Enhanced Coherence-Aware Network with Hierarchical Disentanglement for Aspect-Category Sentiment Analysis

• URL: http://arxiv.org/abs/2403.10214v1
• Date: Fri, 15 Mar 2024 11:32:44 GMT
• Title: Enhanced Coherence-Aware Network with Hierarchical Disentanglement for Aspect-Category Sentiment Analysis
• Authors: Jin Cui, Fumiyo Fukumoto, Xinfeng Wang, Yoshimi Suzuki, Jiyi Li, Noriko Tomuro, Wanzeng Kong,
• Abstract summary: We propose an enhanced coherence-aware network with hierarchical disentanglement (ECAN) for ACSA tasks. Our ECAN effectively decouples multiple categories and sentiments entangled in the coherence representations and achieves state-of-the-art (SOTA) performance.
• Score: 12.024076910894417
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Aspect-category-based sentiment analysis (ACSA), which aims to identify aspect categories and predict their sentiments has been intensively studied due to its wide range of NLP applications. Most approaches mainly utilize intrasentential features. However, a review often includes multiple different aspect categories, and some of them do not explicitly appear in the review. Even in a sentence, there is more than one aspect category with its sentiments, and they are entangled intra-sentence, which makes the model fail to discriminately preserve all sentiment characteristics. In this paper, we propose an enhanced coherence-aware network with hierarchical disentanglement (ECAN) for ACSA tasks. Specifically, we explore coherence modeling to capture the contexts across the whole review and to help the implicit aspect and sentiment identification. To address the issue of multiple aspect categories and sentiment entanglement, we propose a hierarchical disentanglement module to extract distinct categories and sentiment features. Extensive experimental and visualization results show that our ECAN effectively decouples multiple categories and sentiments entangled in the coherence representations and achieves state-of-the-art (SOTA) performance. Our codes and data are available online: \url{https://github.com/cuijin-23/ECAN}.

Related papers

• Category-Extensible Out-of-Distribution Detection via Hierarchical Context Descriptions [35.20091752343433]
  This work introduces two hierarchical contexts, namely perceptual context and spurious context, to carefully describe the precise category boundary. The two contexts hierarchically construct the precise description for a certain category, which is first roughly classifying a sample to the predicted category. The precise descriptions for those categories within the vision-language framework present a novel application: CATegory-EXtensible OOD detection (CATEX)
  arXiv  Detail & Related papers  (2024-07-23T12:53:38Z)
• iACOS: Advancing Implicit Sentiment Extraction with Informative and Adaptive Negative Examples [2.0249250133493195]
  We propose a new method iACOS for extracting Implicit Aspects with Categories and Opinions with Sentiments. iACOS appends two implicit tokens at the end of a text to capture the context-aware representation of all tokens including implicit aspects and opinions. We show that iACOS significantly outperforms other quadruple extraction baselines according to the F1 score on two public benchmark datasets.
  arXiv  Detail & Related papers  (2023-11-07T11:19:06Z)
• AttrSeg: Open-Vocabulary Semantic Segmentation via Attribute Decomposition-Aggregation [33.25304533086283]
  Open-vocabulary semantic segmentation is a challenging task that requires segmenting novel object categories at inference time. Recent studies have explored vision-language pre-training to handle this task, but suffer from unrealistic assumptions in practical scenarios. This work proposes a novel attribute decomposition-aggregation framework, AttrSeg, inspired by human cognition in understanding new concepts.
  arXiv  Detail & Related papers  (2023-08-31T19:34:09Z)
• Prototype-based Embedding Network for Scene Graph Generation [105.97836135784794]
  Current Scene Graph Generation (SGG) methods explore contextual information to predict relationships among entity pairs. Due to the diverse visual appearance of numerous possible subject-object combinations, there is a large intra-class variation within each predicate category. Prototype-based Embedding Network (PE-Net) models entities/predicates with prototype-aligned compact and distinctive representations. PL is introduced to help PE-Net efficiently learn such entitypredicate matching, and Prototype Regularization (PR) is devised to relieve the ambiguous entity-predicate matching.
  arXiv  Detail & Related papers  (2023-03-13T13:30:59Z)
• Semantic Representation and Dependency Learning for Multi-Label Image Recognition [76.52120002993728]
  We propose a novel and effective semantic representation and dependency learning (SRDL) framework to learn category-specific semantic representation for each category. Specifically, we design a category-specific attentional regions (CAR) module to generate channel/spatial-wise attention matrices to guide model. We also design an object erasing (OE) module to implicitly learn semantic dependency among categories by erasing semantic-aware regions.
  arXiv  Detail & Related papers  (2022-04-08T00:55:15Z)
• Category Contrast for Unsupervised Domain Adaptation in Visual Tasks [92.9990560760593]
  We propose a novel Category Contrast technique (CaCo) that introduces semantic priors on top of instance discrimination for visual UDA tasks. CaCo is complementary to existing UDA methods and generalizable to other learning setups such as semi-supervised learning, unsupervised model adaptation, etc.
  arXiv  Detail & Related papers  (2021-06-05T12:51:35Z)
• Weakly-Supervised Aspect-Based Sentiment Analysis via Joint Aspect-Sentiment Topic Embedding [71.2260967797055]
  We propose a weakly-supervised approach for aspect-based sentiment analysis. We learn sentiment, aspect> joint topic embeddings in the word embedding space. We then use neural models to generalize the word-level discriminative information.
  arXiv  Detail & Related papers  (2020-10-13T21:33:24Z)
• Multi-Instance Multi-Label Learning Networks for Aspect-Category Sentiment Analysis [8.378067521821045]
  We propose a Multi-Instance Multi-Label Learning Network for Aspect-Category sentiment analysis (AC-MIMLLN) AC-MIMLLN treats sentences as bags, words as instances, and the words indicating an aspect category as the key instances of the aspect category. Experimental results on three public datasets demonstrate the effectiveness of AC-MIMLLN.
  arXiv  Detail & Related papers  (2020-10-06T12:07:54Z)
• Dynamic Semantic Matching and Aggregation Network for Few-shot Intent Detection [69.2370349274216]
  Few-shot Intent Detection is challenging due to the scarcity of available annotated utterances. Semantic components are distilled from utterances via multi-head self-attention. Our method provides a comprehensive matching measure to enhance representations of both labeled and unlabeled instances.
  arXiv  Detail & Related papers  (2020-10-06T05:16:38Z)
• Sentence Constituent-Aware Aspect-Category Sentiment Analysis with Graph Attention Networks [9.287196185066565]
  Aspect category sentiment analysis aims to predict the sentiment polarities of the aspect categories discussed in sentences. We propose a Sentence Constituent-Aware Network (SCAN) for aspect-category sentiment analysis.
  arXiv  Detail & Related papers  (2020-10-04T01:23:17Z)
• ConsNet: Learning Consistency Graph for Zero-Shot Human-Object Interaction Detection [101.56529337489417]
  We consider the problem of Human-Object Interaction (HOI) Detection, which aims to locate and recognize HOI instances in the form of human, action, object> in images. We argue that multi-level consistencies among objects, actions and interactions are strong cues for generating semantic representations of rare or previously unseen HOIs. Our model takes visual features of candidate human-object pairs and word embeddings of HOI labels as inputs, maps them into visual-semantic joint embedding space and obtains detection results by measuring their similarities.
  arXiv  Detail & Related papers  (2020-08-14T09:11:18Z)

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.