no code implementations • ACL (NL4XAI, INLG) 2020 • Mat Rawsthorne, Tahseen Jilani, Jacob Andrews, Yunfei Long, Jeremie Clos, Samuel Malins, Daniel Hunt
In this paper we report progress on a novel explainable artificial intelligence (XAI) initiative applying Natural Language Processing (NLP) with elements of codesign to develop a text classifier for application in psychotherapy training.
Explainable artificial intelligence Explainable Artificial Intelligence (XAI)
no code implementations • COLING 2022 • Zijie Lin, Bin Liang, Yunfei Long, Yixue Dang, Min Yang, Min Zhang, Ruifeng Xu
This essentially allows the framework to understand the appropriate graph structures for learning intricate relations among different modalities.
no code implementations • EMNLP 2020 • Huilin Zhong, Junsheng Zhou, Weiguang Qu, Yunfei Long, Yanhui Gu
To capture the dependencies between law articles, the model also introduces a self-attention mechanism between multiple representations.
no code implementations • Findings (ACL) 2022 • Xiaotong Jiang, Qingqing Zhao, Yunfei Long, Zhongqing Wang
In this paper, we introduce a new task called synesthesia detection, which aims to extract the sensory word of a sentence, and to predict the original and synesthetic sensory modalities of the corresponding sensory word.
no code implementations • 18 Apr 2024 • Yucheng Lin, Yuhan Xia, Yunfei Long
This study introduces a novel method for irony detection, applying Large Language Models (LLMs) with prompt-based learning to facilitate emotion-centric text augmentation.
no code implementations • 22 Mar 2024 • Yuhan Xia, Qingqing Zhao, Yunfei Long, Ge Xu, Jia Wang
In traditional research approaches, sensory perception and emotion classification have traditionally been considered separate domains.
no code implementations • 18 Mar 2024 • Guangming Huang, Yunfei Long, Yingya Li, Giorgos Papanastasiou
This work presents a thorough scoping review on explainable and interpretable DL in healthcare NLP.
no code implementations • 29 Feb 2024 • Guangming Huang, Yunfei Long, Cunjin Luo, Jiaxing Shen, Xia Sun
In this study, we introduce a Prompting Explicit and Implicit knowledge (PEI) framework, which uses prompts to connect explicit and implicit knowledge, aligning with human reading process for multi-hop QA.
no code implementations • 8 Mar 2023 • Hui Yang, Stella Hadjiantoni, Yunfei Long, Ruta Petraityte, Berthold Lausen
The experimental results show that the suggested automated industry analysis which employs ML techniques allows the processing of large collections of text data in an easy, efficient, and scalable way.
1 code implementation • ACM International Conference on Multimedia 2022 • Zhe Xue, Junping Du, Hai Zhu, Zhongchao Guan, Yunfei Long, Yu Zang, Meiyu Liang
To address these issues, we propose a Robust Diversified Graph Contrastive Network (RDGC) for incomplete multi-view clustering, which integrates multi-view representation learning and diversified graph contrastive regularization into a unified framework.
no code implementations • ICCV 2021 • Yunfei Long, Daniel Morris, Xiaoming Liu, Marcos Castro, Punarjay Chakravarty, Praveen Narayanan
A distinctive feature of Doppler radar is the measurement of velocity in the radial direction for radar points.
no code implementations • 3 Aug 2021 • Chengtao Peng, Yunfei Long, Senhua Zhu, Dandan Tu, Bin Li
Our proposed network contains two stages: the first one is a lung region segmentation step and is used to exclude irrelevant factors, and the second is a detection and recommendation stage.
no code implementations • CVPR 2021 • Yunfei Long, Daniel Morris, Xiaoming Liu, Marcos Castro, Punarjay Chakravarty, Praveen Narayanan
Here we propose a radar-to-pixel association stage which learns a mapping from radar returns to pixels.
no code implementations • LREC 2020 • Rong Xiang, Yunfei Long, Mingyu Wan, Jinghang Gu, Qin Lu, Chu-Ren Huang
Deep neural network models have played a critical role in sentiment analysis with promising results in the recent decade.
no code implementations • LREC 2020 • Rong Xiang, Xuefeng Gao, Yunfei Long, Anran Li, Emmanuele Chersoni, Qin Lu, Chu-Ren Huang
Automatic Chinese irony detection is a challenging task, and it has a strong impact on linguistic research.
no code implementations • 1 May 2019 • Yunfei Long, Amber Bassett, Karen Cichy, Addie Thompson, Daniel Morris
Splits on canned beans appear in the process of preparation and canning.
no code implementations • CVPR 2019 • Saif Imran, Yunfei Long, Xiaoming Liu, Daniel Morris
We also show that the standard Mean Squared Error (MSE) loss function can promote depth mixing, and thus propose instead to use cross-entropy loss for DC.
no code implementations • WS 2018 • Rong Xiang, Yunfei Long, Qin Lu, Dan Xiong, I-Hsuan Chen
Then representation of the major text is learned through an LSTM model whereas the minor text is learned by a separate CNN model.
no code implementations • WS 2018 • Yunfei Long, Mingyu Ma, Qin Lu, Rong Xiang, Chu-Ren Huang
In this work, we propose a dual user and product memory network (DUPMN) model to learn user profiles and product reviews using separate memory networks.
Ranked #6 on Sentiment Analysis on User and product information
no code implementations • IJCNLP 2017 • Yunfei Long, Qin Lu, Rong Xiang, Minglei Li, Chu-Ren Huang
This paper proposes a novel method to incorporate speaker profiles into an attention based LSTM model for fake news detection.
no code implementations • IJCNLP 2017 • Minglei Li, Qin Lu, Yunfei Long
In this paper, we investigate the effectiveness of different affective lexicons through sentiment analysis of phrases.
no code implementations • EMNLP 2017 • Yunfei Long, Qin Lu, Rong Xiang, Minglei Li, Chu-Ren Huang
Evaluations show the CBA based method outperforms the state-of-the-art local context based attention methods significantly.
no code implementations • CONLL 2017 • I-Hsuan Chen, Yunfei Long, Qin Lu, Chu-Ren Huang
We propose a set of syntactic conditions crucial to event structures to improve the model based on the classification of radical groups.
no code implementations • LREC 2016 • Minglei Li, Yunfei Long, Lu Qin, Wenjie Li
Secondly, a SVM based classifier is used to select the data whose natural labels are consistent with the predicted labels.