Summary of Instruct-deberta: a Hybrid Approach For Aspect-based Sentiment Analysis on Textual Reviews, by Dineth Jayakody et al.
Instruct-DeBERTa: A Hybrid Approach for Aspect-based Sentiment Analysis on Textual Reviews
by Dineth Jayakody, A V A Malkith, Koshila Isuranda, Vishal Thenuwara, Nisansa de Silva, Sachintha Rajith Ponnamperuma, G G N Sandamali, K L K Sudheera
First submitted to arxiv on: 23 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Aspect-based Sentiment Analysis (ABSA) is a critical task in Natural Language Processing (NLP) that focuses on extracting sentiments related to specific aspects within a text. This paper presents a comprehensive review of the evolution of ABSA methodologies, from lexicon-based approaches to machine learning and deep learning techniques. The authors emphasize recent advancements in Transformer-based models, particularly Bidirectional Encoder Representations from Transformers (BERT) and its variants, which have set new benchmarks in ABSA tasks. They also explore finetuning Llama and Mistral models, building hybrid models using the SetFit framework, and developing their own model by exploiting the strengths of state-of-the-art Transformer-based models for aspect term extraction (ATE) and aspect sentiment classification (ASC). The authors utilize datasets from different domains to evaluate their model’s performance. Their experiments indicate that the proposed hybrid model significantly improves the accuracy and reliability of sentiment analysis across all experimented domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Aspect-based Sentiment Analysis is a way to understand what people think about specific parts of something, like a product or service. This paper looks at how people have tried to do this in the past, and it shows that some new methods are better than old ones. The authors use special computer models called Transformers to help them understand what people are saying about different things. They test their model on lots of texts from different places and find that it works really well. |
Keywords
» Artificial intelligence » Bert » Classification » Deep learning » Encoder » Llama » Machine learning » Natural language processing » Nlp » Transformer
