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Works48 from public data
- A survey of human-in-the-loop for machine learning829
This paper surveys existing works on human-in-the-loop from a data perspective and classify them into three categories with a progressive relationship: the work of improving model performance from data processing, the work through interventional model training, and the design of the system independent human- in- the-loop.
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- Cross-modal fine-grained alignment and fusion network for multimodal aspect-based sentiment analysis92
- Exploring Cognitive and Aesthetic Causality for Multimodal Aspect-Based Sentiment Analysis52
Chimera is presented, a framework to derive fine-grained holistic features of aspects and infer the fundamental drivers of sentiment expression from both semantic perspectives and affective-cognitive resonance (the synergistic effect between emotional responses and cognitive interpretations).
- Multi-Channel Attentive Graph Convolutional Network with Sentiment Fusion for Multimodal Sentiment Analysis42
A Multi-channel Attentive Graph Convolutional Network (MAGCN), consisting of two main components: cross-modality interactive learning and sentimental feature fusion, that achieves competitive performance on accuracy and F1 scores compared to several state-of-the-art approaches.
- Targeted Sentiment Classification Based on Attentional Encoding and Graph Convolutional Networks42
This paper considers that the semantic information, syntactic information, and their interaction information are very crucial to targeted sentiment analysis, and proposes an attentional-encoding-based graph convolutional network (AEGCN) model that is mainly composed of multi-head attention and an improved graph convolutionsal network built over the dependency tree of a sentence.
- Multi-head self-attention based gated graph convolutional networks for aspect-based sentiment classification33
A novel gated graph convolutional networks on the dependency tree to encode syntactical information, and a Syntax-aware Context Dynamic Weighted layer is designed to guide the model to pay more attention to the local syntax-aware context.
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- Electroencephalogram Access for Emotion Recognition Based on a Deep Hybrid Network31
A method of EEG access for emotion recognition based on a deep hybrid network was proposed, and the accuracy was improved by 0.99% on valence and 14.58% on dominance, which verified the effectiveness of the proposed method.
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- Bayesian estimation‐based sentiment word embedding model for sentiment analysis20
Bayesian estimation ‐ based sentiment word embedding (BESWE) model, which aims to precisely extract the sentiment information of low ‐ frequency words, has been proposed and shows that BESWE outperforms many state ‐ of ‐ the ‐ art methods, for example, C&W, CBOW, GloVe, SE ‐ HyRank and DLJT1 in sentiment analysis tasks.
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- Progressive Evidence Refinement for Open-domain Multimodal Retrieval Question Answering7
A progressive evidence refinement strategy for selecting crucial evidence and a multi-turn retrieval and question-answering strategy to handle multimodal inputs to mitigate the loss of fine-grained information are proposed.
- Cross-domain document layout analysis using document style guide6
This paper integrated the document quality assessment and the document cross-domain analysis into a unified framework that is composed of three components, Document Layout Generator, Document Elements Decorator, and Document Style Discriminator.
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- Bidirectional Directed Acyclic Graph Neural Network for Aspect-level Sentiment Classification5
A Bidirectional Directed Acyclic Graph (BDAG) to reconstruct syntactic dependencies and a Bidirectional Directed Acyclic Graph Neural Network (BDAGNN) to efficiently propagate multi-hop sentiment information are proposed.
- Imaginique Expressions: Tailoring Personalized Short-Text-to-Image Generation Through Aesthetic Assessment and Human Insights4
This work proposes a novel AI methodology: personalized short-text-to-image generation through aesthetic assessment and human insights, which harmonizes aesthetic preferences with the generative process by adapting the stable diffusion framework to align with personalized assessments.
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- DCQA: Document-Level Chart Question Answering towards Complex Reasoning and Common-Sense Understanding3
The goal of this task is to conduct document-level question answering, extracting charts or plots in the document via document layout analysis (DLA) first and subsequently performing chart question answering (CQA), and an OCR-free transformer for document- level chart-oriented understanding is devised.
- Aspect-Level Sentiment Analysis with Local Semantic and Global Syntactic Features Integration3
A local semantic and global syntactic integration scheme, which employs a local focus mechanism over local context words and exploits improved graph convolutional networks over dependency tree to encode global Syntactic information.
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- Aspect-Based Summarization with Self-Aspect Retrieval Enhanced Generation2
This paper proposes a novel framework for aspect-based summarization: Self-Aspect Retrieval Enhanced Summary Generation, which employs an embedding-driven retrieval mechanism to identify its relevant text segments and mitigates the token limitation problem.
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- Large Language Models have Chain-of-Affect1
This work introduces the concept of the chain-of-affect (CoA), a temporally extended affective process through which LLMs develop state-like behavioral tendencies that shape generation, user experience, and collective dynamics.
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- Research on Corpus Construction for Petrochemical Industry–
This paper tries to take advantage of both introspection and statistic method to present a framework of domain corpus, which includes four levels, such as raw corpus level,lexical level, sentence level and chapter level, which can provide service for the higher and NLP analysis.
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 2026-10-12. 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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