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| Avainsanojen poiminta× | Semanttinen samankaltaisuus – Merkityksen mittaaminen tekstien välillä× | |
|---|---|---|
| Tieteenala | Tekstinlouhinta | Tekstinlouhinta |
| Menetelmäperhe | Process / pipeline | Process / pipeline |
| Syntyvuosi≠ | — | 2019 |
| Kehittäjä≠ | — | Nils Reimers & Iryna Gurevych (Sentence-BERT) |
| Tyyppi≠ | NLP text-mining task | NLP text-comparison task |
| Alkuperäislähde≠ | 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 | keyphrase extraction, key term extraction, Anahtar Kelime Çıkarma (Keyword Extraction) | semantic textual similarity, text similarity, Anlamsal Benzerlik Analizi |
| Liittyvät | 4 | 4 |
| Tiivistelmä≠ | 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. |
| ScholarGateAineisto ↗ |
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