Summary of Key Information Retrieval to Classify the Unstructured Data Content Of Preferential Trade Agreements, by Jiahui Zhao et al.
Key Information Retrieval to Classify the Unstructured Data Content of Preferential Trade Agreements
by Jiahui Zhao, Ziyi Meng, Stepan Gordeev, Zijie Pan, Dongjin Song, Sandro Steinbach, Caiwen Ding
First submitted to arxiv on: 23 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Information Retrieval (cs.IR); 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 approach addresses the challenge of predicting long texts by introducing a novel method for long-text classification and prediction. To overcome traditional methods’ limitations, the authors employ embedding techniques to condense the long texts, reducing redundancy and irrelevant information. The Bidirectional Encoder Representations from Transformers (BERT) embedding method is then used for text classification training. Experimental results show improved performance in classifying long texts of Preferential Trade Agreements, with significant reductions in computational complexity. This paper presents a valuable reference for researchers and engineers in natural language processing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper finds a way to predict very long pieces of text that are hard to understand because they have too much repeated information. They use special techniques to make the text shorter and easier to work with, then train a machine learning model on it. The results show that this method is better than others at predicting what kind of text it is. It also makes the process faster and more efficient. |
Keywords
* Artificial intelligence * Bert * Embedding * Encoder * Machine learning * Natural language processing * Text classification
