Neural word segmentation research has benefited from large-scale raw texts by leveraging them for pretraining character and word embeddings. On the other hand, statistical segmentation research has exploited richer sources of external information, such as punctuation, automatic segmentation and POS. We investigate the effectiveness of a range of external training sources for neural word segmentation by building a modular segmentation model, pretraining the most important submodule using rich external sources. Results show that such pretraining significantly improves the model, leading to accuracies competitive to the best methods on six benchmarks.
This paper is concerned with Chinese word segmentation, which is regarded as a character based tagging problem under conditional random field framework. It is different in our method that we consider both feature template selection and tag set selection, instead of feature template focused only method in existing work. Thus, there comes an empirical comparison study of performance among different tag sets in this paper. We show that there is a significant performance difference as different tag sets are selected. Based on the proposed method, our system gives the state-of-the-art performance. There are two dominant approaches to Chinese word segmentation: word-based and character-based models, each with respective strengths. Prior work has shown that gains in segmentation performance can be achieved from combining these two types of models; however, past efforts have not provided a practical technique to allow mainstream adoption. We propose a method that effectively combines the strength of both segmentation schemes using an efficient dual-decomposition algorithm for joint inference. Our method is simple and easy to implement. Experiments on SIGHAN 2003 and 2005 evaluation datasets show that our method achieves the best reported results to date on 6 out of 7 datasets. Nowadays supervised sequence labeling models can reach competitive performance on the task of Chinese word segmentation. However, the ability of these models is restricted by the availability of annotated data and the design of features. We propose a scalable semi-supervised feature engineering approach. In contrast to previous works using pre-defined taskspecific features with fixed values, we dynamically extract representations of label distributions from both an in-domain corpus and an out-of-domain corpus. We update the representation values with a semi-supervised approach. Experiments on the benchmark datasets show that our approach achieve good results and reach an f-score of 0.961. The feature engineering approach proposed here is a general iterative semi-supervised method and not limited to the word segmentation task. In this paper we report results of a supervised machine-learning approach to Chinese word segmentation. A maximum entropy tagger is trained on manually annotated data to automatically assign to Chinese characters, or hanzi, tags that indicate the position of a hanzi within a word. The tagged output is then converted into segmented text for evaluation. Preliminary results show that this approach is competitive against other supervised machine-learning segmenters reported in previous studies, achieving precision and recall rates of 95.01 and 94.94 respectively, trained on a 237K-word training set. 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 present a joint model for Chinese word segmentation and new word detection. We present high dimensional new features, including word-based features and enriched edge (label-transition) features, for the joint modeling. As we know, training a word segmentation system on large-scale datasets is already costly. In our case, adding high dimensional new features will further slow down the training speed. To solve this problem, we propose a new training method, adaptive online gradient descent based on feature frequency information, for very fast online training of the parameters, even given large-scale datasets with high dimensional features. Compared with existing training methods, our training method is an order magnitude faster in terms of training time, and can achieve equal or even higher accuracies. The proposed fast training method is a general purpose optimization method, and it is not limited in the specific task discussed in this paper. We report an empirical investigation on type-supervised domain adaptation for joint Chinese word segmentation and POS-tagging, making use of domainspecific tag dictionaries and only unlabeled target domain data to improve target-domain accuracies, given a set of annotated source domain sentences. Previous work on POS-tagging of other languages showed that type-supervision can be a competitive alternative to tokensupervision, while semi-supervised techniques such as label propagation are important to the effectiveness of typesupervision. We report similar findings using a novel approach for joint Chinese segmentation and POS-tagging, under a cross-domain setting. With the help of unlabeled sentences and a lexicon of 3,000 words, we obtain 33 error reduction in target-domain tagging. In addition, combined type- and token-supervision can lead to improved cost-effectiveness. 