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| Tekstin deduplikointi× | BERT-upotukset – kontekstisidonnaiset tekstiesitykset× | |
|---|---|---|
| Tieteenala | Tekstinlouhinta | Tekstinlouhinta |
| Menetelmäperhe | Process / pipeline | Process / pipeline |
| Syntyvuosi≠ | 1997 | 2019 |
| Kehittäjä≠ | Andrei Z. Broder (MinHash / Resemblance theory, 1997) | Devlin, Chang, Lee & Toutanova (Google AI) |
| Tyyppi≠ | Text preprocessing / corpus quality pipeline | Contextual transformer text-representation method |
| Alkuperäislähde≠ | Broder, A.Z. (1997). On the Resemblance and Containment of Documents. Compression and Complexity of SEQUENCES. link ↗ | Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL-HLT, 4171-4186. DOI ↗ |
| Rinnakkaisnimet≠ | near-duplicate detection, document deduplication, corpus deduplication, Metin Tekilleştirme (Near-Duplicate Detection) | contextual embeddings, transformer embeddings, BERT Tabanlı Metin Gömülmeleri |
| Liittyvät≠ | 5 | 4 |
| Tiivistelmä≠ | 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. | 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. |
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