Summary of Feature Engineering Vs. Deep Learning For Paper Section Identification: Toward Applications in Chinese Medical Literature, by Sijia Zhou et al.
Feature engineering vs. deep learning for paper section identification: Toward applications in Chinese medical literature
by Sijia Zhou, Xin Li
First submitted to arxiv on: 15 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 A novel approach to identifying paper sections is proposed, leveraging classic machine learning algorithms and deep learning models on Chinese medical literature analysis. Building upon previous studies on English literature section identification, the authors experiment with effective features for Conditional Random Fields (CRFs) and find that sentence interdependency plays a crucial role. The study demonstrates that CRFs outperform generic deep learning models and traditional machine learning methods in identifying paper sections. A novel deep learning model, the Structural Bidirectional Long Short-Term Memory (SLSTM) model, is designed to model word and sentence interdependency together with contextual information. Experiments on a human-curated asthma literature dataset show that the approach achieves close to 90% precision and recall, outperforming traditional methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Paper sections can be identified using machine learning algorithms and deep learning models. Researchers studied Chinese medical literature analysis and found that Conditional Random Fields (CRFs) are effective for identifying paper sections. CRFs consider sentence interdependency and performed better than other approaches. A new model called SLSTM was designed to analyze text and achieved high precision and recall. |
Keywords
» Artificial intelligence » Deep learning » Machine learning » Precision » Recall
