Comparar métodos

	Deduplicación de texto	Modelado de Temas
Campo	Minería de texto	Aprendizaje profundo
Familia	Process / pipeline	Machine learning
Año de origen	1997	1999–2003
Autor original	Andrei Z. Broder (MinHash / Resemblance theory, 1997)	Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003)
Tipo	Text preprocessing / corpus quality pipeline	Unsupervised generative probabilistic model
Fuente seminal	Broder, A.Z. (1997). On the Resemblance and Containment of Documents. Compression and Complexity of SEQUENCES.	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.
Alias	near-duplicate detection, document deduplication, corpus deduplication, Metin Tekilleştirme (Near-Duplicate Detection)	Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling
Relacionados	5	5
Resumen	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.