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- Detecting rumors from microblogs with recurrent neural networks1,359
A novel method that learns continuous representations of microblog events for identifying rumors based on recurrent neural networks that detects rumors more quickly and accurately than existing techniques, including the leading online rumor debunking services.
- Interpreting TF-IDF term weights as making relevance decisions880
A novel probabilistic retrieval model forms a basis to interpret the TF-IDF term weights as making relevance decisions, and it is shown that the term-frequency factor of the ranking formula can be rendered into different term- frequency factors of existing retrieval systems.
- 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.
- Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning661
A kernel-based method is proposed, which captures high-order patterns differentiating different types of rumors by evaluating the similarities between their propagation tree structures, and can detect rumors more quickly and accurately than state-of-the-art rumor detection models.
- Document Analysis System635
The requirements and components for a proposed Document Analysis System, which assists a user in encoding printed documents for computer processing, are outlined and several critical functions have been investigated and the technical approaches are discussed.
- Rumor Detection on Twitter with Tree-structured Recursive Neural Networks634
This work proposes two recursive neural models based on a bottom-up and a top-down tree-structured neural networks for rumor representation learning and classification, which naturally conform to the propagation layout of tweets.
- Detect Rumors on Twitter by Promoting Information Campaigns with Generative Adversarial Learning281
This paper proposes a GAN-style approach, where a generator is designed to produce uncertain or conflicting voices, complicating the original conversational threads in order to pressurize the discriminator to learn stronger rumor indicative representations from the augmented, more challenging examples.
- 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.
- Extractive summarization using supervised and semi-supervised learning238
This paper investigates co-training by combining labeled and unlabeled data and shows that this semi-supervised learning approach achieves comparable performance to its supervised counterpart and saves about half of the labeling time cost.
- Detect Rumor and Stance Jointly by Neural Multi-task Learning235
Extensive experiments on real-world datasets gathered from Twitter and news portals demonstrate that the proposed framework improves both rumor detection and stance classification tasks consistently with the help of the strong inter-task connections, achieving much better performance than state-of-the-art methods.
- Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning197
This paper addresses the travel planning task by formulating the task in the mathematical framework of options over Markov Decision Processes (MDPs), and proposing a hierarchical deep reinforcement learning approach to learning a dialogue manager that operates at different temporal scales.
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- Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy Learning183
Deep Dyna-Q is presented, which to the authors' knowledge is the first deep RL framework that integrates planning for task-completion dialogue policy learning and incorporates into the dialogue agent a model of the environment, referred to as the world model, to mimic real user response and generate simulated experience.
- Web 2.0 Environmental Scanning and Adaptive Decision Support for Business Mergers and Acquisitions1136
The managerial implication of the findings is that firms can apply the proposed BI 2.0 technology to enhance their strategic decision making, particularly when making cross-border investments in targeted markets for which private information may not be readily available.
- Development of a knowledge management initiative and system: A case study136
The results of a case study conducted in one company in the United Kingdom (UK) are presented, the major aim being to identify how it has developed a KM initiative and system.
- Sentence-Level Evidence Embedding for Claim Verification with Hierarchical Attention Networks124
Experimental results on three public benchmark datasets show that the proposed end-to-end hierarchical attention network focusing on learning to represent coherent evidence as well as their semantic relatedness with the claim outperforms a set of state-of-the-art baselines.
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- A genetic algorithm-based clustering approach for database partitioning112
This work explores the use of a genetic search-based clustering algorithm for data partitioning to achieve high database retrieval performance and formulating the underlying problem as a traveling salesman problem (TSP) to take advantage of this particular structure.
- Cue-CoT: Chain-of-thought Prompting for Responding to In-depth Dialogue Questions with LLMs109
A novel linguistic cue-based chain-of-thoughts (\textit{Cue}-CoT) method is proposed, which enhances the LLMs inference with an intermediate reasoning step to find cues exhibited in the dialogue, aiming to provide a more personalized and engaging response.
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- 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.
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- FACOPT: a user friendly FACility layout OPTimization system89
FACOPT, a heuristic approach that is effective and user friendly for facility layout that uses two different approaches to solve the facility layout problem effectively is presented.
- Quotation Recommendation for Multi-party Online Conversations Based on Semantic and Topic Fusion87
This work studies automatic quotation recommendation for online conversations by adopting topic-level representation to facilitate the recommendation and conducting extensive quantitative experiments to investigate the difficulty of the quotation recommendation task, validate the topic-based recommendation assumption, and explore the stability of the recommendation.
- Phoneme-Based Transliteration of Foreign Names for OOV Problem81
This work presents a statistical transliteration method for automatic translation according to pronunciation similarities, i.e. to map phonemes comprising an English name to the phonetic representations of the corresponding Chinese name.
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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