This paper describes our system designed for the NLPCC 2016 shared task on word segmentation on micro-blog texts.
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 .
@cite_13 @cite_14 @cite_4 @cite_7 @cite_8 @cite_2 @cite_5 @cite_15 @cite_10 @cite_11
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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 . This paper describes our system designed for the NLPCC 2016 shared task on word segmentation on micro-blog texts (i.e., Weibo). We treat word segmentation as a character-wise sequence labeling problem, and explore two directions to enhance our CRF-based baseline. First, we employ a large-scale external lexicon for constructing extra lexicon features in the model, which is proven to be extremely useful. Second, we exploit two heterogeneous datasets, i.e., Penn Chinese Treebank 7 (CTB7) and People Daily (PD) to help word segmentation on Weibo. We adopt two mainstream approaches, i.e., the guide-feature based approach and the recently proposed coupled sequence labeling approach. We combine the above techniques in different ways and obtain four well-performing models. Finally, we merge the outputs of the four models and obtain the final results via Viterbi-based re-decoding. On the test data of Weibo, our proposed approach outperforms the baseline by (95.63-94.24=1.39 ) in terms of F1 score. Our final system rank the first place among five participants in the open track in terms of F1 score, and is also the best among all 28 submissions. All codes, experiment configurations, and the external lexicon are released at http: hlt.suda.edu.cn zhli. We propose the first joint model for word segmentation, POS tagging, and dependency parsing for Chinese. Based on an extension of the incremental joint model for POS tagging and dependency parsing (, 2011), we propose an efficient character-based decoding method that can combine features from state-of-the-art segmentation, POS tagging, and dependency parsing models. We also describe our method to align comparable states in the beam, and how we can combine features of different characteristics in our incremental framework. In experiments using the Chinese Treebank (CTB), we show that the accuracies of the three tasks can be improved significantly over the baseline models, particularly by 0.6 for POS tagging and 2.4 for dependency parsing. We also perform comparison experiments with the partially joint models. 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.