Keyword Extraction — Automatic Key Term Identification
Also known as: keyphrase extraction, key term extraction, Anahtar Kelime Çıkarma (Keyword Extraction)
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).
Key highlights
- Turns long documents into a compact, ranked list of representative terms automatically.
- Flexible across approaches: statistical frequency, graph-based TextRank, or embedding-based KeyBERT under one pipeline.
- Low barrier to use, supporting both quick description and deeper exploration of a corpus.
Intuition
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How it works
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When to use it
Keyword extraction fits when you have text data and want to summarise or index its content with representative terms, for either description or exploration. The text should be preprocessed before extraction. Statistical extraction is reliable only on a sizeable corpus; on a small corpus (under roughly 100 documents) a simple frequency analysis is the safer choice, and with no text data at all the task cannot run — qualitative content analysis is the alternative.
Strengths & limitations
- Turns long documents into a compact, ranked list of representative terms automatically.
- Flexible across approaches: statistical frequency, graph-based TextRank, or embedding-based KeyBERT under one pipeline.
- Low barrier to use, supporting both quick description and deeper exploration of a corpus.
- Statistical extraction is unreliable on a small corpus; below roughly 100 documents it is not dependable.
- Requires preprocessed text; skipping cleaning degrades the candidate terms.
- Cannot run at all when there is no text data to work with.
Common pitfalls
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Applications
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Frequently asked
Statistical, TextRank, or KeyBERT — which approach should I use?
A statistical approach weights term frequency and is the simplest. TextRank builds a co-occurrence graph and ranks terms by their connections, capturing structure beyond raw counts. KeyBERT uses contextual embeddings to score candidates by semantic similarity to the document, which tends to surface more meaningful phrases at higher computational cost.
How much text do I need?
Statistical keyword extraction is only reliable on a sizeable corpus. On a small corpus — roughly under 100 documents — a simple frequency analysis is the safer choice. With no text data at all, the task cannot run.
Do I need to preprocess the text first?
Yes. Preprocessing is assumed: the text must be cleaned and standardised before extraction, or noisy tokens will distort the candidate terms. A domain-specific dictionary, where available, further improves quality.
Does it return single words or phrases?
Either. The candidate pool can include single words and short phrases (n-grams), so the output is a ranked list of keywords or keyphrases depending on the configuration.
Sources
- 1.Mihalcea, R. & Tarau, P. (2004). TextRank: Bringing Order into Texts. EMNLP, 404-411.
- 2.Grootendorst, M. (2020). KeyBERT. Zenodo.
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Cite this page
ScholarGate. (2026, June 1). Keyword Extraction. ScholarGate. https://scholargate.app/text-mining/keyword-extraction