विधियों की तुलना करें

	टेक्स्ट डिडुप्लीकेशन	विषय मॉडलिंग
क्षेत्र	पाठ खनन	गहन अधिगम
परिवार	Process / pipeline	Machine learning
उद्भव वर्ष	1997	1999–2003
प्रवर्तक	Andrei Z. Broder (MinHash / Resemblance theory, 1997)	Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003)
प्रकार	Text preprocessing / corpus quality pipeline	Unsupervised generative probabilistic model
मौलिक स्रोत	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.
उपनाम	near-duplicate detection, document deduplication, corpus deduplication, Metin Tekilleştirme (Near-Duplicate Detection)	Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling
संबंधित	5	5
सारांश	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.