Keyword and Keyphrase Extraction Implementation

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Keyword and Keyphrase Extraction Implementation
Simple
~2-3 business days
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Keyword/Keyphrase Extraction Implementation

Keywords are short n-grams reflecting main document topics. Applications are broad: indexing, search, content tagging, automatic annotations.

Extraction Methods

Statistical methods—fast, no training:

  • YAKE (Yet Another Keyword Extractor): accounts for word position, collocations, frequency. Works without corpus, 5ms/document
  • RAKE (Rapid Automatic Keyword Extraction): split by stopwords, scoring via co-occurrence
  • TF-IDF: top words by TF-IDF weight—effective with corpus for IDF

Graph-based methods:

  • TextRank (PageRank analog for words): builds co-occurrence graph, ranks nodes. Implementation: gensim, pytextrank

Semantic methods (best quality):

  • KeyBERT: document and candidate embeddings compared via cosine similarity
from keybert import KeyBERT
kw_model = KeyBERT(model="cointegrated/rubert-tiny2")
keywords = kw_model.extract_keywords(text, keyphrase_ngram_range=(1, 3), top_n=10)

For Russian Language

Statistical methods perform poorly without lemmatization. Correct pipeline: lemmatization (pymorphy3) → YAKE/KeyBERT. KeyBERT with rubert-tiny2 yields good quality at ~50ms/document latency.

Production Application

Typical task: tag 10K articles daily. Optimal stack: YAKE for speed + KeyBERT for top documents. Results normalized (lemmatization, lowercase, deduplication) and saved to search index (Elasticsearch with keywords field).