Chinese POS Tagging Research Articles

A Data-Driven Model for Automated Chinese Word Segmentation and POS Tagging.

Chinese natural language processing tasks often require the solution of Chinese word segmentation and POS tagging problems. Traditional Chinese word segmentation and POS tagging methods mainly use simple matching algorithms based on lexicons and rules. The simple matching or statistical analysis requires manual word segmentation followed by POS tagging, which leads to the inability to meet the practical requirements for label prediction accuracy. With the continuous development of deep learning technology, data-driven machine learning models provide new opportunities for automated Chinese word segmentation and POS tagging. Therefore, a data-driven automated Chinese word segmentation and POS tagging model is proposed in order to address the above problems. Firstly, the main idea and overall framework of the proposed automated model are outlined, and the tagging strategy and neural network language model used are described. Secondly, two main optimisations are made on the input side of the model: (1) the use of word2Vec for the representation of text features, thus representing the text as a distributed word vector; and (2) the use of an improved AlexNet for efficient encoding of long-range word, and the addition of an attention mechanism to the model. Finally, on the output side, an additional auxiliary loss function was designed to optimise the Chinese text based on its frequency. The experimental results show that the proposed model can significantly improve the accuracy and operational efficiency of Chinese word segmentation and POS tagging compared with other existing models, thus verifying its effectiveness and advancement.

Encoding multi-granularity structural information for joint Chinese word segmentation and POS tagging

Abstract Recent studies show that the joint Chinese word segmentation and POS tagging can enhance the mutual interaction and yield better performances for two tasks. However, existing joint methods fail to effectively take the advantage of the multiple granularity of information, e.g., character, word and subword, which has been proven prominently useful. In this paper, we propose to improve the joint tasks by leveraging such multi-granularity of information, by exploiting the lattice-LSTM and Convolutional Network (GCN) models for effectively encoding the graph information. On five benchmark datasets our proposed model shows highly competitive performances, achieving the new state-of-the-art results in the literature. Further analysis reveals that the multi-granularity information can relieve the out-of-vocabulary and the long-range dependency issues. Also the GCN structure is more effective for encoding the multi-granularity graph information, compared with the lattice structure.

Lattice LSTM for Chinese Sentence Representation

Words provide a useful source of information for Chinese NLP, and word segmentation has been taken as a pre-processing step for most downstream tasks. For many NLP tasks, however, word segmentation can introduce noise and lead to error propagation. The rise of neural representation learning models allows sentence-level semantic information to be collected from characters directly. As a result, it is an empirical question whether a fully character-based model should be used instead of first performing word segmentation. We investigate a neural representation that simultaneously encodes character and word information without the need for segmentation. In particular, candidate words are found in a sentence by matching with a pre-defined lexicon. A lattice structured LSTM is used to encode the resulting word-character lattice, where gate vectors are used to control information flow through words, so that the more useful words can be automatically identified by end-to-end training. We compare the performance of the resulting lattice LSTM and baseline sequence LSTM structures over both character sequences and automatically segmented word sequences. Results on NER show that the character-word lattice model can significantly improve the performance. In addition, as a general sentence representation architecture, character-word lattice LSTM can also be used for learning contextualized representations. To this end, we compare lattice LSTM structure with its sequential LSTM counterpart, namely ELMo. Results show that our lattice version of ELMo gives better language modeling performances. On Chinese POS-tagging, chunking and syntactic parsing tasks, the resulting contextualized Chinese embeddings also give better performance than ELMo trained on the same data.

A Simple and Effective Neural Model for Joint Word Segmentation and POS Tagging

Joint models have shown stronger capabilities for Chinese word segmentation and POS tagging, and have received great interests in the community of Chinese natural language processing. In this paper, we follow this line of work, presenting a simple yet effective sequence-to-sequence neural model for the joint task, based on a well-defined transition system, by using long short term memory neural network structures. We conduct experiments on five different datasets. The results demonstrate that our proposed model is highly competitive. By using well-trained character-level embeddings, the proposed neural joint model is able to obtain the best-reported performances in the literature.

Towards Accurate and Efficient Chinese Part-of-Speech Tagging

From the perspective of structural linguistics, we explore paradigmatic and syntagmatic lexical relations for Chinese POS tagging, an important and challenging task for Chinese language processing. Paradigmatic lexical relations are explicitly captured by word clustering on large-scale unlabeled data and are used to design new features to enhance a discriminative tagger. Syntagmatic lexical relations are implicitly captured by syntactic parsing in the constituency formalism, and are utilized via system combination. Experiments on the Penn Chinese Treebank demonstrate the importance of both paradigmatic and syntagmatic relations. Our linguistically motivated, hybrid approaches yield a relative error reduction of 18% in total over state-of-the-art baselines. Despite the effectiveness to boost accuracy, computationally expensive parsers make hybrid systems inappropriate for many realistic NLP applications. In this article, we are also concerned with improving tagging efficiency at test time. In particular, we explore unlabeled data to transfer the predictive power of hybrid models to simple sequence models. Specifically, hybrid systems are utilized to create large-scale pseudo training data for cheap models. Experimental results illustrate that the re-compiled models not only achieve high accuracy with respect to per token classification, but also serve as a front-end to a parser well.

Simple Semi-supervised Learning for Chinese Word Segmentation and Pos Tagging

Simple Semi-supervised Learning for Chinese Word Segmentation and Pos Tagging

Joint Chinese Word Segmentation and POS Tagging System with Undirected Graphical Models

在中文词法分析中，分词是词性标注必须经历的阶段。为了能在分词阶段就充分利用词性标注的信息和减少两阶段错误的累计，最好的方法是将两个阶段，整合到一个架构中。该文以无向图模型为基础，将分词和词性标注有机地统一在一个序列标注模型中。由于可以采用更深层次的依赖关系作为特征，一体化系统在1998年人民日报语料上取得了97.19%的分词精确率和95.34%的词性标注精确率，是目前同类系统，在这一语料上取得的最好结果。

Joint Chinese Word Segmentation and POS Tagging Using an Error-Driven Word-Character Hybrid Model

In this paper, we present a discriminative word-character hybrid model for joint Chinese word segmentation and POS tagging. Our word-character hybrid model offers high performance since it can handle both known and unknown words.We describe our strategies that yield good balance for learning the characteristics of known and unknown words and propose an error-driven policy that delivers such balance by acquiring examples of unknown words from particular errors in a training corpus. We describe an efficient framework for training our model based on the Margin Infused Relaxed Algorithm (MIRA), evaluate our approach on the Penn Chinese Treebank, and show that it achieves superior performance compared to the state-of-the-art approaches reported in the literature.