Recognizing Chinese entities in low-resource settings is a challenging but promising task, which extracts structured pre-defined entities and corresponding types from unstructured text. Compared with the prosperous Named Entity Recognition (NER) methods for Indo-European languages, such as English, the research on Chinese NER is still in its infancy. The main obstacles to the development of Chinese NER methods include the ambiguity of Chinese entity boundary recognition and limited data resources. To address these issues, in this paper, a word-segmentation-based model is present for few-shot Chinese NER. First, we enumerate all possible candidate entity spans on the character level for accurate entity boundary identification with the proposed word segmentation and combination strategy. Then, one kind of question-answer-based prompt template loaded with the candidate entity spans is proposed to cast entity extraction into the masked token prediction task, for dealing with the low-data problem by taking full advantage of the generality and transferability of the pre-trained language model. The extensive experimental results show that our method outperforms the state-of-the-art baselines in low-data settings and also achieves comparable performance in full-data settings.

Recently, graph neural network (GNN) has achieved great success in many graph learning tasks such as node classifi-cation and graph classification. However, there is no single GNN architecture that can fit different graph datasets. Designing an effective GNN for a specific graph dataset requires considerable expert experience and huge computational costs. Inspired by the success of neural architecture search (NAS), searching the GNN architectures automatically has attracted more and more attention. Motivated by the fact that the search space plays a critical role in the NAS, we propose a novel and effective graph neural architecture search method called PSP from the perspective of search space design in this paper. We first propose an expressive search space composed of multiple cells. Instead of searching the entire architecture, we focus on searching the architecture of the cell. Then, we propose a progressive space pruning-based algorithm to search the architectures efficiently. Moreover, the data-specific search spaces and architectures ob-tained by PSP can be transferred to new graph datasets based on meta-learning. Extensive experimental results on different types of graph datasets reveal that PSP outperforms the state-of-the-art handcrafted architectures and the existing NAS methods in terms of effectiveness and efficiency.

Named Entity Recognition (NER) aims to identify the pre-defined entities from the unstructured text. Compared with English NER, Chinese NER faces more challenges: the ambiguity problem in entity boundary recognition due to unavailable explicit delimiters between Chinese characters, and the out-of-vocabulary (OOV) problem caused by rare Chinese characters. However, two important features specific to the Chinese language are ignored by previous studies: glyphs and phonetics, which contain rich semantic information of Chinese. To overcome these issues by exploiting the linguistic potential of Chinese as a logographic language, we present MPM-CNER (short for Multi-modal Pretraining Model for Chinese NER), a model for learning multi-modal representations of Chinese semantics, glyphs, and phonetics, via four pretraining tasks: Radical Consistency Identification (RCI), Glyph Image Classification (GIC), Phonetic Consistency Identification (PCI), and Phonetic Classification Modeling (PCM). Meanwhile, a novel cross-modality attention mechanism is proposed to fuse these multimodal features for further improvement. The experimental results show that our method outperforms the state-of-the-art baseline methods on four benchmark datasets, and the ablation study also verifies the effectiveness of the pre-trained multi-modal representations.