Salīdzināt metodes

	NMF tēmu modelēšana	BERT Embeddings
Nozare	Teksta ieguve	Teksta ieguve
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	1999	2019
Autors	Lee & Seung	Devlin, Chang, Lee & Toutanova (Google AI)
Tips	Matrix-factorization topic model	Contextual transformer text-representation method
Pirmavots	Lee, D.D. & Seung, H.S. (1999). Learning the Parts of Objects by Non-negative Matrix Factorization. Nature, 401, 788-791.	Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL-HLT, 4171-4186.
Citi nosaukumi	non-negative matrix factorization topic modeling, NMF topics, Konu Modelleme — NMF	contextual embeddings, transformer embeddings, BERT Tabanlı Metin Gömülmeleri
Saistītās	4	4
Kopsavilkums	NMF topic modeling uses Non-negative Matrix Factorization — the parts-based decomposition introduced by Lee and Seung (1999) — to extract document-topic distributions from a corpus. By factoring a document-term matrix into two non-negative matrices, it recovers a small set of topics and tends to produce more interpretable topics than LDA.	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.