Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Kopā sastopamības analīze× | Atslēgvārdu izvilkums× | |
|---|---|---|
| Nozare | Teksta ieguve | Teksta ieguve |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 1957 | — |
| Autors≠ | J.R. Firth (distributional principle) | — |
| Tips≠ | Text-mining / distributional-semantics technique | NLP text-mining task |
| Pirmavots≠ | Firth, J.R. (1957). A Synopsis of Linguistic Theory. Studies in Linguistic Analysis. Oxford: Blackwell. link ↗ | Mihalcea, R. & Tarau, P. (2004). TextRank: Bringing Order into Texts. EMNLP, 404-411. link ↗ |
| Citi nosaukumi | word co-occurrence, co-occurrence network, Kelime Eş-Oluşum Analizi | keyphrase extraction, key term extraction, Anahtar Kelime Çıkarma (Keyword Extraction) |
| Saistītās | 4 | 4 |
| Kopsavilkums≠ | Co-occurrence analysis is a text-mining technique that statistically counts the word pairs that appear together within a window or a sentence and uses their frequencies to reveal semantic maps and thematic structure. It rests on the distributional principle articulated by J.R. Firth in 1957 — that a word is characterised by the company it keeps. | 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). |
| ScholarGateDatu kopa ↗ |
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