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- Detect Rumors Using Time Series of Social Context Information on Microblogging Websites680
A novel approach to capture the temporal characteristics of features related to microblog contents, users and propagation patterns based on the time series of rumor's lifecycle, for which time series modeling technique is applied to incorporate various social context information.
- K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters648
K-Adapter is proposed, which remains the original parameters of the pre-trained model fixed and supports continual knowledge infusion and captures richer factual and commonsense knowledge than RoBERTa.
- Simplify the Usage of Lexicon in Chinese NER311
This work proposes a simple but effective method for incorporating the word lexicon into the character representations of Chinese named entity recognition using word lexicons, and shows that the proposed method can be easily incorporated with pre-trained models like BERT.
- Incorporating Corporation Relationship via Graph Convolutional Neural Networks for Stock Price Prediction265
Experiments on the data collected from stock market in Mainland China show that the representation learned from the model is able to capture relationships between corporations, and prediction models incorporating related corporations' information are able to make more accurate predictions on stock market.
- Task-oriented Dialogue System for Automatic Diagnosis255
Experimental results on this dialogue system show that additional symptoms extracted from conversation can greatly improve the accuracy for disease identification and the dialogue system is able to collect these symptoms automatically and make a better diagnosis.
- A Lexicon-Based Graph Neural Network for Chinese NER249
A lexicon-based graph neural network with global semantics is introduced, in which lexicon knowledge is used to connect characters to capture the local composition, while a global relay node can capture global sentence semantics and long-range dependency.
- DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services183
Quantitative and qualitative results on DISC-Law-Eval demonstrate the effectiveness of the system in serving various users across diverse legal scenarios, and enhances models' ability to access and utilize external legal knowledge.
- An end-to-end heterogeneous graph representation learning-based framework for drug–target interaction prediction179
A novel 'end-to-end' learning-based framework based on heterogeneous 'graph' convolutional networks for 'DTI' prediction called end- to-end graph (EEG)-DTI, which performs better than existing state-of-art methods for identifying DTIs.
- A learning-based framework for miRNA-disease association identification using neural networks178
A novel learning-based framework, MDA-CNN, is proposed, which outperforms some state-of-the-art approaches in a large margin on both tasks of miRNA-disease association prediction and mi RNA-phenotype association prediction.
- A Partition Filter Network for Joint Entity and Relation Extraction172
A partition filter network to model two-way interaction between tasks properly, where feature encoding is decomposed into two steps: partition and filter, and auxiliary experiments suggest that relation prediction is contributory to named entity prediction in a non-negligible way.
- Learning Implicit Sentiment in Aspect-based Sentiment Analysis with Supervised Contrastive Pre-Training149
Supervised Contrastive Pre-training on large-scale sentiment-annotated corpora retrieved from in-domain language resources leads to better capture of both implicit and explicit sentiment orientation towards aspects in reviews, and comprehensive analysis validates its effectiveness on learning implicit sentiment.
- 123
- From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents116
A comprehensive survey of simulation driven by large language models, illustrating the recent progress in simulation driven by LLM-empowered agents and categorizing the simulations into three types: Individual Simulation, which mimics specific individuals or demographic groups; Scenario Simulation, where multiple agents collaborate to achieve goals within specific contexts; and Society Simulation, which models interactions within agent societies.
- Logic-Driven Context Extension and Data Augmentation for Logical Reasoning of Text94
The results show that the proposed logic-driven data augmentation algorithm achieves state-of-the-art performance on both datasets, and even surpasses human performance on the ReClor dataset.
- 94
- A benchmark for automatic medical consultation system: frameworks, tasks and datasets93
This article creates a new large medical dialogue dataset with multi-level fine-grained annotations and establishes five independent tasks, including named entity recognition, dialogue act classification, symptom label inference, medical report generation and diagnosis-oriented dialogue policy.
- Unsupervised Discovery of Discourse Relations for Eliminating Intra-sentence Polarity Ambiguities92
Experimental results showed that the proposed methods not only effectively recognized the defined discourse relations but also achieved significant improvement by integrating discourse information in sentence-level polarity classification.
- SC2disease: a manually curated database of single-cell transcriptome for human diseases88
The literature prior to March 2020 for studies which used scRNA-seq to study diseases with human samples are reviewed, and the SC2disease database is developed to summarize all the data by different diseases, tissues and cell types.
- DISC-FinLLM: A Chinese Financial Large Language Model based on Multiple Experts Fine-tuning85
This work proposes Multiple Experts Fine-tuning Framework, a methodology that improves general LLMs by endowing them with multi-turn question answering abilities, domain text processing capabilities, mathematical computation skills, and retrieval-enhanced generation capabilities.
- Mask Attention Networks: Rethinking and Strengthen Transformer83
A novel understanding of SAN and FFN are presented as Mask Attention Networks (MANs) and it is shown that they are two special cases of MANs with static mask matrices, which limit the capability for localness modeling in text representation learning.
- 79
- Storytelling from an Image Stream Using Scene Graphs78
This work proposes a novel graph-based architecture for visual storytelling by modeling the two-level relationships on scene graphs and results indicate that this method achieves state-of-the-art results.
- Automatic Essay Scoring Incorporating Rating Schema via Reinforcement Learning77
A reinforcement learning framework for essay scoring that incorporates quadratic weighted kappa as guidance to optimize the scoring system and experiment results show the effectiveness of this framework.
- Enhancing Dialogue Symptom Diagnosis with Global Attention and Symptom Graph75
A global attention mechanism to capture more symptom related information, and a symptom graph to model the associations between symptoms rather than treating each symptom independently are proposed and shown to be effective to boost dialogue symptom diagnosis.
- Math Word Problem Solving with Explicit Numerical Values63
A novel approach called NumS2T is proposed, which enhances math word problem solving performance by explicitly incorporating numerical values into a sequence-to-tree network.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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