Using advanced Chinese named entity recognition technology, you can extract general entities such as time, place, person, country (organization, institution) from the unstructured text you provide, and then apply it to tasks such as text understanding, intent analysis, and dialogue NLU
Large-scale Chinese text corpus provides massive background knowledge for entity tagging technology. Combining bidirectional long and short-term memory network and conditional random field technology, the recognition accuracy and accuracy have reached the extremely high level in the industry
Not only provide industry common tagging entities such as time, place, and people, also provide a wider range of tagging target types such as organizations
Through a large amount of text corpus and deep neural network technology, we weaken the strong demand of entity annotation for context, and solve the problem of entity annotation accuracy in weak context
Through entity tagging, the entities in the text can be accurately identified, which can disambiguate the knowledge in the text, and assist syntactic analysis and other technologies to achieve the purpose of understanding the text content
Through entity annotation, the instance entities in the text can be identified, and then supplemented with knowledge graph related technologies, knowledge can be extracted from the text to construct a knowledge network
Through entity labeling, relevant entities can be accurately identified from the dialogue text, and supplemented by corresponding feature engineering, can effectively improve the accuracy of the reply in the dialogue NLU task
