Traditional methods for sentiment analysis, when applied in a monolingual context, often
yield less than optimal results in multilingual settings. This underscores the need for a more …

Extractive text summarization aims at extracting the most representative sentences from a
given document as its summary. To extract a good summary from a long text document …

Traditional knowledge distillation approaches are typically designed for specific tasks, as
they primarily distilling deep features from intermediate layers of a neural network, generally …

The continuous increase of digital documents on the web creates the need to search for
information patterns that allow the categorization of organizational documents to generate …

Scientific survey papers play a pivotal role in advancing knowledge and scientific progress
by providing concise summaries and analyses of research trends and findings. To facilitate …

In the era of big data and Artificial Intelligence, an emerging paradigm is to utilize contrastive
self-supervised learning to model large-scale heterogeneous data. Many existing foundation …

To create a green and healthy living environment, people have put forward higher
requirements for the refined management of ecological resources. A variety of technologies …

Abstract Knowledge graph embedding (KGE) is a task to transform the symbolic entities and
relations in Knowledge Graphs (KGs) into low-dimensional vectors, which facilitates the use …

Knowledge graph embedding (KGE) is a task to transform the symbolic entities and relations
in Knowledge Graphs (KGs) into lowdimensional vectors, which facilitates the use of KGs in …

With pre-trained bilingual word embedding (BWE) dictionaries, deep learning-based cross-
language sentiment analysis models require text vector representations in both the source …