Neural Attentive Bag-of-Entities Model for Text Classification
Ikuya Yamada, Hiroyuki Shindo · 2019
This study proposes a Neural Attentive Bagof-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.