Opinion Transmission Network for Jointly Improving Aspect-oriented Opinion Words Extraction and Sentiment Classification

• URL: http://arxiv.org/abs/2011.00474v1
• Date: Sun, 1 Nov 2020 11:00:19 GMT
• Title: Opinion Transmission Network for Jointly Improving Aspect-oriented Opinion Words Extraction and Sentiment Classification
• Authors: Chengcan Ying and Zhen Wu and Xinyu Dai and Shujian Huang and Jiajun Chen
• Abstract summary: Aspect-level sentiment classification (ALSC) and aspect oriented opinion words extraction (AOWE) are two highly relevant aspect-based sentiment analysis subtasks. We propose a novel joint model, Opinion Transmission Network (OTN), to exploit the potential bridge between ALSC and AOWE.
• Score: 56.893393134328996
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Aspect-level sentiment classification (ALSC) and aspect oriented opinion words extraction (AOWE) are two highly relevant aspect-based sentiment analysis (ABSA) subtasks. They respectively aim to detect the sentiment polarity and extract the corresponding opinion words toward a given aspect in a sentence. Previous works separate them and focus on one of them by training neural models on small-scale labeled data, while neglecting the connections between them. In this paper, we propose a novel joint model, Opinion Transmission Network (OTN), to exploit the potential bridge between ALSC and AOWE to achieve the goal of facilitating them simultaneously. Specifically, we design two tailor-made opinion transmission mechanisms to control opinion clues flow bidirectionally, respectively from ALSC to AOWE and AOWE to ALSC. Experiment results on two benchmark datasets show that our joint model outperforms strong baselines on the two tasks. Further analysis also validates the effectiveness of opinion transmission mechanisms.

Related papers

• Co-guiding for Multi-intent Spoken Language Understanding [53.30511968323911]
  We propose a novel model termed Co-guiding Net, which implements a two-stage framework achieving the mutual guidances between the two tasks. For the first stage, we propose single-task supervised contrastive learning, and for the second stage, we propose co-guiding supervised contrastive learning. Experiment results on multi-intent SLU show that our model outperforms existing models by a large margin.
  arXiv  Detail & Related papers  (2023-11-22T08:06:22Z)
• Aspect-oriented Opinion Alignment Network for Aspect-Based Sentiment Classification [14.212306015270208]
  We propose a novel Aspect-oriented Opinion Alignment Network (AOAN) to capture the contextual association between opinion words and the corresponding aspect. In addition, we design a multi-perspective attention mechanism that align relevant opinion information with respect to the given aspect. Our model achieves state-of-the-art results on three benchmark datasets.
  arXiv  Detail & Related papers  (2023-08-22T13:55:36Z)
• A Bi-directional Multi-hop Inference Model for Joint Dialog Sentiment Classification and Act Recognition [25.426172735931463]
  The joint task of Dialog Sentiment Classification (DSC) and Act Recognition (DAR) aims to predict the sentiment label and act label for each utterance in a dialog simultaneously. We propose a Bi-directional Multi-hop Inference Model (BMIM) that iteratively extract and integrate rich sentiment and act clues in a bi-directional manner. BMIM outperforms state-of-the-art baselines by at least 2.6% on F1 score in DAR and 1.4% on F1 score in DSC.
  arXiv  Detail & Related papers  (2023-08-08T17:53:24Z)
• Contrastive variational information bottleneck for aspect-based sentiment analysis [36.83876224466177]
  We propose to reduce spurious correlations for aspect-based sentiment analysis (ABSA) via a novel Contrastive Variational Information Bottleneck framework (called CVIB) The proposed CVIB framework is composed of an original network and a self-pruned network, and these two networks are optimized simultaneously via contrastive learning. Our approach achieves better performance than the strong competitors in terms of overall prediction performance, robustness, and generalization.
  arXiv  Detail & Related papers  (2023-03-06T02:52:37Z)
• Anticipating the Unseen Discrepancy for Vision and Language Navigation [63.399180481818405]
  Vision-Language Navigation requires the agent to follow natural language instructions to reach a specific target. The large discrepancy between seen and unseen environments makes it challenging for the agent to generalize well. We propose Unseen Discrepancy Anticipating Vision and Language Navigation (DAVIS) that learns to generalize to unseen environments via encouraging test-time visual consistency.
  arXiv  Detail & Related papers  (2022-09-10T19:04:40Z)
• A Joint Training Dual-MRC Framework for Aspect Based Sentiment Analysis [9.587513675287829]
  Aspect based sentiment analysis involves three fundamental subtasks: aspect term extraction, opinion term extraction, and aspect-level sentiment classification. Previous approaches fail to solve all subtasks in a unified end-to-end framework. We construct two machine reading comprehension problems and solve all subtasks by joint training two BERT-MRC models with parameters sharing.
  arXiv  Detail & Related papers  (2021-01-04T07:47:53Z)
• Introducing Syntactic Structures into Target Opinion Word Extraction with Deep Learning [89.64620296557177]
  We propose to incorporate the syntactic structures of the sentences into the deep learning models for targeted opinion word extraction. We also introduce a novel regularization technique to improve the performance of the deep learning models. The proposed model is extensively analyzed and achieves the state-of-the-art performance on four benchmark datasets.
  arXiv  Detail & Related papers  (2020-10-26T07:13:17Z)
• Attention Transfer Network for Aspect-level Sentiment Classification [30.704053194980528]
  Aspect-level sentiment classification (ASC) aims to detect the sentiment polarity of a given opinion target in a sentence. Data scarcity causes the attention mechanism sometimes to fail to focus on the corresponding sentiment words of the target. We propose a novel Attention Transfer Network (ATN) in this paper, which can successfully exploit attention knowledge from document-level sentiment classification datasets.
  arXiv  Detail & Related papers  (2020-10-23T04:26:33Z)
• 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)
• Latent Opinions Transfer Network for Target-Oriented Opinion Words Extraction [63.70885228396077]
  We propose a novel model to transfer opinions knowledge from resource-rich review sentiment classification datasets to low-resource task TOWE. Our model achieves better performance compared to other state-of-the-art methods and significantly outperforms the base model without transferring opinions knowledge.
  arXiv  Detail & Related papers  (2020-01-07T11:50:54Z)

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.