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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.
- An Attention-based Rumor Detection Model with Tree-structured Recursive Neural Networks76
This work proposes to learn discriminative features from microblog posts by following their non-sequential propagation structure and generate more powerful representations for identifying rumors, and reveals that effective rumor detection is highly related to finding evidential posts.
- Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning76
This paper presents a new method - adversarial advantage actor-critic (Adversarial A2C), which significantly improves the efficiency of dialogue policy learning in task-completion dialogue systems.
- A Survey on Recent Advances and Challenges in Reinforcement Learning Methods for Task-oriented Dialogue Policy Learning72
An overview of the recent advances and challenges in dialogue policy from the perspective of RL is presented and a comprehensive survey of applying RL to DPL is provided by categorizing recent methods into five basic elements in RL.
- Convolution-based Memory Network for Aspect-based Sentiment Analysis65
A novel convolutional memory network which incorporates an attention mechanism is proposed which sequentially computes the weights of multiple memory units corresponding to multi-words for aspect-based sentiment analysis.
- Dynamic joint sentiment-topic model65
A dynamic Joint Sentiment-Topic model (dJST) is proposed which allows the detection and tracking of views of current and recurrent interests and shifts in topic and sentiment and shows the effectiveness on the Mozilla add-on reviews crawled between 2007 and 2011.
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- A Unified Graph Model for Sentence-Based Opinion Retrieval62
A sentence-based approach based on a new information representation, namely topic-sentiment word pair, to capture intra-sentence contextual information between an opinion and its target and experimental results showed that the graph-based model achieved significant improvement.
- Recurrent Neural Networks with External Memory for Spoken Language Understanding61
This work proposes to use an external memory to improve memorisation capability of RNNs and demonstrates that the proposed model was able to achieve the state-of-the-art results.
- Improving Rumor Detection by Promoting Information Campaigns With Transformer-Based Generative Adversarial Learning57
This paper attempts to combat such chaotic phenomenon with a countermeasure by mirroring against how such chaos is created in order to make automatic rumor detection more robust and effective, and reveals that feature learning effectiveness is highly relevant to the quality of generated parody, viz., how hard it is to get distinguished from real posts.
- Sentiment vector space model for lyric-based song sentiment classification55
The preliminary experiments prove that the s-VSM model outperforms the VSM model in the lyric-based song sentiment classification task.
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- Using contextual analysis for news event detection51
The results show that the performance of detection using concept terms together with story keywords is better than traditional methods which only use keyword representation.
- Approximate Graph Schema Extraction for Semi-structured Data51
An approximation approach is proposed for graph schema extraction by summarizing the semi-structured data graph using an incremental clustering method and results have shown that approximate graph schemas were more compact than the conventional accurategraph schemas and promising in query evaluation that involved regular path expressions.
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- Learning to rank only using training data from related domain40
This work proposes to exploit training data annotated for a related domain to learn to rank retrieved documents in the target domain, in which no labeled data is available, and presents a simple yet effective approach based on instance-weighting scheme.
- Applying machine learning to Chinese temporal relation resolution39
A computational model based on machine learning and heterogeneous collaborative bootstrapping is proposed for analyzing temporal relations in a Chinese multiple-clause sentence, which takes into account the effects of linguistic features such as tense/aspect, temporal connectives, and discourse structures.
- WatME: Towards Lossless Watermarking Through Lexical Redundancy37
A novel approach leveraging linguistic prior knowledge of inherent lexical redundancy in LLM vocabularies to seamlessly integrate watermarks is introduced, which effectively prevents the unavailability of appropriate tokens and preserves the expressive power of LLMs.
- A retrospective study of a hybrid document-context based retrieval model37
The experiments showed that (a) the operators that are consistent with aggregate relevance principle were effective in combining the estimated preferences, and (b) that estimating probabilities using the contexts in the relevant documents can produce better retrieval effectiveness than using the entire relevant documents.
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- Acting Less is Reasoning More! Teaching Model to Act Efficiently36
This work proposes Optimal Tool Call-controlled Policy Optimization (OTC-PO), a simple yet effective RL-based framework that encourages models to produce accurate answers with minimal tool calls and is the first RL-based framework that explicitly optimizes tool-use efficiency in TIR 1.
- Exploiting query logs for cross-lingual query suggestions36
This article presents an effective means to map the input query of one language to queries of the other language in the query log of cross-lingual query suggestion (CLQS), and finds that when combined with pseudo-relevance feedback, the effectiveness of CLIR using CLQS is enhanced for different pairs of languages.
- Parallel Database Techniques36
This book presents a number of state of the art techniques that can be adopted in the design of parallel database software and hardware execution environments and can directly or indirectly lead to high-performance parallel database implementation.
- Extracting common emotions from blogs based on fine-grained sentiment clustering34
A novel method based on Probabilistic Latent Semantic Analysis (PLSA) is presented to model the hidden sentiment factors and an emotion-oriented clustering approach is proposed to find common emotions according to the fine-grained sentiment similarity between blogs.
- Product recommendation algorithms in the age of omnichannel retailing – An intuitive clustering approach33
This work adopts an intuitive co-clustering algorithm for locating useful patterns in the matrix that is intuitive rather than a black box type and thus may increase the chances of it being actually adopted.
- Topic Extraction from Microblog Posts Using Conversation Structures33
This work organize microblog posts as conversation trees based on reposting and replying relations, which enrich context information to alleviate data sparseness and captures the different extents that leader and follower messages may contain the key topical words, thus further enhances the quality of the induced topics.
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- Improving Conversational Recommender System Via Contextual and Time-Aware Modeling With Less Domain-Specific Knowledge26
This work captures both entity-level and contextual-level representations to jointly model user preferences for the recommendation, and uses the pre-trained BART to initialize the generation module to alleviate the data scarcity and enhance the context modeling.
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- Vulnerability-Aware Alignment: Mitigating Uneven Forgetting in Harmful Fine-Tuning22
This work reveals that certain subsets of alignment data are consistently more prone to forgetting during HFT across different fine-tuning tasks, and proposes Vulnerability-Aware Alignment (VAA), which estimates data vulnerability, partitions data into vulnerable groups and encourages balanced learning using a group distributionally robust optimization (Group DRO) framework.
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- Improving Transliteration with Precise Alignment of Phoneme Chunks and Using Contextual Features18
This paper presents a direct probabilistic transliteration model using contextual features of phonemes with a tailored alignment scheme for phoneme chunks with superior performance over the source-channel for the task of English-Chinese transliterations.
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- Large Language Models as Source Planner for Personalized Knowledge-grounded Dialogue9
SAFARI is a novel framework that decouples the knowledge grounding into multiple sources and response generation, which allows easy extension to various knowledge sources including the possibility of not using any sources.
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- Towards Real-World Stickers Use: A New Dataset for Multi-Tag Sticker Recognition5
StickerTAG is introduced, the first multi-tag sticker dataset comprising a collected tag set with 461 tags and 13,571 sticker-tag pairs, designed to provide a deeper understanding of stickers.
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- Chinese document indexing based on a new partitioned signature file: Model and evaluation5
This article investigates the use of signature files in Chinese information retrieval system and proposes a new partitioning method for Chinese signature file based on the characteristic of Chinese words, called Partitioned Signature File for Chinese (PSFC), which offers faster search efficiency than the traditional single signature file approach.
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- Señorita-2M: A High-Quality Instruction-based Dataset for General Video Editing by Video Specialists4
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- Self-DC: When to Reason and When to Act? Self Divide-and-Conquer for Compositional Unknown Questions3
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- Information Retrieval Technology : 4th Asia Information Retrieval Symposium, AIRS 2008: Harbin, China, January 15-18, 2008: Revised Selected Papers2
Improving Expertise Recommender Systems by Odds Ratio and Improving Spamdexing Detection Via a Two-Stage Classification Strategy are explored.
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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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