Results 31 to 40 of about 1,745,508 (275)
Explicit Interaction Model towards Text Classification
, 2018 Text classification is one of the fundamental tasks in natural language processing. Recently, deep neural networks have achieved promising performance in the text classification task compared to shallow models. Despite of the significance of deep models,
Chin, Zhaozheng +5 more
core +1 more source
Text Classification For Authorship Attribution Analysis
, 2013 Authorship attribution mainly deals with undecided authorship of literary texts. Authorship attribution is useful in resolving issues like uncertain authorship, recognize authorship of unknown texts, spot plagiarism so on. Statistical methods can be used
Elayidom, M. Sudheep +3 more
core +1 more source
Research on manufacturing text classification based on improved genetic algorithm
Brazilian Archives of Biology and TechnologyAccording to the features of texts, a text classification model is proposed. Base on this model, an optimized objective function is designed by utilizing the occurrence frequency of each feature in each category.
Zhou Kaijun, Tong Yifei
doaj +1 more source
HDLTex: Hierarchical Deep Learning for Text Classification
, 2017 The continually increasing number of documents produced each year necessitates ever improving information processing methods for searching, retrieving, and organizing text. Central to these information processing methods is document classification, which
Barnes, Laura E. +5 more
core +1 more source
Social Media Text Classification by Enhancing Well-Formed Text Trained Model
Journal of ICT Research and Applications, 2016 Social media are a powerful communication tool in our era of digital information. The large amount of user-generated data is a useful novel source of data, even though it is not easy to extract the treasures from this vast and noisy trove.
Phat Jotikabukkana +3 more
doaj +1 more source
Generative Multi-Task Learning for Text Classification
IEEE Access, 2020 Multi-task learning leverages potential correlations among related tasks to extract common features and yield performance gains. In this paper, a generative multi-task learning (MTL) approach for text classification and categorization is proposed, which ...
Wei Zhao, Hui Gao, Shuhui Chen, Nan Wang
doaj +1 more source
Pediatric Blood &Cancer, EarlyView.ABSTRACT Introduction Characterizing stressful events reported by childhood cancer survivors experienced throughout the lifespan may help improve trauma‐informed care relevant to the survivor experience. Methods Participants included 2552 survivors (54% female; 34 years of age) and 469 community controls (62% female; 33 years of age) from the St.
Megan E. Ware +13 more
wiley +1 more source
Three-Branch BERT-Based Text Classification Network for Gastroscopy Diagnosis Text
International Journal of Crowd ScienceDuring a hospital visit, a significant volume of Gastroscopy Diagnostic Text (GDT) data are produced, representing the unstructured gastric medical records of patients undergoing gastroscopy.
Zhichao Wang +3 more
doaj +1 more source
Pediatric Blood &Cancer, EarlyView.ABSTRACT Chemotherapy‐induced peripheral neuropathy remains a major complication in pediatric cancer, with disrupted somatosensory and nociceptive processing being a key aspect. This review synthesizes empirical studies on alterations in somatosensory and nociceptive processing in children and adolescents with cancer.
Julia Schweiger +4 more
wiley +1 more source
Text Classification: How Machine Learning Is Revolutionizing Text Categorization
InformationThe automated classification of texts into predefined categories has become increasingly prominent, driven by the exponential growth of digital documents and the demand for efficient organization.
Hesham Allam +4 more
doaj +1 more source