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• W3014318675 abstract "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." @default.
• W3014318675 created "2020-04-10" @default.
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• W3014318675 date "2019-09-01" @default.
• W3014318675 modified "2023-10-01" @default.
• W3014318675 title "Global Entity Disambiguation with Pretrained Contextualized Embeddings of Words and Entities" @default.
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