crosstem 0.2.0

Comprehensive morphological analysis: derivational stemming, inflectional analysis, and cross-lingual etymology

Crosstem

A comprehensive Python package for morphological analysis combining derivational stemming, inflectional analysis, and cross-lingual etymology.

Why Crosstem?

Crosstem finds true linguistic roots across part-of-speech boundaries, which is something traditional stemmers and lemmatizers cannot do.

What Makes It Different

# Traditional stemmers (Porter, Lancaster) - Rule-based, prone to errors
Porter: "organization" → "organ"        # Overstemming loses meaning

# Lemmatizers (WordNet, spaCy) - Only handle inflections, not derivations  
WordNet: "organization" → "organization"  # Can't cross POS boundaries
WordNet: "beautiful" → "beautiful"        # Stuck at adjective form

# Crosstem - Linguistically accurate, crosses POS boundaries
Crosstem: "organization" → "organize"   # Noun → Verb (true root)
Crosstem: "beautiful" → "beauty"        # Adjective → Noun (semantic base)

Key Advantages

• Cross-POS derivational stemming: Only library that finds roots across parts of speech
• Linguistic accuracy: Uses MorphyNet morphological data, not brittle rules
• Etymology tracing: 4.2M relationships across 2,265 languages (unique feature)
• Word families: Discover complete derivational networks (e.g., organize → 43 related words)
• Pure Python: Zero dependencies, works anywhere (no heavy ML models required)
• 15 languages: Multilingual morphology support out of the box

Performance Benchmark vs Porter

We compared Crosstem against the widely-used Porter stemmer on 44 English words with 1,000 iterations each.

Speed Results

Crosstem:     ~0.080s (~547,000 words/sec)
Porter:       ~0.490s (~90,000 words/sec)

⚡ Crosstem is ~6× FASTER than Porter

Why? Crosstem uses O(1) hash lookups in JSON dictionaries, while Porter applies sequential pattern-matching rules.

Note: Results averaged over multiple runs; ±3% variance is normal due to system load.

Accuracy Comparison

Word	Crosstem	Porter	Winner
organization	organize	organ	✅ Crosstem (finds true root)
organizational	organize	organiz	✅ Crosstem (multi-hop)
beautiful	beauty	beauti	✅ Crosstem (crosses POS)
destruction	destruct	destruct	⚖️ Tie
democracy	democracy	democraci	✅ Crosstem (avoids error)
computerization	compute	computer	✅ Crosstem (deeper root)
happiness	happy	happi	✅ Crosstem (productivity filter avoids "hap")
redness	red	red	⚖️ Tie

Key Findings:

1. Cross-POS stemming: Crosstem finds roots across parts of speech (organization → organize, verb), Porter cannot
2. Overstemming prevention: Porter creates non-words (beauti, organiz), Crosstem always produces real words
3. Data quality: Crosstem filters bad roots (democrat), Porter has no quality control
4. Multi-hop: Crosstem traverses multiple derivations (organizational → organization → organize), Porter only strips one suffix

When to Use Each

Choose Crosstem when:

• ✅ Need linguistically accurate roots
• ✅ Working with derivational families (organize/organizer/organization)
• ✅ Building semantic search, clustering, or word embeddings
• ✅ Quality matters more than simplicity
• ✅ Multilingual support needed (15 languages)

Choose Porter when:

• ✅ Legacy system compatibility required
• ✅ Working with noisy/misspelled text (rule-based is robust)
• ✅ Only need basic suffix normalization
• ✅ Want the absolute simplest possible solution

Note: Crosstem is now faster than Porter while being more accurate, making it the better choice for most modern NLP applications.

Features

• Derivational Stemming: Find roots across part-of-speech boundaries (organization → organize)
• Inflectional Analysis: Lemmatization and grammatical forms (running → run)
• Cross-Lingual Etymology: Trace word origins across 2,265 languages
• Word Family Analysis: Discover complete derivational networks

Installation

pip install crosstem

Optional: Etymology Data

Etymology features require additional data (~1 GB) that's downloaded separately:

from crosstem import download_etymology

# One-time download (saves to package data directory)
download_etymology()

Or from command line:

python -m crosstem.download

Quick Start

Basic Morphological Analysis

from crosstem import MorphologyAnalyzer

# Works immediately - no etymology needed
analyzer = MorphologyAnalyzer('eng', load_etymology=False)
result = analyzer.analyze('organizations')

print(result['derivational_stem'])    # 'organize'
print(result['inflectional_lemma'])   # 'organization'

With Etymology Features

from crosstem import MorphologyAnalyzer, download_etymology

# Download etymology data first (one-time)
if not MorphologyAnalyzer.is_etymology_available():
    download_etymology()

# Now etymology features work
analyzer = MorphologyAnalyzer('eng', load_etymology=True)
result = analyzer.analyze('portmanteau')
print(result['etymology'])  # Shows Middle French origin

Usage

Derivational Stemming

from crosstem import DerivationalStemmer

stemmer = DerivationalStemmer('eng')
stemmer.stem('organization')  # 'organize'
stemmer.stem('beautiful')     # 'beauty'

family = stemmer.get_word_family('organize')
# ['organize', 'organizer', 'organization', ...]

Inflectional Analysis

from crosstem import InflectionAnalyzer

inflector = InflectionAnalyzer('eng')
inflector.get_lemma('running')  # 'run'

forms = inflector.get_inflections('run')
# [{'form': 'runs', 'pos': 'V', ...}, ...]

Etymology (Requires Download)

from crosstem import EtymologyLinker, download_etymology

# Download etymology data first (one-time, ~1 GB)
download_etymology()

linker = EtymologyLinker()
chain = linker.trace_origin_chain('portmanteau', 'English')
# [{'term': 'portmanteau', 'lang': 'English'},
#  {'term': 'portemanteau', 'lang': 'Middle French', ...}]

Supported Languages

15 languages with derivational morphology:

• English (eng), Russian (rus), French (fra), German (deu), Spanish (spa)
• Portuguese (por), Italian (ita), Polish (pol), Czech (ces)
• Serbo-Croatian (hbs), Hungarian (hun), Finnish (fin)
• Swedish (swe), Mongolian (mon), Catalan (cat)

Plus 2,265 languages with etymology data.

How It Works

Theoretical Framework

Crosstem is built on three pillars of morphological linguistics:

1. Derivational Morphology (Word Formation)

Unlike inflection (which modifies words grammatically), derivation creates new words by adding affixes or converting between parts of speech:

• organize + -ation → organization (verb → noun)
• beauty + -ful → beautiful (noun → adjective)
• organize + -er → organizer (agent noun)

Crosstem models this as a directed graph where:

• Nodes = word forms with POS tags
• Edges = derivational relationships (affixes, conversions)
• Stemming = graph traversal to find the root (preferring verbs and shorter forms)

         ┌─────────────┐
         │  organize   │ ← ROOT (verb, shortest)
         │     (V)     │
         └──────┬──────┘
           ┌────┴────┬───────┬──────────┐
           ▼         ▼       ▼          ▼
      organizer  organization  organized  reorganize
         (N)        (N)         (ADJ)      (V)
                     │
                     ▼
             organizational
                  (ADJ)

This graph-based approach ensures linguistically accurate roots, avoiding the overstemming problem of rule-based stemmers.

2. Inflectional Morphology (Grammatical Forms)

Inflection expresses grammatical categories without changing core meaning:

• Number: cat → cats
• Tense: run → ran, running
• Comparison: good → better, best

Crosstem stores inflectional paradigms as lemma → forms mappings:

{
  "run": {
    "pos": "V",
    "forms": {
      "runs": [{"pos": "V", "features": "PRS;3;SG"}],
      "running": [{"pos": "V", "features": "V.PTCP;PRS"}],
      "ran": [{"pos": "V", "features": "PST"}]
    }
  }
}

This enables both lemmatization (running → run) and paradigm generation (run → all forms).

3. Cross-Lingual Etymology (Historical Linguistics)

Etymology traces how words evolve and transfer across languages:

• Borrowing: English portmanteau ← Middle French portemanteau
• Cognates: Dutch woordenboek ↔ German Wörterbuch (shared Germanic ancestor)
• Inheritance: Latin mater → French mère, Italian madre, Spanish madre

Crosstem represents this as a multilingual graph with typed edges:

English: "portmanteau" ──borrowed_from──→ Middle French: "portemanteau"
                                               │
                                        has_root: "porter" (to carry)
                                               │
                                        has_root: "manteau" (coat)

Implementation

All three frameworks are implemented as fast JSON lookups with graph traversal algorithms:

1. Preprocessing: TSV/CSV data → optimized JSON dictionaries
2. Indexing: Multi-level indices (word → derivations, lemma → inflections, term+lang → etymology)
3. Traversal: BFS for word families, chain-following for etymology
4. Filtering: POS preference (verbs), length minimization, cycle detection

Result: 0.01-0.05ms lookups with zero dependencies, purely in Python.

Stemming Algorithm Details

Multi-Hop BFS Traversal

Crosstem uses a sophisticated breadth-first search algorithm to find the optimal root:

# Example: organizational → organization → organize
organizational  (14 chars, ADJ)
    ↓ (depth 1)
organization    (12 chars, N, productivity=16) ← candidate
    ↓ (depth 2)  
organize        (8 chars, V, productivity=13)  ← BEST (verb, shortest)

Algorithm steps:

1. Start from input word, add to queue
2. Expand all DERIVED_FROM relationships (parents in morphology graph)
3. Score each candidate:
   • Length (shorter is better)
   • POS (verbs score -10, nouns -5)
   • Depth (penalize by +2 per hop)
4. Filter by productivity threshold (language-specific):
   • English: Verbs ≥5, Others ≥9
   • French/Italian: Verbs ≥4, Others ≥5
   • German: Verbs ≥4, Others ≥3 (compound-heavy)
   • Spanish/Portuguese: Verbs ≥3, Others ≥4
   • Russian/Slavic: Verbs ≥3, Others ≥2-3 (lower productivity)
5. Continue traversal through low-productivity nodes (enables multi-hop)
6. Return lowest-scoring candidate that's shorter than input or a verb

Productivity-Based Filtering

Problem: MorphyNet contains archaic roots (e.g., hap) and data errors (e.g., democracy → democrat)

Solution: Use productivity as a quality signal. Words with many derivations are more likely to be modern, correct roots.

Examples (English thresholds: V≥5, N≥9):

Word	Productivity	POS	Threshold	Result
red	18 derivations	N	≥9	✅ PASS (productive noun)
run	33 derivations	V	≥5	✅ PASS (very productive verb)
destruct	6 derivations	V	≥5	✅ PASS (verb threshold)
hap	8 derivations	N	≥9	❌ FILTERED (archaic)
democrat	7 derivations	N	≥9	❌ FILTERED (data error)

This data-driven approach avoids hard-coded rules while maintaining quality.

Language-Specific Calibration: Thresholds are adjusted for each language based on morphological richness. Languages with lower overall productivity (Russian, Spanish) use lower thresholds to avoid over-filtering, while English uses higher thresholds due to rich derivational data.

Why This Works

Traditional stemmers fail because they use brittle suffix rules:

# Porter stemmer rule: -ation → (remove suffix)
"organization" → "organ"  # Lost the "ize" (overstemming)

# Lancaster stemmer: even more aggressive
"organization" → "org"    # Completely loses meaning

Crosstem succeeds because it uses linguistic knowledge:

# Graph data knows: organization DERIVED_FROM organize
"organization" → "organize"  # Preserves semantic relationship

Data Sources

• MorphyNet v1.0: Derivational and inflectional morphology (CC BY-SA 4.0)
• Wiktionary: Cross-lingual etymology data (CC BY-SA 3.0)

Citation

If you use this library in your research, please cite:

@software{crosstem2025,
  title={Crosstem: Comprehensive Morphological Analysis for Python},
  author={Avinash Bhojanapalli},
  year={2025},
  url={https://github.com/droidmaximus/crosstem},
  note={A Python package for derivational stemming, inflectional analysis, and cross-lingual etymology}
}

@inproceedings{batsuren2021morphynet,
  title={MorphyNet: a Large Multilingual Database of Derivational and Inflectional Morphology},
  author={Batsuren, Khuyagbaatar and Bella, Gábor and Giunchiglia, Fausto},
  booktitle={Proceedings of the 18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology},
  pages={39--48},
  year={2021}
}

@misc{wiktionary2025,
  title={Wiktionary, The Free Dictionary},
  author={{Wiktionary contributors}},
  year={2025},
  url={https://en.wiktionary.org/},
  note={Etymology data extracted from Wiktionary dumps}
}

License

• Code: MIT License
• Data: CC BY-SA 4.0 (MorphyNet), CC BY-SA 3.0 (Wiktionary)