Neural word segmentation has attracted more and more research interests for
its ability to alleviate the effort of feature engineering and utilize the
external resource by the pre-trained character or word embeddings. In this
paper, we propose a new neural model to incorporate the word-level information
for Chinese word segmentation. Unlike the previous word-based models, our model
still adopts the framework of character-based sequence labeling, which has
advantages on both effectiveness and efficiency at the inference stage. To
utilize the word-level information, we also propose a new long short-term
memory (LSTM) architecture over directed acyclic graph (DAG). Experimental
results demonstrate that our model leads to better performances than the
baseline models.

本文介绍了一种新型神经模型，通过在有向无环图上使用长短时记忆网络来整合单词级别信息，结合预训练的字符或单词嵌入向量，实现了比基准模型更好的中文分词表现。

基于有向无环图的长短时记忆网络用于语言分词

DAG-based Long Short-Term Memory for Neural Word Segmentation

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.

通过使用更丰富的外部信息来预先训练模块化分词模型，神经词分割的效果得到了显著的提高，在六项基准测试中达到了竞争最佳方法的准确度。