… To address these problems, we propose a joint learning method for event text classification
and event assignment for Chinese government hotline. Firstly, graph convolution network (…

… In this paper, we put forward a multi-label text classification algorithm LELC(joint learning …
to solve the issue of multi-label text classification with a large number of class labels. …

… learning the representations of text sequences. We propose to view text classification as a
label-word joint … the compatibility of embeddings between text sequences and labels. The …

… To address this problem, we propose a novel joint learning algorithm that allows the feedbacks
to be propagated from the classifiers for latter labels to the classifier for the current label. …

… text classification tasks show that the joint learning of multiple related tasks together can
improve the performance of each task relative to learning … Thus, after the joint learning phase, …

… several states of the art models in terms of text perplexity and topic coherence. Moreover, …
text classification task. Finally, given the input texts, our model can generate meaningful texts …

… This further validates the advantages of our joint learning of multi-loss classification for
optimising re-id especially when the re-id test population size increases (751 people on Market-…

… In this paper, we propose a joint learning approach for sentiment and emotion classification.
The basic idea of the approach is to annotate a small size of data where the samples are …

… We propose a novel framework with multi-source domain adaptation and joint learning
for multi-source cross-domain sentiment classification tasks This framework uses bi-directional …

… 3.2 Neural Text Classification Model For the text classification model, we use a hierarchical
… the joint learning of topics and sentiment attentions. Another advantage of mutual learning is …