Zobrazeno 1 - 10
of 338
pro vyhledávání: '"Donghong, Ji"'
Autor:
Kamran Aziz, Donghong Ji, Prasun Chakrabarti, Tulika Chakrabarti, Muhammad Shahid Iqbal, Rashid Abbasi
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-22 (2024)
Abstract Aspect-Based Sentiment Analysis (ABSA) represents a fine-grained approach to sentiment analysis, aiming to pinpoint and evaluate sentiments associated with specific aspects within a text. ABSA encompasses a set of sub-tasks that together fac
Externí odkaz:
https://doaj.org/article/d2b8f489cda940dca15eb6712b071aeb
Autor:
Kamran Aziz, Naveed Ahmed, Hassan Jalil Hadi, Aizihaierjiang Yusufu, Mohammaed Ali Alshara, Yasir Javed, Donghong Ji
Publikováno v:
Journal of King Saud University: Computer and Information Sciences, Vol 36, Iss 9, Pp 102221- (2024)
Urdu, with its rich linguistic complexity, poses significant challenges for computational sentiment analysis. This study presents an enhanced version of UrduAspectNet, specifically designed for Aspect-Based Sentiment Analysis (ABSA) in Urdu. We intro
Externí odkaz:
https://doaj.org/article/bf7bc25d29b842db99458cd74a67cc3e
Publikováno v:
Journal of King Saud University: Computer and Information Sciences, Vol 36, Iss 7, Pp 102153- (2024)
Recently, Nominal Compound Chain Extraction (NCCE) has been proposed to detect related mentions in a document to improve understanding of the document’s topic. NCCE involves longer span detection and more complicated rules for relation decisions, m
Externí odkaz:
https://doaj.org/article/b1fbe3a6bddb448c8a1e86c69fabf767
Publikováno v:
IEEE Access, Vol 9, Pp 79671-79684 (2021)
Deep learning approaches have demonstrated significant progress in breast cancer histopathological image diagnosis. Training an interpretable diagnosis model using high-resolution histopathological image is still challenging. To alleviate this proble
Externí odkaz:
https://doaj.org/article/f3f273220df342bcbf0d0587f476281a
Publikováno v:
IEEE Access, Vol 8, Pp 85729-85739 (2020)
Normalizing disease names is a crucial task for biomedical and healthcare domains. Previous work explored various approaches, including rules, machine learning and deep learning, which focused on only one approach or one model. In this study, we syst
Externí odkaz:
https://doaj.org/article/aafbd9ee2a7d4fefb0aeac10b5e24b9e
Publikováno v:
IEEE Access, Vol 8, Pp 103619-103634 (2020)
Image sentiment analysis is a hot research topic in the field of computer vision. However, two key issues need to be addressed. First, high-quality training samples are scarce. There are numerous ambiguous images in the original datasets owing to div
Externí odkaz:
https://doaj.org/article/c4c406370465419f92f786213b0cd07c
Publikováno v:
IEEE Access, Vol 8, Pp 71584-71592 (2020)
The lack of human annotations has been one of the main obstacles for neural named entity recognition in low-resource domains. To address this problem, there have been many efforts on automatically generating silver annotations according to domain-spe
Externí odkaz:
https://doaj.org/article/6f5df119689d4335bda952116e56efc3
Publikováno v:
IEEE Access, Vol 8, Pp 44202-44210 (2020)
Keyphrases provide core information for users to understand the document. Most previous works utilize machine learning based methods for keyphrases extraction and achieve promising performance. However, these methods focus on identify keyphrases from
Externí odkaz:
https://doaj.org/article/0c1c7a911ed14ce494d540b59db1c605
Autor:
Hongbin Zhang, Ziliang Jiang, Qipeng Xiong, Jinpeng Wu, Tian Yuan, Guangli Li, Yiwang Huang, Donghong Ji
Publikováno v:
IEEE Access, Vol 8, Pp 159511-159529 (2020)
Material recognition is a fundamental problem in the field of computer vision. Material recognition is still challenging because of varying camera perspectives, light conditions, and illuminations. Feature learning or feature engineering helps build
Externí odkaz:
https://doaj.org/article/f0b0ae0ccd594afbb6cf132a65814d5d
Autor:
Guangli Li, Tian Yuan, Chuanxiu Li, Jianwu Zhuo, Ziliang Jiang, Jinpeng Wu, Donghong Ji, Hongbin Zhang
Publikováno v:
IEEE Access, Vol 8, Pp 227538-227555 (2020)
Early detection and diagnosis of breast cancer are crucial to improve the survival rates of patients. Hence, pathologists and radiologists need a computer-aided diagnosis system to assist their clinical diagnoses effectively and efficiently. However,
Externí odkaz:
https://doaj.org/article/917d5250046544a9a1e4dcabfb53b0e3