With the massive growth of the Internet, text data has become one of the main formats of
tourism big data. As an effective expression means of tourists' opinions, text mining of such …

Abstract Language model pre-training has proven to be useful in learning universal
language representations. As a state-of-the-art language model pre-training model, BERT …

Text classification results can be hindered when just the bag-of-words model is used for
representing features, because it ignores word order and senses, which can vary with the …

Efficient word representation techniques (word embeddings) with modern machine learning
models have shown reasonable improvement on automatic text classification tasks …

Recurrent neural network (RNN) has achieved remarkable performance in text
categorization. RNN can model the entire sequence and capture long-term dependencies …

In dialog system, dialog act recognition and sentiment classification are two correlative tasks
to capture speakers' intentions, where dialog act and sentiment can indicate the explicit and …

Text classification is one of the fundamental tasks in natural language processing. Recently,
deep neural networks have achieved promising performance in the text classification task …

Recurrent neural network (RNN) and convolutional neural network (CNN) are two prevailing
architectures used in text classification. Traditional approaches combine the strengths of …

With the rapid adoption of machine learning (ML), a number of domains now use the
approach of fine tuning models which were pre-trained on a large corpus of data. However …

The exploding cost and time needed for data labeling and model training are bottlenecks for
training DNN models on large datasets. Identifying smaller representative data samples with …