About This Document
- sl:arxiv_author :
- sl:arxiv_firstAuthor : Ikuya Yamada
- sl:arxiv_num : 1909.01259
- sl:arxiv_published : 2019-09-03T15:50:34Z
- sl:arxiv_summary : 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.@en
- sl:arxiv_title : Neural Attentive Bag-of-Entities Model for Text Classification@en
- sl:arxiv_updated : 2019-09-10T10:23:49Z
- sl:bookmarkOf : https://arxiv.org/abs/1909.01259
- sl:creationDate : 2020-09-02
- sl:creationTime : 2020-09-02T16:46:43Z
- sl:relatedDoc : http://www.semanlink.net/doc/2020/01/investigating_entity_knowledge_
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