Summary of Generating Effective Ensembles For Sentiment Analysis, by Itay Etelis et al.
Generating Effective Ensembles for Sentiment Analysis
by Itay Etelis, Avi Rosenfeld, Abraham Itzhak Weinberg, David Sarne
First submitted to arxiv on: 26 Feb 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 Transformers have transformed Natural Language Processing (NLP), achieving exceptional results in tasks like Sentiment Analysis (SA). State-of-the-art SA approaches rely on transformers alone, achieving high accuracy levels on benchmark datasets. However, this paper shows that further improving SA accuracy requires combining transformers with traditional NLP models, despite the latter’s inferiority. A new Hierarchical Ensemble Construction (HEC) algorithm is proposed to construct these mixed model ensembles, which significantly outperform traditional ensembles across eight canonical SA datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Transformers have made big changes in language processing. Right now, the best approaches for understanding sentiment use only transformers and get great results on tests. But this paper shows that we can do even better by mixing in some older language processing models too, even though they’re not as good. We need to build these mixed model teams in a special way, using something called Hierarchical Ensemble Construction (HEC). When we did this, our team got much better results than usual on eight big tests for understanding sentiment. |
Keywords
» Artificial intelligence » Natural language processing » Nlp
