Dependency Structure Augmented Contextual Scoping Framework for Multimodal Aspect-Based Sentiment Analysis

• URL: http://arxiv.org/abs/2504.11331v1
• Date: Tue, 15 Apr 2025 16:05:09 GMT
• Title: Dependency Structure Augmented Contextual Scoping Framework for Multimodal Aspect-Based Sentiment Analysis
• Authors: Hao Liu, Lijun He, Jiaxi Liang, Zhihan Ren, Fan Li,
• Abstract summary: Multimodal Aspect-Based Sentiment Analysis (MABSA) seeks to extract fine-grained information from image-text pairs.<n>DASCO is a fine-grained scope-oriented framework that enhances aspect-level sentiment reasoning by leveraging dependency parsing trees.<n>Experiments on two benchmark datasets demonstrate that DASCO achieves state-of-the-art performance in MABSA.
• Score: 9.561100210295699
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal Aspect-Based Sentiment Analysis (MABSA) seeks to extract fine-grained information from image-text pairs to identify aspect terms and determine their sentiment polarity. However, existing approaches often fall short in simultaneously addressing three core challenges: Sentiment Cue Perception (SCP), Multimodal Information Misalignment (MIM), and Semantic Noise Elimination (SNE). To overcome these limitations, we propose DASCO (\textbf{D}ependency Structure \textbf{A}ugmented \textbf{Sco}ping Framework), a fine-grained scope-oriented framework that enhances aspect-level sentiment reasoning by leveraging dependency parsing trees. First, we designed a multi-task pretraining strategy for MABSA on our base model, combining aspect-oriented enhancement, image-text matching, and aspect-level sentiment-sensitive cognition. This improved the model's perception of aspect terms and sentiment cues while achieving effective image-text alignment, addressing key challenges like SCP and MIM. Furthermore, we incorporate dependency trees as syntactic branch combining with semantic branch, guiding the model to selectively attend to critical contextual elements within a target-specific scope while effectively filtering out irrelevant noise for addressing SNE problem. Extensive experiments on two benchmark datasets across three subtasks demonstrate that DASCO achieves state-of-the-art performance in MABSA, with notable gains in JMASA (+3.1\% F1 and +5.4\% precision on Twitter2015).

Related papers

• Unsupervised Robust Cross-Lingual Entity Alignment via Neighbor Triple Matching with Entity and Relation Texts [17.477542644785483]
  Cross-lingual entity alignment (EA) enables the integration of multiple knowledge graphs (KGs) across different languages.<n>EA pipeline that jointly performs entity-level and Relation-level Alignment by neighbor triple matching strategy.
  arXiv  Detail & Related papers  (2024-07-22T12:25:48Z)
• Revisiting Structured Sentiment Analysis as Latent Dependency Graph Parsing [38.27437431585664]
  Internal structures of spans are neglected, thus only the boundary tokens of spans are used for relation prediction and span recognition. Long spans occupy a significant proportion in the SSA datasets, which further exacerbates the problem of internal structure neglect. We propose a two-stage parsing method and leverage TreeCRFs with a novel constrained inside algorithm to model latent structures explicitly.
  arXiv  Detail & Related papers  (2024-07-05T18:18:50Z)
• RNG: Reducing Multi-level Noise and Multi-grained Semantic Gap for Joint Multimodal Aspect-Sentiment Analysis [27.545702415272125]
  We propose a novel framework named RNG for Joint Multimodal Aspect-Sentiment Analysis (JMASA) Specifically, to reduce multi-level modality noise and multi-grained semantic gap, we design three constraints. Experiments on two datasets validate our new state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-05-20T12:18:46Z)
• Amplifying Aspect-Sentence Awareness: A Novel Approach for Aspect-Based Sentiment Analysis [2.9045498954705886]
  Aspect-Based Sentiment Analysis (ABSA) is increasingly crucial in Natural Language Processing (NLP) ABSA goes beyond traditional sentiment analysis by extracting sentiments related to specific aspects mentioned in the text. We propose Amplifying Aspect-Sentence Awareness (A3SN), a novel technique designed to enhance ABSA through amplifying aspect-sentence awareness attention.
  arXiv  Detail & Related papers  (2024-05-14T10:29:59Z)
• A Novel Energy based Model Mechanism for Multi-modal Aspect-Based Sentiment Analysis [85.77557381023617]
  We propose a novel framework called DQPSA for multi-modal sentiment analysis. PDQ module uses the prompt as both a visual query and a language query to extract prompt-aware visual information. EPE module models the boundaries pairing of the analysis target from the perspective of an Energy-based Model.
  arXiv  Detail & Related papers  (2023-12-13T12:00:46Z)
• M$^3$Net: Multi-view Encoding, Matching, and Fusion for Few-shot Fine-grained Action Recognition [80.21796574234287]
  M$3$Net is a matching-based framework for few-shot fine-grained (FS-FG) action recognition. It incorporates textitmulti-view encoding, textitmulti-view matching, and textitmulti-view fusion to facilitate embedding encoding, similarity matching, and decision making. Explainable visualizations and experimental results demonstrate the superiority of M$3$Net in capturing fine-grained action details.
  arXiv  Detail & Related papers  (2023-08-06T09:15:14Z)
• Multi-Grained Multimodal Interaction Network for Entity Linking [65.30260033700338]
  Multimodal entity linking task aims at resolving ambiguous mentions to a multimodal knowledge graph. We propose a novel Multi-GraIned Multimodal InteraCtion Network $textbf(MIMIC)$ framework for solving the MEL task.
  arXiv  Detail & Related papers  (2023-07-19T02:11:19Z)
• A semantically enhanced dual encoder for aspect sentiment triplet extraction [0.7291396653006809]
  Aspect sentiment triplet extraction (ASTE) is a crucial subtask of aspect-based sentiment analysis (ABSA) Previous research has focused on enhancing ASTE through innovative table-filling strategies. We propose a framework that leverages both a basic encoder, primarily based on BERT, and a particular encoder comprising a Bi-LSTM network and graph convolutional network (GCN) Experiments conducted on benchmark datasets demonstrate the state-of-the-art performance of our proposed framework.
  arXiv  Detail & Related papers  (2023-06-14T09:04:14Z)
• Understanding and Constructing Latent Modality Structures in Multi-modal Representation Learning [53.68371566336254]
  We argue that the key to better performance lies in meaningful latent modality structures instead of perfect modality alignment. Specifically, we design 1) a deep feature separation loss for intra-modality regularization; 2) a Brownian-bridge loss for inter-modality regularization; and 3) a geometric consistency loss for both intra- and inter-modality regularization.
  arXiv  Detail & Related papers  (2023-03-10T14:38:49Z)
• Learning Relation Alignment for Calibrated Cross-modal Retrieval [52.760541762871505]
  We propose a novel metric, Intra-modal Self-attention Distance (ISD), to quantify the relation consistency by measuring the semantic distance between linguistic and visual relations. We present Inter-modal Alignment on Intra-modal Self-attentions (IAIS), a regularized training method to optimize the ISD and calibrate intra-modal self-attentions mutually via inter-modal alignment.
  arXiv  Detail & Related papers  (2021-05-28T14:25:49Z)
• A Dependency Syntactic Knowledge Augmented Interactive Architecture for End-to-End Aspect-based Sentiment Analysis [73.74885246830611]
  We propose a novel dependency syntactic knowledge augmented interactive architecture with multi-task learning for end-to-end ABSA. This model is capable of fully exploiting the syntactic knowledge (dependency relations and types) by leveraging a well-designed Dependency Relation Embedded Graph Convolutional Network (DreGcn) Extensive experimental results on three benchmark datasets demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2020-04-04T14:59:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.