Chinese Lexical Sememe Prediction Using CilinE Knowledge

Sememes are the smallest semantic units of human languages, composition which can represent meaning words. have been successfully applied to many downstream applications in natural language processing (NLP) field. Annotation a word's sememes depends on experts, is both time-consuming and labor-consuming, limiting large-scale application sememe. Researchers proposed some sememe prediction methods automatically predict for However, existing focus information word itself, ignoring expert-annotated knowledge bases indicate relations between words should value predication. Therefore, we aim at incorporating into process. To achieve that, propose CilinE-guided model employs an base CilinE remodel from relational perspective. Experiments HowNet, widely used Chinese base, shown that has obvious positive effect prediction. Furthermore, our method be integrated significantly improves performance. We will release data code public.