Building a German Thesaurus Using Dictionary Entries and Hyphenation Rules

Building a German Thesaurus Using Dictionary Entries and Hyphenation Rules

Overview

• Goal: create a German thesaurus that groups synonyms/near-synonyms and supports correct hyphenation for compound words and word breaks.

Key components

1. Source dictionary — lemma list, POS tags, inflection paradigms, example usages.
2. Synonym extraction — map lemmas to synonym sets (synsets) using dictionary definitions, bilingual dictionaries, word embeddings, or existing thesauri (e.g., OpenThesaurus).
3. Hyphenation patterns — German-specific TeX/Patgen patterns (e.g., hyph-de), syllable rules, and exception lists to correctly split compounds and prefixes.
4. Morphology & compound handling — decompose compounds (Fugenelemente like -s-, -es-, linking elements), normalize stems, and generate inflected forms.
5. POS and sense disambiguation — separate senses so synonyms align by meaning (e.g., Bank = “bench” vs “bank (finance)”).
6. Data model — entries: lemma, POS, senses (definition, example), synset id, hyphenation points for lemma and canonical inflections, compound components.
7. Quality & evaluation — native-speaker validation, precision/recall vs. gold thesaurus, hyphenation test suite, and automated checks for false synonyms across POS or senses.

Implementation steps

1. Gather resources: licensed German dictionary data, OpenThesaurus, hyphenation pattern files (hyph-de), morphological lexicon (e.g., GermaNet, SMOR, or GF resources).
2. Normalize and tokenize entries; lemmatize and attach POS tags.
3. Derive candidate synonym links: exact definition matches, shared translations, distributional similarity (fastText/BERT embeddings). Score candidates.
4. Cluster into synsets per POS using graph clustering (connected components, Louvain, or agglomerative clustering with thresholds).
5. Attach hyphenation: apply pattern-based hyphenation to lemma and generated inflections; for compounds, hyphenate at component boundaries and validate with syllable rules. Store exception overrides.
6. Sense disambiguation: use context sentences and contextual embeddings to separate senses; split synsets when semantic divergence detected.
7. Produce outputs: lookup API, downloadable files (XML/JSON), and optional search/index with fuzzy matching.
8. Test & iterate: native review, hyphenation regression tests, user feedback loops.

Practical tips

• Prefer high-quality morphological resources to handle German compounds robustly.
• Use pattern-based hyphenation as baseline and add an exceptions list from corpus evidence.
• Keep synset granularity balanced: too fine harms usability; too coarse mixes unrelated senses.
• Track provenance and confidence scores per synonym link and hyphenation entry.

Example data schema (simplified)

• id: synset_1234
• lemma: “Bank”
• pos: “NOUN”
• senses: [{sense_id, definition, examples}]
• synonyms: [“Sitzbank”, “Bankinstitut”—only for relevant senses]
• hyphenation: “Bank” -> “Bank” (no break); compounds: “Sitzbank” -> “Sitz-bank”
• confidence: 0.87
• source: [“OpenThesaurus”,“hyph-de”]

Timeline (rough)

• 0–2 weeks: collect and normalize resources.
• 2–6 weeks: synonym extraction, initial clustering.
• 6–10 weeks: hyphenation integration, morphology, and compound handling.
• 10–14 weeks: evaluation, native review, and release.

If you want, I can: provide a JSON schema for entries, propose algorithms/code snippets for clustering or hyphenation, or draft an exception list extraction method.