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- A composite kernel to extract relations between entities with both flat and structured features286
This study illustrates that the composite kernel can effectively capture both flat and structured features without the need for extensive feature engineering, and can also easily scale to include more features.
- Code Bad Smells: a review of current knowledge213
A systematic literature review of 319 papers published since Fowler et al. identified Code Bad Smells suggests that there is little evidence currently available to justify using Code Good Smells.
- Large Language Model Cascades with Mixture of Thoughts Representations for Cost-efficient Reasoning195
This paper proposes a cascade pipeline that follows the intuition that simpler questions can be addressed by a weaker but more affordable LLM, whereas only the challenging questions necessitate the stronger and more expensive LLM.
- Joint Multi-modal Aspect-Sentiment Analysis with Auxiliary Cross-modal Relation Detection168
This paper is the first to jointly perform multi-modal ATE (MATE) and multi- modal ASC (MASC), and it proposes a multi-Modal joint learning approach with auxiliary cross-modAL relation detection for multi- Modal aspect-level sentiment analysis (MALSA).
- RaSa: Relation and Sensitivity Aware Representation Learning for Text-based Person Search150
A Relation and Sensitivity aware representation learning method that outperforms existing state-of-the-art methods by 6.94%, 4.45% and 15.35% in terms of Rank@1 on CUHK-P EDES, ICFG-PEDES and RSTPReid datasets, respectively.
- A generation model to unify topic relevance and lexicon-based sentiment for opinion retrieval132
A novel generation model that unifies topic-relevance and opinion generation by a quadratic combination is proposed and demonstrates that in the opinion retrieval task, a Bayesian approach to combining multiple ranking functions is superior to using a linear combination.
- Do users rate or review?: boost phrase-level sentiment labeling with review-level sentiment classification125
This paper attempts to bridge the gap between phrase-level and review/document- level sentiment analysis by leveraging the results given by review-level sentiment classification to boost phrase- Level sentiment polarity labeling in contextual sentiment lexicon construction tasks, using a novel constrained convex optimization framework.
- Aspect Sentiment Classification with both Word-level and Clause-level Attention Networks105
The need for incorporating the importance degrees of both words and clauses inside a sentence is highlighted and a hierarchical network with both word-level and clause-level attentions to aspect sentiment classification is proposed.
- Code Summarization with Structure-induced Transformer102
ructure-induced Transformer is proposed, which encodes sequential code inputs with multi-view structural clues in terms of a newly-proposed structure-induced self-attention mechanism, and helps achieve new state-of-the-art results on benchmarks.
- Understanding User Experience in Large Language Model Interactions97
This study develops a taxonomy of 7 user intents in LLM interactions, grounded in analysis of real-world user interaction logs and human verification, and conducts a user survey to gauge their satisfaction with LLM services, encompassing usage frequency, experiences across intents, and predominant concerns.
- Emotion Tokens: Bridging the Gap among Multilingual Twitter Sentiment Analysis97
This paper focuses on sentiment analysis for Twitter messages (tweets) by the analysis of emotion tokens, including emotion symbols, irregular forms of words and combined punctuations, which become a useful signal for sentiment analysis on multilingual tweets.
- Overview of the NTCIR-9 INTENT Task90
This is an overview of the NTCIR-9 INTENT task, which comprises the Subtopic Mining and the Document Ranking subtask, and describes the subtasks, data and evaluation methods, and reports on the results.
- Microblog Sentiment Analysis with Emoticon Space Model78
The emoticon space model (ESM) is proposed that leverages more emoticons to construct word representations from a massive amount of unlabeled data and helps identify subjectivity, polarity, and emotion in microblog environments.
- White Blood Cells Classification with Deep Convolutional Neural Networks72
The necessary step in the diagnosis of leukemia by the attending physician is to classify the white blood cells in the bone marrow, which requires the attending doctor to have a wealth of clinic experience.
- Overview of the NTCIR-11 IMine Task72
An overview of the NTCIR IMine task, which is a core task of NTCir-11 and also a succeeding work of INTENT@NTCIR-9 and INTENT2@N TCIR-10 tasks, and describes the task details, annotation of results, evaluation strategies and then the official evaluation results.
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- From Skimming to Reading: A Two-stage Examination Model for Web Search70
This study designs an experimental search engine to collect both the user's feedback on their examinations and the eye-tracking/click-through data and proposes a two-stage examination model, which suggests that users make judgments according to different signals in different stages.
- Aspect Sentiment Classification with Document-level Sentiment Preference Modeling69
A Cooperative Graph Attention Networks (CoGAN) approach for cooperatively learning the aspect-related sentence representation and the importance of the document-level sentiment preference information to ASC is justified and the effectiveness of the approach capturing such information is demonstrated.
- Overview of the NTCIR-10 INTENT-2 Task.66
This paper provides an overview of the NTCIR-10 INTENT-2 task (the second INTENT task), which comprises the Subtopic Mining and the Document Ranking subtask, and describes the subtasks, data and evaluation methods.
- Expressive and Secure Searchable Encryption in the Public Key Setting (Full Version).65
This paper presents an expressive and secure asymmetric searchable encryption (ESASE) scheme, which is the first that simultaneously supports conjunctive, disjunctive and negation search operations, and analyzes the efficiency and proves it is secure in the standard model.
- Towards Reasoning in Large Language Models via Multi-Agent Peer Review Collaboration64
This work introduces a multi-agent collaboration strategy that emulates the academic peer review process and shows that the collaboration approach delivers superior accuracy across all ten datasets compared to existing methods.
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- A multicenter clinical AI system study for detection and diagnosis of focal liver lesions62
The evidence suggests that LiAIDS can serve as a routine diagnostic tool and enhance the diagnostic capabilities of radiologists for liver lesions, and with the assistance of LiAIDS, the diagnostic accuracy of all radiologists improved.
- Grey Wolf Optimization algorithm based on Cauchy-Gaussian mutation and improved search strategy61
The paper proposed a Grey Wolf Optimization algorithm based on Cauchy-Gaussian mutation and improved search strategy (CG-GWO), introduced to increase the population diversity of the leader wolves and improve the global search ability of the algorithm.
- An Analysis of BERT in Document Ranking55
A state-of-the-art BERT-based ranking model with focus on its attention mechanism and interaction behavior is investigated and it is indicated that it is possible to transform BERT into a more efficient representation-focused model.
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 2026-10-14. 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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