Neural Attentive Bag-of-Entities Model for Text Classification

CONLL 2019  ·  Ikuya Yamada, Hiroyuki Shindo ·

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.

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Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Text Classification 20NEWS NABoE-full Accuracy 88.1 # 4
F-measure 86.2 # 2
Text Classification R8 NABoE-full Accuracy 97.9 # 2
F-measure 91.7 # 1

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