/extracting-keywords

name: extracting-keywords
description: Extract keywords from documents using YAKE algorithm with support for 34 languages (Arabic to Chinese). Use when users request keyword extraction, key terms, topic identification, content summarization, or document analysis. Includes domain-specific stopwords for AI/ML and life sciences. Optional deeper extraction mode (n=2+n=3 combined) for comprehensive coverage.
metadata:
  version: 0.2.1

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 YA