HAlf-MAsked Model for Named Entity Sentiment analysis
- URL: http://arxiv.org/abs/2308.15793v1
- Date: Wed, 30 Aug 2023 06:53:24 GMT
- Title: HAlf-MAsked Model for Named Entity Sentiment analysis
- Authors: Anton Kabaev, Pavel Podberezko, Andrey Kaznacheev, Sabina Abdullayeva
- Abstract summary: We study different transformers-based solutions NESA in RuSentNE-23 evaluation.
We present several approaches to overcome this problem, among which there is a novel technique of additional pass over given data with masked entity.
Our proposed model achieves the best result on RuSentNE-23 evaluation data and demonstrates improved consistency in entity-level sentiment analysis.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Named Entity Sentiment analysis (NESA) is one of the most actively developing
application domains in Natural Language Processing (NLP). Social media NESA is
a significant field of opinion analysis since detecting and tracking sentiment
trends in the news flow is crucial for building various analytical systems and
monitoring the media image of specific people or companies. In this paper, we
study different transformers-based solutions NESA in RuSentNE-23 evaluation.
Despite the effectiveness of the BERT-like models, they can still struggle with
certain challenges, such as overfitting, which appeared to be the main obstacle
in achieving high accuracy on the RuSentNE-23 data. We present several
approaches to overcome this problem, among which there is a novel technique of
additional pass over given data with masked entity before making the final
prediction so that we can combine logits from the model when it knows the exact
entity it predicts sentiment for and when it does not. Utilizing this
technique, we ensemble multiple BERT- like models trained on different subsets
of data to improve overall performance. Our proposed model achieves the best
result on RuSentNE-23 evaluation data and demonstrates improved consistency in
entity-level sentiment analysis.
Related papers
- Instruct-DeBERTa: A Hybrid Approach for Aspect-based Sentiment Analysis on Textual Reviews [2.0143010051030417]
Aspect-based Sentiment Analysis (ABSA) is a critical task in Natural Language Processing (NLP)
Traditional sentiment analysis methods, while useful for determining overall sentiment, often miss the implicit opinions about particular product or service features.
This paper presents a comprehensive review of the evolution of ABSA methodologies, from lexicon-based approaches to machine learning.
arXiv Detail & Related papers (2024-08-23T16:31:07Z) - A Comparative Study of Transfer Learning for Emotion Recognition using CNN and Modified VGG16 Models [0.0]
We investigate the performance of CNN and Modified VGG16 models for emotion recognition tasks across two datasets: FER2013 and AffectNet.
Our findings reveal that both models achieve reasonable performance on the FER2013 dataset, with the Modified VGG16 model demonstrating slightly increased accuracy.
When evaluated on the Affect-Net dataset, performance declines for both models, with the Modified VGG16 model continuing to outperform the CNN.
arXiv Detail & Related papers (2024-07-19T17:41:46Z) - Self-Training with Pseudo-Label Scorer for Aspect Sentiment Quad Prediction [54.23208041792073]
Aspect Sentiment Quad Prediction (ASQP) aims to predict all quads (aspect term, aspect category, opinion term, sentiment polarity) for a given review.
A key challenge in the ASQP task is the scarcity of labeled data, which limits the performance of existing methods.
We propose a self-training framework with a pseudo-label scorer, wherein a scorer assesses the match between reviews and their pseudo-labels.
arXiv Detail & Related papers (2024-06-26T05:30:21Z) - Innovative Horizons in Aerial Imagery: LSKNet Meets DiffusionDet for
Advanced Object Detection [55.2480439325792]
We present an in-depth evaluation of an object detection model that integrates the LSKNet backbone with the DiffusionDet head.
The proposed model achieves a mean average precision (MAP) of approximately 45.7%, which is a significant improvement.
This advancement underscores the effectiveness of the proposed modifications and sets a new benchmark in aerial image analysis.
arXiv Detail & Related papers (2023-11-21T19:49:13Z) - QualEval: Qualitative Evaluation for Model Improvement [82.73561470966658]
We propose QualEval, which augments quantitative scalar metrics with automated qualitative evaluation as a vehicle for model improvement.
QualEval uses a powerful LLM reasoner and our novel flexible linear programming solver to generate human-readable insights.
We demonstrate that leveraging its insights, for example, improves the absolute performance of the Llama 2 model by up to 15% points relative.
arXiv Detail & Related papers (2023-11-06T00:21:44Z) - Towards Robust Aspect-based Sentiment Analysis through
Non-counterfactual Augmentations [40.71705332298682]
We present an alternative approach that relies on non-counterfactual data augmentation.
Our approach further establishes a new state-of-the-art on the ABSA robustness benchmark and transfers well across domains.
arXiv Detail & Related papers (2023-06-24T13:57:32Z) - On the Robustness of Aspect-based Sentiment Analysis: Rethinking Model,
Data, and Training [109.9218185711916]
Aspect-based sentiment analysis (ABSA) aims at automatically inferring the specific sentiment polarities toward certain aspects of products or services behind social media texts or reviews.
We propose to enhance the ABSA robustness by systematically rethinking the bottlenecks from all possible angles, including model, data, and training.
arXiv Detail & Related papers (2023-04-19T11:07:43Z) - Exploring the Efficacy of Automatically Generated Counterfactuals for
Sentiment Analysis [17.811597734603144]
We propose an approach to automatically generating counterfactual data for data augmentation and explanation.
A comprehensive evaluation on several different datasets and using a variety of state-of-the-art benchmarks demonstrate how our approach can achieve significant improvements in model performance.
arXiv Detail & Related papers (2021-06-29T10:27:01Z) - Comparing Test Sets with Item Response Theory [53.755064720563]
We evaluate 29 datasets using predictions from 18 pretrained Transformer models on individual test examples.
We find that Quoref, HellaSwag, and MC-TACO are best suited for distinguishing among state-of-the-art models.
We also observe span selection task format, which is used for QA datasets like QAMR or SQuAD2.0, is effective in differentiating between strong and weak models.
arXiv Detail & Related papers (2021-06-01T22:33:53Z) - Interpretable Multi-dataset Evaluation for Named Entity Recognition [110.64368106131062]
We present a general methodology for interpretable evaluation for the named entity recognition (NER) task.
The proposed evaluation method enables us to interpret the differences in models and datasets, as well as the interplay between them.
By making our analysis tool available, we make it easy for future researchers to run similar analyses and drive progress in this area.
arXiv Detail & Related papers (2020-11-13T10:53:27Z)
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.