Datasets for NLPCC2022.SharedTask5.Track2

Domain knowledge graph has been widely adopted for various domains, e.g., medicine, agriculture and service industry, because it can provide promising functions including intelligent search and personalized recommendation. Knowledge graph is normally composited of a huge number of entities and relations (connect entities), and the utility of knowledge graph largely depends on the richness of these entities/relations. To construct high-value knowledge graph (e.g., informative domain knowledge graph), researchers aim at automatically extracting entities from massive heterogeneous sources, which is often impossible to achieve with pure manual labor.With the blooming of natural language processing (NLP), researchers have proposed Named Entity Recognition (NER) technique to automatically extract entities from raw texts. NER is mostly regarded as a supervised sequence labeling/tagging task; that is, recognizing entities from unseen texts according to the patterns learned from labeled texts. As an essential step for knowledge graph construction, as well as some other NLP tasks, the development of NER is one of the main focuses in both the academia and the industry in recent years. Under this background, this competition targets at exploring novel and insightful NER methods to better capture the entities, especially for the construction of domain knowledge graphs.