Results 41 to 50 of about 1,551,832 (303)

A Novel Hierarchical Topic Model for Horizontal Topic Expansion With Observed Label Information

IEEE Access, 2019
Hierarchical topic models, such as hierarchical Latent Dirichlet Allocation (hLDA)and its variations, can organize topics into a hierarchy automatically.
Xi Zou, Yuelong Zhu, Jun Feng, Jiamin Lu, Xiaodong Li   +4 more
doaj   +1 more source

TLATR: Automatic Topic Labeling Using Automatic (Domain-Specific) Term Recognition

IEEE Access, 2021
Topic modeling is a probabilistic graphical model for discovering latent topics in text corpora by using multinomial distributions of topics over words. Topic labeling is used to assign meaningful labels for the discovered topics.
Ciprian-Octavian Truica, Elena-Simona Apostol   +1 more
doaj   +1 more source

Predicting protein function via multi-label supervised topic model on gene ontology

Biotechnology & Biotechnological Equipment, 2017
As the biological datasets accumulate rapidly, computational methods designed to automate protein function prediction are critically needed. The problem of protein function prediction can be considered as a multi-label classification problem resulting in
Lin Liu, Lin Tang, Libo He, Shaowen Yao, Wei Zhou   +4 more
doaj   +1 more source

Topic Detection and Tracking Based on Event Ontology

IEEE Access, 2020
In recent years, Topic Detection and Tracking (TDT) has served as a core technology for searching, organizing and structuring news oriented textual materials from a variety of internet news and social media. The biggest challenges of TDT are the sparsity
Wei Liu, Lei Jiang, Yusen Wu, Tingting Tang, Weimin Li   +4 more
doaj   +1 more source

Investigating the performance of automatic new topic identification across multiple datasets [PDF]

Proceedings of the American Society for Information Science and Technology, 2006
AbstractRecent studies on automatic new topic identification in Web search engine user sessions demonstrated that neural networks are successful in automatic new topic identification. However most of this work applied their new topic identification algorithms on data logs from a single search engine.
Özmutlu, H. Cenk, Cavdur, Fatih, Spink, Amanda, Özmutlu, Seda   +3 more
openaire   +3 more sources

Semi‐supervised classification of fundus images combined with CNN and GCN

Journal of Applied Clinical Medical Physics, Volume 23, Issue 12, December 2022., 2022
Abstract Purpose Diabetic retinopathy (DR) is one of the most serious complications of diabetes, which is a kind of fundus lesion with specific changes. Early diagnosis of DR can effectively reduce the visual damage caused by DR. Due to the variety and different morphology of DR lesions, automatic classification of fundus images in mass screening can ...
Sixu Duan, Pu Huang, Min Chen, Ting Wang, Xiaolei Sun, Meirong Chen, Xueyuan Dong, Zekun Jiang, Dengwang Li   +8 more
wiley   +1 more source

DRAFT: Dense Retrieval Augmented Few-shot Topic classifier Framework [PDF]

arXiv, 2023
With the growing volume of diverse information, the demand for classifying arbitrary topics has become increasingly critical. To address this challenge, we introduce DRAFT, a simple framework designed to train a classifier for few-shot topic classification.
arxiv  

Cell‐free and extracellular vesicle microRNAs with clinical utility for solid tumors

Molecular Oncology, EarlyView.
Cell‐free microRNAs (cfmiRs) are small‐RNA circulating molecules detectable in almost all body biofluids. Innovative technologies have improved the application of cfmiRs to oncology, with a focus on clinical needs for different solid tumors, but with emphasis on diagnosis, prognosis, cancer recurrence, as well as treatment monitoring.
Yoshinori Hayashi, Janelle‐Cheri Millen, Romela Irene Ramos, Jennifer A. Linehan, Timothy G. Wilson, Dave S. B. Hoon, Matias A. Bustos   +6 more
wiley   +1 more source

Uniqueness of radiomic features in non‐small cell lung cancer

Journal of Applied Clinical Medical Physics, Volume 23, Issue 12, December 2022., 2022
Abstract Purpose The uniqueness of radiomic features, combined with their reproducibility, determines the reliability of radiomic studies. This study is to test the hypothesis that radiomic features extracted from a defined region of interest (ROI) are unique to the underlying structure (e.g., tumor). Approach Two cohorts of non‐small cell lung cancer (
Gary Ge, Jie Zhang
wiley   +1 more source

A Study on Performance Enhancement by Integrating Neural Topic Attention with Transformer-Based Language Model

Applied Sciences
As an extension of the transformer architecture, the BERT model has introduced a new paradigm for natural language processing, achieving impressive results in various downstream tasks.
Taehum Um, Namhyoung Kim
doaj   +1 more source