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	Osadzenia BERT	Klasyfikacja Tekstu	Modelowanie tematów
Dziedzina	Eksploracja tekstu	Eksploracja tekstu	Uczenie głębokie
Rodzina	Process / pipeline	Process / pipeline	Machine learning
Rok powstania	2019	—	1999–2003
Twórca	Devlin, Chang, Lee & Toutanova (Google AI)	—	Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003)
Typ	Contextual transformer text-representation method	Supervised NLP classification task	Unsupervised generative probabilistic model
Źródło pierwotne	Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL-HLT, 4171-4186.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.
Inne nazwy	contextual embeddings, transformer embeddings, BERT Tabanlı Metin Gömülmeleri	text categorization, document classification, topic classification, metin sınıflandırma	Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling
Pokrewne	4	4	5
Podsumowanie	BERT-based text embeddings, introduced by Devlin and colleagues at Google AI in 2019, turn text into context-sensitive dense vectors using a bidirectional Transformer encoder. Because the meaning of a word shifts with its context, BERT produces richer representations than static methods such as Word2Vec or topic models like LDA.	Text classification, also called text categorization, is a supervised natural-language-processing task that automatically assigns documents to predefined categories. Building on the support-vector-machine approach to text categorization established by Joachims (1998) and consolidated in the text-mining literature by Aggarwal and Zhai (2012), it powers tasks such as spam detection and topic classification by learning from labelled examples.	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.