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| Dokumenttien klusterointi× | Avainsanojen poiminta× | Semanttinen samankaltaisuus – Merkityksen mittaaminen tekstien välillä× | |
|---|---|---|---|
| Tieteenala | Tekstinlouhinta | Tekstinlouhinta | Tekstinlouhinta |
| Menetelmäperhe | Process / pipeline | Process / pipeline | Process / pipeline |
| Syntyvuosi≠ | — | — | 2019 |
| Kehittäjä≠ | — | — | Nils Reimers & Iryna Gurevych (Sentence-BERT) |
| Tyyppi≠ | Unsupervised text-mining task | NLP text-mining task | NLP text-comparison task |
| Alkuperäislähde≠ | Aggarwal, C. C. & Zhai, C. (2012). Mining Text Data. Springer. ISBN: 9781461432227 | Mihalcea, R. & Tarau, P. (2004). TextRank: Bringing Order into Texts. EMNLP, 404-411. link ↗ | Reimers, N. & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. EMNLP. link ↗ |
| Rinnakkaisnimet | text clustering, unsupervised text grouping, Belge Kümeleme (Document Clustering) | keyphrase extraction, key term extraction, Anahtar Kelime Çıkarma (Keyword Extraction) | semantic textual similarity, text similarity, Anlamsal Benzerlik Analizi |
| Liittyvät | 4 | 4 | 4 |
| Tiivistelmä≠ | Document clustering is an unsupervised text-mining task that groups documents with similar content together without using any labels. It is used to organise large collections and for exploratory analysis, drawing on the body of text-mining techniques consolidated by Aggarwal and Zhai (2012) and compared empirically by Steinbach, Karypis and Kumar (2000). | Keyword extraction is a natural-language-processing task that automatically identifies the words or phrases that best represent the content of a document. It turns a body of free text into a compact, ranked list of key terms, drawing on statistical, graph-based methods such as TextRank (Mihalcea & Tarau, 2004), or embedding-based methods such as KeyBERT (Grootendorst, 2020). | Semantic similarity analysis measures how close in meaning two texts are, rather than how many words they share on the surface. Building on the Sentence-BERT work of Reimers and Gurevych (2019), it represents each text as a vector and compares those vectors so that paraphrases score high even when their wording differs. |
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