Key Information Retrieval to Classify the Unstructured Data Content of Preferential Trade Agreements
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913205325922304 |
|---|---|
| author | Zhao, Jiahui Meng, Ziyi Gordeev, Stepan Pan, Zijie Song, Dongjin Steinbach, Sandro Ding, Caiwen |
| author_facet | Zhao, Jiahui Meng, Ziyi Gordeev, Stepan Pan, Zijie Song, Dongjin Steinbach, Sandro Ding, Caiwen |
| contents | With the rapid proliferation of textual data, predicting long texts has emerged as a significant challenge in the domain of natural language processing. Traditional text prediction methods encounter substantial difficulties when grappling with long texts, primarily due to the presence of redundant and irrelevant information, which impedes the model's capacity to capture pivotal insights from the text. To address this issue, we introduce a novel approach to long-text classification and prediction. Initially, we employ embedding techniques to condense the long texts, aiming to diminish the redundancy therein. Subsequently,the Bidirectional Encoder Representations from Transformers (BERT) embedding method is utilized for text classification training. Experimental outcomes indicate that our method realizes considerable performance enhancements in classifying long texts of Preferential Trade Agreements. Furthermore, the condensation of text through embedding methods not only augments prediction accuracy but also substantially reduces computational complexity. Overall, this paper presents a strategy for long-text prediction, offering a valuable reference for researchers and engineers in the natural language processing sphere. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_12520 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Key Information Retrieval to Classify the Unstructured Data Content of Preferential Trade Agreements Zhao, Jiahui Meng, Ziyi Gordeev, Stepan Pan, Zijie Song, Dongjin Steinbach, Sandro Ding, Caiwen Computation and Language Information Retrieval Machine Learning With the rapid proliferation of textual data, predicting long texts has emerged as a significant challenge in the domain of natural language processing. Traditional text prediction methods encounter substantial difficulties when grappling with long texts, primarily due to the presence of redundant and irrelevant information, which impedes the model's capacity to capture pivotal insights from the text. To address this issue, we introduce a novel approach to long-text classification and prediction. Initially, we employ embedding techniques to condense the long texts, aiming to diminish the redundancy therein. Subsequently,the Bidirectional Encoder Representations from Transformers (BERT) embedding method is utilized for text classification training. Experimental outcomes indicate that our method realizes considerable performance enhancements in classifying long texts of Preferential Trade Agreements. Furthermore, the condensation of text through embedding methods not only augments prediction accuracy but also substantially reduces computational complexity. Overall, this paper presents a strategy for long-text prediction, offering a valuable reference for researchers and engineers in the natural language processing sphere. |
| title | Key Information Retrieval to Classify the Unstructured Data Content of Preferential Trade Agreements |
| topic | Computation and Language Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2401.12520 |