Cleaner Pretraining Corpus Curation with Neural Web Scraping
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910488388960256 |
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| author | Xu, Zhipeng Liu, Zhenghao Yan, Yukun Liu, Zhiyuan Yu, Ge Xiong, Chenyan |
| author_facet | Xu, Zhipeng Liu, Zhenghao Yan, Yukun Liu, Zhiyuan Yu, Ge Xiong, Chenyan |
| contents | The web contains large-scale, diverse, and abundant information to satisfy the information-seeking needs of humans. Through meticulous data collection, preprocessing, and curation, webpages can be used as a fundamental data resource for language model pretraining. However, when confronted with the progressively revolutionized and intricate nature of webpages, rule-based/feature-based web scrapers are becoming increasingly inadequate. This paper presents a simple, fast, and effective Neural web Scraper (NeuScraper) to help extract primary and clean text contents from webpages. Experimental results show that NeuScraper surpasses the baseline scrapers by achieving more than a 20% improvement, demonstrating its potential in extracting higher-quality data to facilitate the language model pretraining. All of the code is available at https://github.com/OpenMatch/NeuScraper. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_14652 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Cleaner Pretraining Corpus Curation with Neural Web Scraping Xu, Zhipeng Liu, Zhenghao Yan, Yukun Liu, Zhiyuan Yu, Ge Xiong, Chenyan Computation and Language The web contains large-scale, diverse, and abundant information to satisfy the information-seeking needs of humans. Through meticulous data collection, preprocessing, and curation, webpages can be used as a fundamental data resource for language model pretraining. However, when confronted with the progressively revolutionized and intricate nature of webpages, rule-based/feature-based web scrapers are becoming increasingly inadequate. This paper presents a simple, fast, and effective Neural web Scraper (NeuScraper) to help extract primary and clean text contents from webpages. Experimental results show that NeuScraper surpasses the baseline scrapers by achieving more than a 20% improvement, demonstrating its potential in extracting higher-quality data to facilitate the language model pretraining. All of the code is available at https://github.com/OpenMatch/NeuScraper. |
| title | Cleaner Pretraining Corpus Curation with Neural Web Scraping |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2402.14652 |