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Found 28 papers, 1 papers with code
Modeling Intra- and Inter-Modal Relations: Hierarchical Graph Contrastive Learning for Multimodal Sentiment Analysis
This essentially allows the framework to understand the appropriate graph structures for learning intricate relations among different modalities.
Chinese Synesthesia Detection: New Dataset and Models
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.
An Element-aware Multi-representation Model for Law Article Prediction
To capture the dependencies between law articles, the model also introduces a self-attention mechanism between multiple representations.
ExTRA: Explainable Therapy-Related Annotations
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)
Augmenting emotion features in irony detection with Large language modeling
This study introduces a novel method for irony detection, applying Large Language Models (LLMs) with prompt-based learning to facilitate emotion-centric text augmentation.
SensoryT5: Infusing Sensorimotor Norms into T5 for Enhanced Fine-grained Emotion Classification
In traditional research approaches, sensory perception and emotion classification have traditionally been considered separate domains.
From explainable to interpretable deep learning for natural language processing in healthcare: how far from reality?
This work presents a thorough scoping review on explainable and interpretable DL in healthcare NLP.
Prompting Explicit and Implicit Knowledge for Multi-hop Question Answering Based on Human Reading Process
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.
Automatic Detection of Industry Sectors in Legal Articles Using Machine Learning Approaches
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.
Robust Diversified Graph Contrastive Network for Incomplete Multi-view Clustering
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.
Full-Velocity Radar Returns by Radar-Camera Fusion
A distinctive feature of Doppler radar is the measurement of velocity in the radial direction for radar points.
DuCN: Dual-children Network for Medical Diagnosis and Similar Case Recommendation towards COVID-19
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.
Radar-Camera Pixel Depth Association for Depth Completion
Here we propose a radar-to-pixel association stage which learns a mapping from radar returns to pixels.
Affection Driven Neural Networks for Sentiment Analysis
Deep neural network models have played a critical role in sentiment analysis with promising results in the recent decade.
Ciron: a New Benchmark Dataset for Chinese Irony Detection
Automatic Chinese irony detection is a challenging task, and it has a strong impact on linguistic research.
Bean Split Ratio for Dry Bean Canning Quality and Variety Analysis
Splits on canned beans appear in the process of preparation and canning.
Depth Coefficients for Depth Completion
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.
Leveraging Writing Systems Change for Deep Learning Based Chinese Emotion Analysis
Then representation of the major text is learned through an LSTM model whereas the minor text is learned by a separate CNN model.
Dual Memory Network Model for Biased Product Review Classification
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
Are Manually Prepared Affective Lexicons Really Useful for Sentiment Analysis
In this paper, we investigate the effectiveness of different affective lexicons through sentiment analysis of phrases.
Fake News Detection Through Multi-Perspective Speaker Profiles
This paper proposes a novel method to incorporate speaker profiles into an attention based LSTM model for fake news detection.
A Cognition Based Attention Model for Sentiment Analysis
Evaluations show the CBA based method outperforms the state-of-the-art local context based attention methods significantly.
Leveraging Eventive Information for Better Metaphor Detection and Classification
We propose a set of syntactic conditions crucial to event structures to improve the model based on the classification of radical groups.
Emotion Corpus Construction Based on Selection from Hashtags
Secondly, a SVM based classifier is used to select the data whose natural labels are consistent with the predicted labels.
