Yanghui Rao
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View article: Plastid RNA Editing in Glycyrrhiza uralensis: Landscape Characterization and Comparative Assessment of RNA-Seq Library Strategies for Detection
Plastid RNA Editing in Glycyrrhiza uralensis: Landscape Characterization and Comparative Assessment of RNA-Seq Library Strategies for Detection Open
Background: Plastid RNA editing is widespread in angiosperms yet remains underexplored in the medicinal non-model species Glycyrrhiza uralensis. This study aimed to (i) comprehensively identify plastid RNA editing sites in G. uralensis, an…
View article: Plastid RNA Editing in <em>Glycyrrhiza uralensis</em>: Landscape Characterization and Comparative Assessment of RNA‑seq Library Strategies for Detection
Plastid RNA Editing in <em>Glycyrrhiza uralensis</em>: Landscape Characterization and Comparative Assessment of RNA‑seq Library Strategies for Detection Open
Background: Plastid RNA editing is widespread in angiosperms, yet remains underexplored in the medicinal nonmodel species Glycyrrhiza uralensis. This study aimed to: (i) comprehensively identify plastid RNA editing sites in G. uralensis; a…
View article: Adaptive Adversarial Training for Balancing Model Robustness and Standard Performance
Adaptive Adversarial Training for Balancing Model Robustness and Standard Performance Open
View article: GuARD: Effective Anomaly Detection through a Text-Rich and Graph-Informed Language Model
GuARD: Effective Anomaly Detection through a Text-Rich and Graph-Informed Language Model Open
View article: iTFKAN: Interpretable Time Series Forecasting with Kolmogorov-Arnold Network
iTFKAN: Interpretable Time Series Forecasting with Kolmogorov-Arnold Network Open
As time evolves, data within specific domains exhibit predictability that motivates time series forecasting to predict future trends from historical data. However, current deep forecasting methods can achieve promising performance but gene…
View article: CARE: A Disagreement Detection Framework with Concept Alignment and Reasoning Enhancement
CARE: A Disagreement Detection Framework with Concept Alignment and Reasoning Enhancement Open
View article: Neural Topic Modeling via Contextual and Graph Information Fusion
Neural Topic Modeling via Contextual and Graph Information Fusion Open
View article: Detecting Emotional Incongruity of Sarcasm by Commonsense Reasoning
Detecting Emotional Incongruity of Sarcasm by Commonsense Reasoning Open
This paper focuses on sarcasm detection, which aims to identify whether given statements convey criticism, mockery, or other negative sentiment opposite to the literal meaning. To detect sarcasm, humans often require a comprehensive unders…
View article: GuARD: Effective Anomaly Detection through a Text-Rich and Graph-Informed Language Model
GuARD: Effective Anomaly Detection through a Text-Rich and Graph-Informed Language Model Open
Anomaly detection on text-rich graphs is widely prevalent in real life, such as detecting incorrectly assigned academic papers to authors and detecting bots in social networks. The remarkable capabilities of large language models (LLMs) pa…
View article: IterSelectTune: An Iterative Training Framework for Efficient Instruction-Tuning Data Selection
IterSelectTune: An Iterative Training Framework for Efficient Instruction-Tuning Data Selection Open
As large language models (LLMs) continue to advance, instruction tuning has become critical for improving their ability to generate accurate and contextually appropriate responses. Although numerous instruction-tuning datasets have been de…
View article: Multimodal Clickbait Detection by De-confounding Biases Using Causal Representation Inference
Multimodal Clickbait Detection by De-confounding Biases Using Causal Representation Inference Open
This paper focuses on detecting clickbait posts on the Web. These posts often use eye-catching disinformation in mixed modalities to mislead users to click for profit. That affects the user experience and thus would be blocked by content p…
View article: A self-training interpretable cell type annotation framework using specific marker gene
A self-training interpretable cell type annotation framework using specific marker gene Open
Motivation Recent advances in sequencing technology provide opportunities to study biological processes at a higher resolution. Cell type annotation is an important step in scRNA-seq analysis, which often relies on established marker genes…
View article: Comprehensive single-cell RNA-seq analysis using deep interpretable generative modeling guided by biological hierarchy knowledge
Comprehensive single-cell RNA-seq analysis using deep interpretable generative modeling guided by biological hierarchy knowledge Open
Recent advances in microfluidics and sequencing technologies allow researchers to explore cellular heterogeneity at single-cell resolution. In recent years, deep learning frameworks, such as generative models, have brought great changes to…
View article: Business chatbots with deep learning technologies: state-of-the-art, taxonomies, and future research directions
Business chatbots with deep learning technologies: state-of-the-art, taxonomies, and future research directions Open
With the support of advanced hardware and software technology, Artificial Intelligence (AI) techniques, especially the increasing number of deep learning algorithms, have spawned the popularization of online intelligent services and accele…
View article: Contrastive learning for hierarchical topic modeling
Contrastive learning for hierarchical topic modeling Open
Topic models have been widely used in automatic topic discovery from text corpora, for which, the external linguistic knowledge contained in Pre-trained Word Embeddings (PWEs) is valuable. However, the existing Neural Topic Models (NTMs), …
View article: Two-Dimensional Data Partitioning for Non-Negative Matrix Tri-Factorization
Two-Dimensional Data Partitioning for Non-Negative Matrix Tri-Factorization Open
View article: Out-of-vocabulary word embedding learning based on reading comprehension mechanism
Out-of-vocabulary word embedding learning based on reading comprehension mechanism Open
Currently, most natural language processing tasks use word embeddings as the representation of words. However, when encountering out-of-vocabulary (OOV) words, the performance of downstream models that use word embeddings as input is often…
View article: Nonlinear Structural Equation Model Guided Gaussian Mixture Hierarchical Topic Modeling
Nonlinear Structural Equation Model Guided Gaussian Mixture Hierarchical Topic Modeling Open
Hierarchical topic models, which can extract semantically meaningful topics from a textcorpus in an unsupervised manner and automatically organise them into a topic hierarchy, have been widely used to discover the underlying semantic struc…
View article: Exploring Robust Overfitting for Pre-trained Language Models
Exploring Robust Overfitting for Pre-trained Language Models Open
We identify the robust overfitting issue for pre-trained language models by showing that the robust test loss increases as the epoch grows. Through comprehensive exploration of the robust loss on the training set, we attribute robust overf…
View article: Counterfactual Multihop QA: A Cause-Effect Approach for Reducing Disconnected Reasoning
Counterfactual Multihop QA: A Cause-Effect Approach for Reducing Disconnected Reasoning Open
Multi-hop QA requires reasoning over multiple supporting facts to answer the question. However, the existing QA models always rely on shortcuts, e.g., providing the true answer by only one fact, rather than multi-hop reasoning, which is re…
View article: Graph-based Relation Mining for Context-free Out-of-vocabulary Word Embedding Learning
Graph-based Relation Mining for Context-free Out-of-vocabulary Word Embedding Learning Open
The out-of-vocabulary (OOV) words are difficult to represent while critical to the performance of embedding-based downstream models. Prior OOV word embedding learning methods failed to model complex word formation well. In this paper, we p…
View article: Semi-Supervised Sentiment Classification and Emotion Distribution Learning Across Domains
Semi-Supervised Sentiment Classification and Emotion Distribution Learning Across Domains Open
In this study, sentiment classification and emotion distribution learning across domains are both formulated as a semi-supervised domain adaptation problem, which utilizes a small amount of labeled documents in the target domain for model …
View article: A deep data augmentation framework based on generative adversarial networks
A deep data augmentation framework based on generative adversarial networks Open
View article: Graph-based Dynamic Word Embeddings
Graph-based Dynamic Word Embeddings Open
As time goes by, language evolves with word semantics changing. Unfortunately, traditional word embedding methods neglect the evolution of language and assume that word representations are static. Although contextualized word embedding mod…
View article: Topic Driven Adaptive Network for Cross-Domain Sentiment Classification
Topic Driven Adaptive Network for Cross-Domain Sentiment Classification Open
Cross-domain sentiment classification has been a hot spot these years, which aims to learn a reliable classifier using labeled data from a source domain and evaluate it on a target domain. In this vein, most approaches utilized domain adap…
View article: Pronunciation-Enhanced Chinese Word Embedding
Pronunciation-Enhanced Chinese Word Embedding Open
Chinese word embeddings have recently garnered considerable attention. Chinese characters and their sub-character components, which contain rich semantic information, are incorporated to learn Chinese word embeddings. Chinese characters ca…
View article: Target-specified Sequence Labeling with Multi-head Self-attention for Target-oriented Opinion Words Extraction
Target-specified Sequence Labeling with Multi-head Self-attention for Target-oriented Opinion Words Extraction Open
Yuhao Feng, Yanghui Rao, Yuyao Tang, Ninghua Wang, He Liu. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.
View article: Lifelong Learning of Topics and Domain-Specific Word Embeddings
Lifelong Learning of Topics and Domain-Specific Word Embeddings Open
Lifelong topic models mainly focus on indomain text streams in which each chunk only contains documents from a single domain.To overcome data diversity of the in-domain corpus, most of the existing methods exploit the information from limi…
View article: Tree-Structured Topic Modeling with Nonparametric Neural Variational Inference
Tree-Structured Topic Modeling with Nonparametric Neural Variational Inference Open
Ziye Chen, Cheng Ding, Zusheng Zhang, Yanghui Rao, Haoran Xie. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Lo…
View article: Context Reinforced Neural Topic Modeling over Short Texts
Context Reinforced Neural Topic Modeling over Short Texts Open
As one of the prevalent topic mining tools, neural topic modeling has attracted a lot of interests for the advantages of high efficiency in training and strong generalisation abilities. However, due to the lack of context in each short tex…