Attentive Gated Lexicon Reader with Contrastive Contextual Co-Attention for Sentiment Classification (D18-1)
Copied to clipboard
| Challenge: | Existing sentiment lexicons do not handle word sense and the concept of semantic compositionality is non-existent in simple lexiconic approaches. |
| Approach: | They propose a lexicon-driven contextual attention mechanism and a contrastive co-attention mechanism that models contrasting polarities between all positive and negative words in a sentence. |
| Outcome: | The proposed model outperforms many other neural baselines on sentiment classification tasks on multiple benchmark datasets. |
Similar Papers
Attention and Lexicon Regularized LSTM for Aspect-based Sentiment Analysis (P19-2)
Copied to clipboard
| Challenge: | End-to-end deep learning systems lack flexibility as one cannot adjust the network to fix an obvious problem. |
| Approach: | They propose a way to leverage lexicon information to make the model more flexible . they also explore the effect of regularizing attention vectors to allow the network to have a broader "focus" |
| Outcome: | The proposed approach leverages lexicon information to make it more flexible and robust. |
A Lexicon-Based Supervised Attention Model for Neural Sentiment Analysis (C18-1)
Copied to clipboard
| Challenge: | Existing attention models do not take full advantage of sentiment lexicons, which provide rich sentiment information and play a critical role in sentiment analysis. |
| Approach: | They propose a lexicon-based supervised attention model which allows a neural network to focus on the sentiment content, thus generating sentiment-informative representations. |
| Outcome: | The proposed model outperforms existing models on three large-scale sentiment classification datasets. |
Classifier-based Polarity Propagation in a WordNet (L18-1)
Copied to clipboard
| Challenge: | a wordnet-based sentiment lexicon can be built to express sentiment polarity in a way shared across domains. |
| Approach: | They propose a method to build a sense-level sentiment lexicon on the basis of a wordnet . they use a rich set of wordnet-based features to recognize and assign sentiment polarity values . |
| Outcome: | The proposed method allows for the construction of a more reliable sentiment lexicon . the proposed method is partially automated, but it's performance drops in cross-domain applications . |
Learning Implicit Sentiment in Aspect-based Sentiment Analysis with Supervised Contrastive Pre-Training (2021.emnlp-main)
Copied to clipboard
| Challenge: | Recent studies have focused on identifying the sentiment polarity of aspects in product reviews. |
| Approach: | They propose to use supervised Contrastive Pre-Training to learn implicit sentiment . they propose to train large-scale sentiment-annotated corpora from in-domain language resources . |
| Outcome: | The proposed model achieves state-of-the-art performance on SemEval2014 benchmarks and comprehensively validates its effectiveness on learning implicit sentiment. |
Effective Attention Modeling for Aspect-Level Sentiment Classification (C18-1)
Copied to clipboard
| Challenge: | Aspect-level sentiment classification aims to determine sentiment polarity of review sentence towards opinion target . main challenge is to separate different opinion contexts for different targets . |
| Approach: | They propose a method that captures the semantic meaning of the opinion target and a model that incorporates syntactic information into the attention mechanism. |
| Outcome: | The proposed method captures the semantic meaning of the opinion target and incorporates syntactic information into the attention mechanism. |
Sentiment-Aware Word and Sentence Level Pre-training for Sentiment Analysis (2022.emnlp-main)
Copied to clipboard
Shuai Fan, Chen Lin, Haonan Li, Zhenghao Lin, Jinsong Su, Hang Zhang, Yeyun Gong, JIan Guo, Nan Duan
| Challenge: | Existing pre-trained language representation models (PLMs) capture sentiment information from word-level while under-considering sentence-level information. |
| Approach: | They propose a Sentiment-aware pre-trained language model with combined Word-level and Sentence-level Pre-training tasks that enhance the PLM’s knowledge about sentiment words. |
| Outcome: | The proposed model achieves state-of-the-art on various sentence-level and aspect-level sentiment classification benchmarks. |
SoftMCL: Soft Momentum Contrastive Learning for Fine-grained Sentiment-aware Pre-training (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods for pre-training language models capture general language understanding but fail to distinguish affective impact of a particular context to a specific word. |
| Approach: | They propose a soft momentum contrastive learning method for fine-grained sentiment-aware pre-training that uses valence ratings as soft-label supervision instead of hard labels. |
| Outcome: | The proposed method improves on four sentiment-related tasks and the results are published online. |
CONTRASTE: Supervised Contrastive Pre-training With Aspect-based Prompts For Aspect Sentiment Triplet Extraction (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies on Aspect Sentiment Triplet Extraction focus on developing more efficient techniques for the task, but our proposed approach can improve the downstream performance of multiple ABSA tasks simultaneously. |
| Approach: | They propose a novel approach that uses contrastive learning to enhance the ASTE performance by masked sentiments. |
| Outcome: | The proposed approach improves the performance of multiple ABSA tasks simultaneously. |
Progressive Self-Supervised Attention Learning for Aspect-Level Sentiment Analysis (P19-1)
Copied to clipboard
| Challenge: | Experimental results show that our proposed approach yields better attention mechanisms . dominant ASC models are mostly discriminative classifiers based on manual feature engineering . |
| Approach: | They propose a self-supervised approach to aspect-level sentiment classification that mines useful attention supervision information from a training corpus to refine attention mechanisms. |
| Outcome: | The proposed approach yields better attention mechanisms on multiple datasets. |
Domain-Specific Sentiment Lexicons Induced from Labeled Documents (2020.coling-main)
Copied to clipboard
| Challenge: | Existing sentiment lexicons reflect abstract notion of polarity and do not do justice to substantial differences of word polarities between domains. |
| Approach: | They propose to use domain-specific sentiment lexicons to induce initial word intensity scores and train new deep models based on word vector representations to overcome the scarcity of the seed data. |
| Outcome: | The proposed models show that they perform well on review classification and cross-lingual word sentiment prediction. |