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تسجيل مستخدم جديدGlobal Entity Disambiguation with Pretrained Contextualized Embeddings of Words and Entities
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We propose a new global entity disambiguation (ED) model based on contextualized embeddings of words and entities. Our model is based on a bidirectional transformer encoder (i.e., BERT) and produces contextualized embeddings for words and entities in the input text. The model is trained using a new masked entity prediction task that aims to train the model by predicting randomly masked entities in entity-annotated texts obtained from Wikipedia. We further extend the model by solving ED as a sequential decision task to capture global contextual information. We evaluate our model using six standard ED datasets and achieve new state-of-the-art results on all but one dataset.
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