text: Chinese word segmentation and dependency parsing are two fundamental tasks for Chinese natural language processing. The dependency parsing is defined on word-level, therefore word segmentation is the precondition of dependency parsing, which makes dependency parsing suffers from error propagation. In this paper, we propose a unified model to integrate Chinese word segmentation and dependency parsing. Different from previous joint models, our proposed model is a graph-based model and more concise, which results in fewer efforts of feature engineering. Our joint model achieves better performance than previous joint models. Our joint model achieves the state-of-the-art results in both Chinese word segmentation and dependency parsing.
  @cite_0: For Chinese POS tagging, word segmentation is a preliminary step. To avoid error propagation and improve segmentation by utilizing POS information, segmentation and tagging can be performed simultaneously. A challenge for this joint approach is the large combined search space, which makes efficient decoding very hard. Recent research has explored the integration of segmentation and POS tagging, by decoding under restricted versions of the full combined search space. In this paper, we propose a joint segmentation and POS tagging model that does not impose any hard constraints on the interaction between word and POS information. Fast decoding is achieved by using a novel multiple-beam search algorithm. The system uses a discriminative statistical model, trained using the generalized perceptron algorithm. The joint model gives an error reduction in segmentation accuracy of 14.6 and an error reduction in tagging accuracy of 12.2 , compared to the traditional pipeline approach.
  @cite_15: Chinese part-of-speech (POS) tagging assigns one POS tag to each word in a Chinese sentence. However, since words are not demarcated in a Chinese sentence, Chinese POS tagging requires word segmentation as a prerequisite. We could perform Chinese POS tagging strictly after word segmentation (one-at-a-time approach), or perform both word segmentation and POS tagging in a combined, single step simultaneously (all-atonce approach). Also, we could choose to assign POS tags on a word-by-word basis, making use of word features in the surrounding context (word-based), or on a character-by-character basis with character features (character-based). This paper presents an in-depth study on such issues of processing architecture and feature representation for Chinese POS tagging, within a maximum entropy framework. We found that while the all-at-once, characterbased approach is the best, the one-at-a-time, character-based approach is a worthwhile compromise, performing only slightly worse in terms of accuracy, but taking shorter time to train and run. As part of our investigation, we also built a state-of-the-art Chinese word segmenter, which outperforms the best SIGHAN 2003 word segmenters in the closed track on 3 out of 4 test corpora.
  @cite_6: We show that the standard beam-search algorithm can be used as an efficient decoder for the global linear model of Zhang and Clark (2008) for joint word segmentation and POS-tagging, achieving a significant speed improvement. Such decoding is enabled by: (1) separating full word features from partial word features so that feature templates can be instantiated incrementally, according to whether the current character is separated or appended; (2) deciding the POS-tag of a potential word when its first character is processed. Early-update is used with perceptron training so that the linear model gives a high score to a correct partial candidate as well as a full output. Effective scoring of partial structures allows the decoder to give high accuracy with a small beam-size of 16. In our 10-fold cross-validation experiments with the Chinese Tree-bank, our system performed over 10 times as fast as Zhang and Clark (2008) with little accuracy loss. The accuracy of our system on the standard CTB 5 test was competitive with the best in the literature.
summary: Since the segmentation is a character-level task and POS tagging is a word-level task, an intuitive idea is to transfer both the tasks into character-level and incorporate them in a uniform framework. A popular method is to assign a cross-tag to each character @cite_15 . The cross-tag is composed of a word boundary part and a POS part, e.g., B-NN'' refers to the first character in a word with POS tag NN''. Thus, the joint CWS and POS tagging can be regarded as a sequence labeling problem. Following this work, utilized neural models to alleviate the efforts of feature engineering. Another line of the joint segmentation and POS tagging method is transition-based method @cite_0 @cite_6 , in which the joint decoding process is regarded as a sequence of action predictions. used a simple yet effective sequence-to-sequence neural model to improve the performance of the transition-based method.