How minhaskamal/bengalidictionary works
1. Repository type
- This is primarily a data repository for Bengali language resources, with two small Java utilities layered on top.
- The architecture is centered on static dictionary/word-list files rather than a running service or application.
2. Core data assets
- The root of the repo contains the main artifacts: multiple Bengali word lists, translation dictionaries, a character-combination list, and a phonetic word-frequency file.
- These are the primary source of truth for all functionality in the repo.
3. Dataset families
- Word lists: BengaliWordList_40.txt, _48.txt, _112.txt, _439.txt
- Translation dictionaries: BengaliDictionary_17.txt, _36.csv, _82.xml, _93.csv, _93..csv, BengaliDictionary.json
- Supporting linguistic resources: BengaliCharacterCombinations.txt, BengaliPhoneticWordFrequency_164.txt
- The varied suffixes suggest different sizes, versions, or export formats of the same underlying lexical content.
4. File-format boundary
- The repository intentionally exposes the same language data in multiple formats: TXT, CSV, XML, and JSON.
- This creates a clear boundary between the raw linguistic dataset and any downstream consumer that wants a specific serialization format.
5. Java utilities
- src/Dictionary.java is the Java implementation for an English-to-Bengali dictionary lookup use case.
- src/SpellChecker.java is the Java implementation for Bengali spell checking / correction.
- These are small consumers of the static resources, not a full application framework.
6. Data flow into the Java code
- The expected flow is: load local dictionary/word-list files → index or scan the words → perform lookup/spell-check operations.
- The Java code likely reads the dataset files directly from disk, making the repo fundamentally file-driven rather than network-driven.
7. Primary functional split
- The repository has two main functional paths:
- Translation/lookup path: dictionary data served through Dictionary.java
- Spell-check path: word lists and character-combination data used by SpellChecker.java
- The phonetic frequency file likely supports ranking or suggestion quality in spell correction or typing assistance.
8. No service layer or persistence subsystem
- There is no database, API server, queue, or frontend in this repository.
- Persistence is entirely via checked-in files, and “state” is represented by the dataset files themselves.
9. External consumption model
- The README positions the repo as a reusable resource for spell checking, spell correction, dictionary lookup, typing suggestion, and related NLP tasks.
- So the architectural boundary is: this repo supplies lexical assets and simple reference implementations; downstream apps integrate it as a library/data source.
10. Operational/runtime view
- The only runtime technology explicitly implied is Java for the sample implementations.
- Everything else is static content, so an architecture diagram should show a local file corpus feeding Java utilities, with no external dependencies beyond the consumer application.
Main entry points: SpellChecker.java SpellChecker · Dictionary.java Dictionary · BengaliDictionary.json Dictionary JSON · BengaliCharacterCombinations.txt Char combos
Components 17
Static lexical data
Checked-in Bengali language resources and format variants.
- Dictionary set · Bengali translation/dictionary content published in several export formats and versions.
- Word lists · Multiple Bengali word-list snapshots used as the main vocabulary source.
- Linguistic aids · Character-combination and phonetic-frequency resources that support correction and suggestion logic.
- Dict 17 · BengaliDictionary_17.txt
BengaliDictionary_17.txt - Dict 36 · BengaliDictionary_36.csv
BengaliDictionary_36.csv - Dict 82 · BengaliDictionary_82.xml
BengaliDictionary_82.xml - Dict 93 · BengaliDictionary_93.csv
BengaliDictionary_93.csv - Dict 93 alt · BengaliDictionary_93..csv
BengaliDictionary_93..csv - Dictionary JSON · BengaliDictionary.json
BengaliDictionary.json - Words 40 · BengaliWordList_40.txt
BengaliWordList_40.txt - Words 48 · BengaliWordList_48.txt
BengaliWordList_48.txt - Words 112 · BengaliWordList_112.txt
BengaliWordList_112.txt - Words 439 · BengaliWordList_439.txt
BengaliWordList_439.txt - Char combos · BengaliCharacterCombinations.txt
BengaliCharacterCombinations.txt - Phonetic freq · BengaliPhoneticWordFrequency_164.txt
BengaliPhoneticWordFrequency_164.txt
Java utilities
Small reference implementations that consume the local data files.
- Dictionary · English-to-Bengali dictionary lookup utility.
src/Dictionary.java - SpellChecker · Bengali spell checking and correction utility.
src/SpellChecker.java
Connections 18
- Dictionary set → Dict 17: serialized as · Dictionary content appears in text export form.
- Dictionary set → Dict 36: serialized as · Dictionary content appears in CSV export form.
- Dictionary set → Dict 82: serialized as · Dictionary content appears in XML export form.
- Dictionary set → Dict 93: serialized as · Dictionary content appears in another CSV variant.
- Dictionary set → Dict 93 alt: serialized as · Alternative CSV variant of the dictionary data.
- Dictionary set → Dictionary JSON: serialized as · Dictionary content appears in JSON form.
- Word lists → Words 40: includes · One of several word-list snapshots.
- Word lists → Words 48: includes · One of several word-list snapshots.
- Word lists → Words 112: includes · One of several word-list snapshots.
- Word lists → Words 439: includes · Largest word-list snapshot in the repo.
- Linguistic aids → Char combos: includes · Character-combination constraints or patterns.
- Linguistic aids → Phonetic freq: includes · Phonetic ranking/frequency support data.
- Dictionary → Dictionary set: reads · Lookup utility consumes dictionary data files directly.
- Dictionary → Dictionary JSON: reads · Can consume the JSON dictionary representation.
- SpellChecker → Word lists: reads · Spell-check logic scans or indexes word lists.
- SpellChecker → Linguistic aids: uses · Correction logic relies on character and phonetic aids.
- SpellChecker → Phonetic freq: ranks by · Frequency data can improve suggestion ordering.
- SpellChecker → Char combos: validates with · Character-combination data can constrain candidate generation.