Fine-grained Entity Type Classification Based on Transfer Learning

The aim of fine-grained entity type classification (FETC) is that mapping the entity appearing in the text into hierarchical fine-grained entity type. In recent years, deep neural network is used to entity classification and has made great progress. However, training a neural network model with precise recognition requires a great quantity labeled data. The labeled dataset of fine-grained entity classification is so rare that hard to classify unlabeled entity. This paper proposes a fine-grained entity classification method based on transfer learning for the task of entity classification with lack labeled dataset. Firstly, we construct a mapping relation model to mining the semantic relationship between labeled entity type and unlabeled entity type, we construct a corresponding labeled entity type mapping set for each unlabeled entity type. Then, we construct a bidirectional long short term memory (BiLSTM) model, the sentence vector combination representing the mapping type set is used as the input of the model to train the unlabeled entity type. Lastly, the attention mechanism is constructed based on the semantic distance between different types in the mapping type set and corresponding unlabeled type, so as to realize entity classifier to recognize the classification of unknown entities. The experiment shows that our method have achieved good results and achieved the purpose of identifying unknown named entity classification with unlabeled dataset. Copyright © 2020 Acta Automatica Sinica. All rights reserved.