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| Kulcsszavak kinyerése× | Szemantikai hasonlóság× | |
|---|---|---|
| Tudományterület | Szövegbányászat | Szövegbányászat |
| Módszercsalád | Process / pipeline | Process / pipeline |
| Keletkezés éve≠ | — | 2019 |
| Megalkotó≠ | — | Nils Reimers & Iryna Gurevych (Sentence-BERT) |
| Típus≠ | NLP text-mining task | NLP text-comparison task |
| Alapmű≠ | 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 ↗ |
| Alternatív nevek | keyphrase extraction, key term extraction, Anahtar Kelime Çıkarma (Keyword Extraction) | semantic textual similarity, text similarity, Anlamsal Benzerlik Analizi |
| Kapcsolódó | 4 | 4 |
| Összefoglaló≠ | 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. |
| ScholarGateAdatkészlet ↗ |
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