Summary of Deep Content Understanding Toward Entity and Aspect Target Sentiment Analysis on Foundation Models, by Vorakit Vorakitphan et al.
Deep Content Understanding Toward Entity and Aspect Target Sentiment Analysis on Foundation Models
by Vorakit Vorakitphan, Milos Basic, Guilhaume Leroy Meline
First submitted to arxiv on: 4 Jul 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 The proposed Entity-Aspect Sentiment Triplet Extraction (EASTE) task is an extension of Target-Aspect-Sentiment Detection (TASD), introducing a finer level of complexity by separating aspect categories into predefined entities and aspects. The EASTE task aims to expose true sentiment of chained aspects to their entities, adding a layer of nuance to traditional Aspect-Based Sentiment Analysis (ABSA). The study explores the capabilities of language models based on transformer architectures, such as BERT, Flan-T5, Flan-Ul2, Llama2, Llama3, and Mixtral, employing various alignment techniques like zero/few-shot learning and Parameter Efficient Fine Tuning (PEFT) with Low-Rank Adaptation (LoRA). The model performances are evaluated on the SamEval-2016 benchmark dataset for fair comparison to existing works. This research not only seeks high performance on the EASTE task but also investigates the impact of model size, type, and adaptation techniques on task performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to analyze sentiment by considering the relationships between entities, aspects, and sentiments. It’s like trying to understand how someone really feels about their favorite food – not just whether they like it or dislike it, but what specific things they like or dislike about it. The researchers used special models called transformers to see if they could improve this kind of analysis. They tested different models and techniques to see which ones worked best, and then compared the results to other studies in the field. |
Keywords
» Artificial intelligence » Alignment » Bert » Few shot » Fine tuning » Lora » Low rank adaptation » Parameter efficient » T5 » Transformer
