With the proliferation of mobile devices, the amount of social media users and online news
articles are rapidly increasing, and text information online is accumulating as big data. As …

As the scale of online news and social media expands, attempts to analyze the latest social
issues and consumer trends are increasing. Research on detecting spatio-temporal event …

Aiming at the problems of insufficient feature extraction and low classification accuracy in the
classification of Chinese news texts by the neural network, this paper proposes a hybrid …

With the rapid development of text categorization technology, there are still some problems,
such as low classification efficiency, low accuracy and incomplete extraction of text features …

문서 분류 문제는 오랜 기간 동안 자연어 처리 분야에서 연구되어 왔다. 우리는 기존 컨볼루션
신경망을 이용했던 연구에서 나아가, 순환 신경망에 기반을 둔 문서 분류를 수행하였고 그 …

Text classification is the process of discriminating predetermined text into a certain class or
some certain classes. Text categorization has important applications in redundant filtering …

Text classification is one of the most important and typical tasks in Natural Language
Processing (NLP) which can be applied for many applications. Recently, deep learning …

In text classification models based on deep learning, feature extraction and feature
aggregation are two key steps. As one of the basic feature extraction methods, CNN has …

Although several traditional models like bag of words (BOW), n-grams, and their variants of
TFIDF exhibit high performance in the field of text classification, neural network methods …

A sequential learning framework for text categorization based on Meta-cognitive Neural
Network (McNN) is presented in this paper. Initially text documents are pre-processed and …