Extracting Keywords

Extract keywords from text using YAKE (Yet Another Keyword Extractor), an unsupervised statistical keyword extraction algorithm.

Installation

First time only: Install YAKE with optimized dependencies to avoid unnecessary downloads.

cd /home/claude
uv venv yake-venv --system-site-packages
uv pip install yake --python yake-venv/bin/python --no-deps
uv pip install jellyfish segtok regex --python yake-venv/bin/python

This reuses system packages (numpy, networkx) instead of downloading them (~0.08s vs ~5s).

Stopwords Configuration

Built-in YAKE stopwords (34 languages): Use lan="<code>" parameter

• See Parameters section below for all 34 supported language codes
• English (lan="en") is the default

Custom domain stopwords (bundled in assets/):

AI/ML: stopwords_ai.txt

• English stopwords + 783 AI/ML domain-specific terms (1357 total)
• Filters AI/ML methodology noise (model, training, network, algorithm, parameter)
• Filters ML boilerplate (dataset, baseline, benchmark, experiment, evaluation)
• Filters technical terms (transformer, embedding, attention, optimization, inference)
• Includes full lemmatization (train/trains/trained/training/trainer)
• Use for AI/ML papers, technical reports, machine learning literature
• Performance impact: +4-5% runtime vs English stopwords

Life Sciences: stopwords_ls.txt

• English stopwords + 719 life sciences domain-specific terms (1293 total)
• Filters research methodology noise (study, results, analysis, significant, observed)
• Filters academic boilerplate (paper, manuscript, publication, review, editing)
• Filters statistical terms (correlation, distribution, deviation, variance)
• Filters clinical terms (patient, treatment, diagnosis, symptom, therapy)
• Filters biology/medicine (cell, tissue, protein, gene, organism)
• Includes full lemmatization (analyze/analyzes/analyzed/analyzing/analysis)
• Use for biomedical papers, clinical studies, research articles, scientific literature
• Performance impact: +4-5% runtime vs English stopwords

Basic Usage

import yake

# Read text
with open('document.txt', 'r') as f:
    text = f.read()

# Extract with English stopwords (default)
kw_extractor = yake.KeywordExtractor(
    lan="en",           # Language code
    n=3,                # Max n-gram size (1-3 word phrases)
    dedupLim=0.9,       # Deduplication threshold (0-1)
    top=20              # Number of keywords to return
)

keywords = kw_extractor.extract_keywords(text)

# Display results (lower score = more important)
for kw, score in keywords:
    print(f"{score:.4f}  {kw}")

Domain-Specific Extraction

Using Life Sciences Stopwords

Option 1: Install custom stopwords file

# Copy life sciences stopwords to YAKE package
cp assets/stopwords_ls.txt /home/claude/yake-venv/lib/python3.12/site-packages/yake/core/StopwordsList/stopwords_ls.txt

# Use with lan="ls"
kw_extractor = yake.KeywordExtractor(lan="ls", n=3, top=20)

Option 2: Load custom stopwords directly

# Load stopwords from file
with open('assets/stopwords_ls.txt', 'r') as f:
    custom_stops = set(line.strip().lower() for line in f)

# Pass to extractor
kw_extractor = yake.KeywordExtractor(
    stopwords=custom_stops,
    n=3,
    top=20
)

Using AI/ML Stopwords

# Load AI/ML stopwords
with open('/mnt/skills/user/extracting-keywords/assets/stopwords_ai.txt', 'r') as f:
    ai_stops = set(line.strip().lower() for line in f)

# Extract with AI stopwords
kw_extractor = yake.KeywordExtractor(
    stopwords=ai_stops,
    n=3,
    top=20
)
keywords = kw_extractor.extract_keywords(text)

Deeper Extraction (n=2 + n=3 Combined)

For more comprehensive extraction, run both n=2 and n=3 and consolidate results. This captures both focused phrases and broader context with ~100% time overhead (still <2s for large documents).

import yake

# Load domain stopwords
with open('/mnt/skills/user/extracting-keywords/assets/stopwords_ai.txt', 'r') as f:
    stops = set(line.strip().lower() for line in f)

# Extract with n=2 (captures focused phrases)
kw_n2 = yake.KeywordExtractor(stopwords=stops, n=2, dedupLim=0.9, top=50)
results_n2 = kw_n2.extract_keywords(text)

# Extract with n=3 (captures broader context)
kw_n3 = yake.KeywordExtractor(stopwords=stops, n=3, dedupLim=0.9, top=50)
results_n3 = kw_n3.extract_keywords(text)

# Consolidate: union with score averaging for overlaps
combined = {}
for kw, score in results_n2:
    combined[kw] = score
for kw, score in results_n3:
    if kw in combined:
        combined[kw] = (combined[kw] + score) / 2
    else:
        combined[kw] = score

# Sort by score (lower = more important)
consolidated = sorted(combined.items(), key=lambda x: x[1])

# Display top 30
for kw, score in consolidated[:30]:
    print(f"{score:.4f}  {kw}")

Benefits:

• n=2 extracts cleaner domain-specific phrases ("disk move", "error rate")
• n=3 captures contextual combinations ("Move disk 1", "per-step error rate")
• Consolidation provides richer keyword set for topic modeling or search indexing

Performance:

• Combined approach: ~2x runtime of single extraction
• Typical timing: 0.4s (small doc) to 1.0s (large doc)
• Use when quality matters more than speed

Parameters

lan (str): Language code for built-in stopwords

• "en" - English (default)
• "ai" - AI/ML (if stopwords_ai.txt installed in YAKE)
• "ls" - Life sciences (if stopwords_ls.txt installed in YAKE)

Built-in YAKE languages (34 total):

• "ar" - Arabic
• "bg" - Bulgarian
• "br" - Breton
• "cz" - Czech
• "da" - Danish
• "de" - German
• "el" - Greek
• "es" - Spanish
• "et" - Estonian
• "fa" - Farsi/Persian
• "fi" - Finnish
• "fr" - French
• "hi" - Hindi
• "hr" - Croatian
• "hu" - Hungarian
• "hy" - Armenian
• "id" - Indonesian
• "it" - Italian
• "ja" - Japanese
• "lt" - Lithuanian
• "lv" - Latvian
• "nl" - Dutch
• "no" - Norwegian
• "pl" - Polish
• "pt" - Portuguese
• "ro" - Romanian
• "ru" - Russian
• "sk" - Slovak
• "sl" - Slovenian
• "sv" - Swedish
• "tr" - Turkish
• "uk" - Ukrainian
• "zh" - Chinese

n (int): Maximum n-gram size (default: 3)

• 1 - Single words only
• 2 - Up to 2-word phrases
• 3 - Up to 3-word phrases (recommended)
• 4-5 - May produce suboptimal results with YAKE's algorithm

dedupLim (float): Deduplication threshold (default: 0.9)

• Range: 0.0 to 1.0
• Higher values = more aggressive deduplication
• Controls handling of similar terms (e.g., "cancer cell" vs "cancer cells")

top (int): Number of keywords to return (default: 20)

stopwords (set): Custom stopwords set (overrides lan parameter)

Workflow Patterns

Single Document Analysis

import yake

# Read document
with open('/mnt/user-data/uploads/article.txt', 'r') as f:
    text = f.read()

# Extract keywords
kw_extractor = yake.KeywordExtractor(lan="en", n=3, top=30)
keywords = kw_extractor.extract_keywords(text)

# Format results
results = []
for kw, score in keywords:
    results.append(f"{score:.4f}  {kw}")

print("\n".join(results))

Comparing Stopwords Strategies

import yake

# Load life sciences stopwords
with open('assets/stopwords_ls.txt', 'r') as f:
    ls_stops = set(line.strip().lower() for line in f)

# Extract with English stopwords
kw_en = yake.KeywordExtractor(lan="en", n=3, top=20)
keywords_en = kw_en.extract_keywords(text)

# Extract with life sciences stopwords
kw_ls = yake.KeywordExtractor(stopwords=ls_stops, n=3, top=20)
keywords_ls = kw_ls.extract_keywords(text)

# Compare results
print("English stopwords:")
for kw, score in keywords_en:
    print(f"  {score:.4f}  {kw}")

print("\nLife sciences stopwords:")
for kw, score in keywords_ls:
    print(f"  {score:.4f}  {kw}")

Batch Processing

import yake
import os

# Initialize extractor
kw_extractor = yake.KeywordExtractor