Text classification is an important and classical problem in natural language processing.
There have been a number of studies that applied convolutional neural networks …

Text classification is a challenging problem due to the high dimensionality of the text, which
can limit classification performance. The orthogonal matching pursuit (OMP) algorithm is one …

Despite the academic interest in city branding, further empirical studies are needed to
explore the use of spotlight events in rebuilding images of cities. Colombiamoda, a trade …

Text classification is a fundamental and important task in natural language processing.
There have been many graph-based neural networks for this task with the capacity of …

Detecting the evolutionary stages of social media events such as Twitter and Sina Weibo is
beneficial for enterprises and governments to take necessary actions before emergent …

Graphs have been widely used as modeling tools in Natural Language Processing (NLP),
Text Mining (TM) and Information Retrieval (IR). Traditionally, the unigram bag-of-words …

Many institutions are devoted to providing investment advising services to stock investors to
help them make sound investment decisions. Industry analysts at these institutions need to …

Text classification is an essential task of natural language processing. Preprocessing, which
determines the representation of text features, is one of the key steps of text classification …

[Objective] This paper proposes a new model integrating tag semantics. It uses text-level
hypergraph and cross attention mechanism to capture the organizational structure and …

In text classification, the problem of overfitting arises due to the high dimensionality, making
regularization essential. Although classic regularizers provide sparsity, they fail to return …