Summary of Cerm: Context-aware Literature-based Discovery Via Sentiment Analysis, by Julio Christian Young and Uchenna Akujuobi
CERM: Context-aware Literature-based Discovery via Sentiment Analysis
by Julio Christian Young, Uchenna Akujuobi
First submitted to arxiv on: 27 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 Relationship Sentiment Analysis (ERSA) task aims to capture the sentiment of biomedical texts based on entity pairs, extending the Aspect-Based Sentiment Analysis (ABSA) task. ERS A involves analyzing the relationship between biomedical and food concepts, which is a significant challenge as sentence sentiment may not align with entity relationship sentiment. The study proposes CERM, a semi-supervised architecture that combines different word embeddings to enhance ERSA encoding. Experimental results demonstrate the model’s efficiency across diverse learning scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new task called Entity Relationship Sentiment Analysis (ERSA) that helps us understand how food relates to our health. Right now, most systems just look at the nutritional value of ingredients or use simple models trained on labeled data. But what if we could develop better models that capture the connection between different foods and medical concepts? This would be super helpful for researchers studying the relationship between food and health. The problem is that labeling this kind of data can be very expensive, so we need to find ways to make our models work well with both labeled and unlabeled data. |
Keywords
* Artificial intelligence * Semi supervised
