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 …
approaches in various text classification tasks, including sentiment analysis, news …
A survey on text classification algorithms: From text to predictions
In recent years, the exponential growth of digital documents has been met by rapid progress
in text classification techniques. Newly proposed machine learning algorithms leverage the …
in text classification techniques. Newly proposed machine learning algorithms leverage the …
A survey on text classification: From traditional to deep learning
Text classification is the most fundamental and essential task in natural language
processing. The last decade has seen a surge of research in this area due to the …
processing. The last decade has seen a surge of research in this area due to the …
Don't give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization
We introduce extreme summarization, a new single-document summarization task which
does not favor extractive strategies and calls for an abstractive modeling approach. The idea …
does not favor extractive strategies and calls for an abstractive modeling approach. The idea …
Learned in translation: Contextualized word vectors
Computer vision has benefited from initializing multiple deep layers with weights pretrained
on large supervised training sets like ImageNet. Natural language processing (NLP) …
on large supervised training sets like ImageNet. Natural language processing (NLP) …
A survey on text classification: From shallow to deep learning
Text classification is the most fundamental and essential task in natural language
processing. The last decade has seen a surge of research in this area due to the …
processing. The last decade has seen a surge of research in this area due to the …
Attention-emotion-enhanced convolutional LSTM for sentiment analysis
Long short-term memory (LSTM) neural networks and attention mechanism have been
widely used in sentiment representation learning and detection of texts. However, most of …
widely used in sentiment representation learning and detection of texts. However, most of …
Lagging inference networks and posterior collapse in variational autoencoders
The variational autoencoder (VAE) is a popular combination of deep latent variable model
and accompanying variational learning technique. By using a neural inference network to …
and accompanying variational learning technique. By using a neural inference network to …
Learning to generate reviews and discovering sentiment
We explore the properties of byte-level recurrent language models. When given sufficient
amounts of capacity, training data, and compute time, the representations learned by these …
amounts of capacity, training data, and compute time, the representations learned by these …
Topic modelling meets deep neural networks: A survey
Topic modelling has been a successful technique for text analysis for almost twenty years.
When topic modelling met deep neural networks, there emerged a new and increasingly …
When topic modelling met deep neural networks, there emerged a new and increasingly …