in both segmentation and tagging accuracies (with error reductions of 30.2 and 14 , respectively), which in turn helps improve Chinese parsing accuracy.
Using external lexicon is first described in Pi-Chuan (2008) @cite_13 . (2012) @cite_5 find the lexicon features are also very helpful for domain adaptation of WS models, (2009) @cite_11 first propose the simple yet effective guild-feature based method, which is further extended in @cite_10 @cite_4 @cite_14 . (2013) @cite_15 propose a model that performs heterogeneous Chinese word segmentation and POS tagging and produces two sets of results following @math and @math styles respectively. Their model is based on linear perceptron, and uses approximate inference. (2015) @cite_8 first propose the coupled sequence labeling approach. , (2015) @cite_2 make extensive use of the coupled approach in participating the NLPCC 2015 shared task of WS &POS for Webo texts. , (2016) @cite_7 further improves the coupled approach in terms of efficiency via context-aware pruning, and first apply the coupled approach to the joint WS &POS task. In this work, we directly use the coupled model built in for converting the WS &POS annotations in PD into the style of CTB .