方法对比
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| 文本去重× | 主题建模× | |
|---|---|---|
| 领域≠ | 文本挖掘 | 深度学习 |
| 方法族≠ | Process / pipeline | Machine learning |
| 起源年份≠ | 1997 | 1999–2003 |
| 提出者≠ | Andrei Z. Broder (MinHash / Resemblance theory, 1997) | Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003) |
| 类型≠ | Text preprocessing / corpus quality pipeline | Unsupervised generative probabilistic model |
| 开创性文献≠ | Broder, A.Z. (1997). On the Resemblance and Containment of Documents. Compression and Complexity of SEQUENCES. link ↗ | Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022. link ↗ |
| 别名 | near-duplicate detection, document deduplication, corpus deduplication, Metin Tekilleştirme (Near-Duplicate Detection) | Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling |
| 相关 | 5 | 5 |
| 摘要≠ | Text deduplication is a corpus-quality pipeline that identifies and removes exact and near-duplicate documents from large text collections. Grounded in Andrei Broder's 1997 resemblance theory, it is widely used to improve dataset quality for machine learning model training, search engine indexing, and any downstream NLP task that assumes a non-redundant corpus. | Topic Modeling is a family of unsupervised probabilistic techniques for discovering latent thematic structure in large text collections. By learning which words tend to co-occur, models such as Latent Dirichlet Allocation (LDA) automatically surface coherent topics — each represented as a distribution over vocabulary — without requiring labelled data. |
| ScholarGate数据集 ↗ |
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