Semi-Markov conditional random fields (Semi-CRFs) have been successfully utilized in many segmentation problems, including Chinese word segmentation (CWS). The advantage of Semi-CRF lies in its inherent ability to exploit properties of segments instead of individual elements of sequences. Despite its theoretical advantage, Semi-CRF is still not the best choice for CWS because its computation complexity is quadratic to the sentence’s length. In this paper, we propose a simple yet effective framework to help Semi-CRF achieve comparable performance with CRF-based models under similar computation complexity. Specifically, we first adopt a bi-directional long short-term memory (BiLSTM) on character level to model the context information, and then use simple but effective fusion layer to represent the segment information. Besides, to model arbitrarily long segments within linear time complexity, we also propose a new model named Semi-CRF-Relay. The direct modeling of segments makes the combination with word features easy and the CWS performance can be enhanced merely by adding publicly available pre-trained word embeddings. Experiments on four popular CWS datasets show the effectiveness of our proposed methods. The source codes and pre-trained embeddings of this paper are available on https://github.com/fastnlp/fastNLP/ .

中文翻译：

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基于中继节点的 BiLSTM+Semi-CRF 中文分词

半马尔可夫条件随机场 (Semi-CRF) 已成功用于许多分词问题，包括中文分词 (CWS)。Semi-CRF 的优势在于其固有的利用片段属性而不是序列的单个元素的能力。尽管具有理论上的优势，但 Semi-CRF 仍然不是 CWS 的最佳选择，因为它的计算复杂度与句子长度成二次方。在本文中，我们提出了一个简单而有效的框架，以帮助 Semi-CRF 在相似的计算复杂度下实现与基于 CRF 的模型相当的性能。具体来说，我们首先在字符级别采用双向长短期记忆（BiLSTM）对上下文信息进行建模，然后使用简单但有效的融合层来表示片段信息。除了，为了在线性时间复杂度内对任意长的段进行建模，我们还提出了一种名为 Semi-CRF-Relay 的新模型。段的直接建模使与词特征的组合变得容易，并且仅通过添加公开可用的预训练词嵌入就可以增强 CWS 性能。在四个流行的 CWS 数据集上的实验表明了我们提出的方法的有效性。本文的源代码和预训练嵌入可在 https://github.com/fastnlp/fastNLP/ 上获得。在四个流行的 CWS 数据集上的实验表明了我们提出的方法的有效性。本文的源代码和预训练嵌入可在 https://github.com/fastnlp/fastNLP/ 上获得。在四个流行的 CWS 数据集上的实验表明了我们提出的方法的有效性。本文的源代码和预训练嵌入可在 https://github.com/fastnlp/fastNLP/ 上获得。