In the era of big data, the internet produces vast amounts of data every day, among which
text data occupies the main position. It is difficult for manual processing to deal with the …

In the era of big data, a large number of text data generated by the Internet has given birth to
a variety of text representation methods. In natural language processing (NLP), text …

Collecting and analyzing data from all devices to improve the efficiency of business
processes is an important task of Industrial Internet of Things (IIoT). In the age of data …

[Objective] This paper proposed a model to extract semantic features from texts more
comprehensively and to improve the representation of semantics by text vectors.[Methods] …

Text representations in text classification usually have high dimensionality and are lack of
semantics, resulting in poor classification effect. In this paper, TF-IDF is optimized by using …

Automatic text classification is a classic topic for natural language processing. Text
classification research mainly focuses on feature representation of text documents or …

In view of the traditional classification algorithm, the problem of high feature dimension and
data sparseness often occurs when text classification of short texts. This paper proposes a …

Text classification has always been a research hot-spot in the field of natural language
processing. For the neural network input matrix, only the word vector of the word level is …

Text classification is one of the classic tasks in the field of natural language processing. The
goal is to identify the category to which the text belongs. Text categorization is widely used in …

Text classification is a process of automatically classifying test data according to given rules.
Word embedding technology is based on neural probabilistic language model, which can …