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• W2971683378 abstract "This study proposes a Neural Attentive Bag-of-Entities model, which is a neural network model that performs text classification using entities in a knowledge base. Entities provide unambiguous and relevant semantic signals that are beneficial for capturing semantics in texts. We combine simple high-recall entity detection based on a dictionary, to detect entities in a document, with a novel neural attention mechanism that enables the model to focus on a small number of unambiguous and relevant entities. We tested the effectiveness of our model using two standard text classification datasets (i.e., the 20 Newsgroups and R8 datasets) and a popular factoid question answering dataset based on a trivia quiz game. As a result, our model achieved state-of-the-art results on all datasets. The source code of the proposed model is available online at https://github.com/wikipedia2vec/wikipedia2vec." @default.
• W2971683378 created "2019-09-12" @default.
• W2971683378 creator A5063398043 @default.
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• W2971683378 date "2019-09-03" @default.
• W2971683378 modified "2023-09-24" @default.
• W2971683378 title "Neural Attentive Bag-of-Entities Model for Text Classification" @default.
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• W2971683378 doi "https://doi.org/10.48550/arxiv.1909.01259" @default.
• W2971683378 hasPublicationYear "2019" @default.
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