Cross-domain Chinese Word Segmentation (CWS) remains a challenge despite recent progress in neural-based CWS. The limited amount of annotated data in the target domain has been the key obstacle to a satisfactory performance. In this paper, we propose a semi-supervised word-based approach to improving cross-domain CWS given a baseline segmenter. Particularly, our model only deploys word embeddings trained on raw text in the target domain, discarding complex hand-crafted features and domain-specific dictionaries. Innovative subsampling and negative sampling methods are proposed to derive word embeddings optimized for CWS. We conduct experiments on five datasets in special domains, covering domains in novels, medicine, and patent. Results show that our model can obviously improve cross-domain CWS, especially in the segmentation of domain-specific noun entities. The word F-measure increases by over 3.0 on four datasets, outperforming state-of-the-art semi-supervised and unsupervised cross-domain CWS approaches with a large margin. We make our code and data available on Github.
However, one challenge for cross-domain CWS is the lack of such annotated data. liu2012unsupervised propose an unsupervised model, in which they use features derived from character clustering, together with a self-training algorithm to jointly model CWS and POS-tagging. This approach is highly time-consuming @cite_3 . Another challenge is the segmentation of domain-specific noun entities. In a task of segmenting Chinese novels, qiu2015word design a double-propagation algorithm with complex feature templates to iteratively extract noun entities and their context, to improve segmentation performance. This approach still relies heavily on feature templates. Similarly, our model does not require any annotated target data. In contrast to their work, our model is efficient and feature-free.
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Word segmentation is a necessary first step for automatic syntactic analysis of Chinese text. Chinese segmentation is highly accurate on news data, but the accuracies drop significantly on other domains, such as science and literature. For scientific domains, a significant portion of out-of-vocabulary words are domain-specific terms, and therefore lexicons can be used to improve segmentation significantly. For the literature domain, however, there is not a fixed set of domain terms. For example, each novel can contain a specific set of person, organization and location names. We investigate a method for automatically mining common noun entities for each novel using information extraction techniques, and use the resulting entities to improve a state-of-the-art segmentation model for the novel. In particular, we design a novel double-propagation algorithm that mines noun entities together with common contextual patterns, and use them as plug-in features to a model trained on the source domain. An advantage of our method is that no retraining for the segmentation model is needed for each novel, and hence it can be applied efficiently given the huge number of novels on the web. Results on five different novels show significantly improved accuracies, in particular for OOV words.