2 research outputs found
Neural Attentive Bag-of-Entities Model for Text Classification
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.Comment: Accepted to CoNLL 201
Deep Learning Based Text Classification: A Comprehensive Review
Deep learning based models have surpassed classical machine learning based
approaches in various text classification tasks, including sentiment analysis,
news categorization, question answering, and natural language inference. In
this paper, we provide a comprehensive review of more than 150 deep learning
based models for text classification developed in recent years, and discuss
their technical contributions, similarities, and strengths. We also provide a
summary of more than 40 popular datasets widely used for text classification.
Finally, we provide a quantitative analysis of the performance of different
deep learning models on popular benchmarks, and discuss future research
directions