symspell-ex

SymSpellEx

Spelling correction & Fuzzy search based on Symmetric Delete Spelling Correction algorithm (SymSpell)

Work in progress, need more optimizations

Installation

npm install symspell-ex --save

Changes v1.1.1

• Tokenization support
• Term frequency should be provided for training and terms should be unique
• Correct function return different object (Correction object)
• Hash table implemented in redis store instead of normal list structure
• Enhanced testing code and coverage
• Fixed bugs in lookup

Features

• Very fast
• Word suggestions
• Word correction
• Multiple languages supported - The algorithm, and the implementation are language independent
• Extendable - Edit distance and data stores can be implemented to extend library functionalities

Usage

Training

For single term training you can use add function:

import {SymSpellEx, MemoryStore} from 'symspell-ex';

const LANGUAGE = 'en';
// Create SymSpellEx instnce and inject store new store instance
symSpellEx = new SymSpellEx(new MemoryStore());
await symSpellEx.initialize();
// Train data
await symSpellEx.add("argument", LANGUAGE);
await symSpellEx.add("computer", LANGUAGE);

For multiple terms (Array) you can use train function:

const terms = ['argument', 'computer'];
await symSpellEx.train(terms, 1, LANGUAGE);

Searching

search function can be used to get multiple suggestions if available up to the maxSuggestions value

Arguments:

• input String (Wrong/Invalid word we need to correct)
• language String (Language to be used in search)
• maxDistance Number, optional, default = 2 (Maximum distance for suggestions)
• maxSuggestions Number, optional, default = 5 (Maximum suggestions number to return)

Return: Array<Suggetion> Array of suggestions

Example

await symSpellEx.search('argoments', 'en');

Example Suggestion Object:

{
  "term": "argoments",
  "suggestion": "arguments",  
  "distance": 2,
  "frequency": 155
}

Correction

correct function can be used to get the best suggestion for input word or sentence in terms of edit distance and frequency

Arguments:

• input String (Wrong/Invalid word we need to correct)
• language String (Language to be used in search)
• maxDistance Number, optional, default = 2 (Maximum distance for suggestions)

Return: Correction object which contains original input and corrected output string, with array of suggestions

Example

await symSpellEx.correct('Special relatvity was orignally proposed by Albert Einstein', 'en');

Returns this Correction object:

This output is totally depending on the quality of the training data that was push into the store

{
  "suggestions": [],
  "input": "Special relatvity was orignally proposed by Albert Einstein",
  "output": "Special relativity was originally proposed by Albert Einstein"
}

Computational Complexity

The algorithm has constant time O(1) time, independent of the dictionary size, but depend on the average term length and maximum edit distance, Hash Table is used to store all search entries which has an average search time complexity of O(1).

Why the algorithm is fast?

Pre-calculation

in training phase all possible spelling error variants as generated (deletes only) and stored in hash table

This makes the algorithm very fast, but it also required a large memory footprint, and the training phase takes a considerable amount of time to build the dictionary first time. (Using RedisStore makes it easy to train and build once, then search and correct from any external source)

Symmetric Delete Spelling Correction

It allows a tremendous reduction of the number of spelling error candidates to be pre-calculated (generated and added to hash table), which then allows O(1) search while getting spelling suggestions.

Library Design

Tokenizer

This interface can be implemented to provide a different tokenizer for the library

Interface type

export interface Tokenizer {
    tokenize(input: string): Array<Token>;
}

EditDistance

This interface can be implemented to provide more algorithms to use to calculate edit distance between two words

Edit Distance is a way of quantifying how dissimilar two strings (e.g., words) are to one another by counting the minimum number of operations required to transform one string into the other

Interface type

interface EditDistance {
    name: String;
    calculateDistance(source: string, target: string): number;
}

DataStore

This interface can be implemented to provide additional method to store data other than built-in stores (Memory, Redis)

Data store should handle storage for these 2 data types:

• Terms: List data structure to store terms and retrieve it by index
• Entries: Hash Table data structure to store dictionary entries and retrieve data by term (Key)

Data store should also handle storage for multiple languages and switch between them

Interface type

export interface DataStore {
    name: string;
    initialize(): Promise<void>;
    isInitialized(): boolean;
    setLanguage(language: string): Promise<void>;
    pushTerm(value: string): Promise<number>;
    getTermAt(index: number): Promise<string>;
    getTermsAt(indexes: Array<number>): Promise<Array<string>>;
    getEntry(key: string): Promise<Array<number>>;
    getEntries(keys: Array<string>): Promise<Array<Array<number>>>;
    setEntry(key: string, value: Array<number>): Promise<boolean>;
    hasEntry(key: string): Promise<boolean>;
    maxEntryLength(): Promise<number>;
    clear(): Promise<void>;
}

Built-in data stores

• Memory: Stores data in memory, using array structure for terms and high speed hash table (megahash) to manage dictionary entries

May be limited by node process memory limits, which can be overridden

• Redis: Stores data into Redis database using list structure to store terms and hash to store dictionary data

Very efficient way to train and store data, it will allow accessing by multiple processes and/or machines, also dumping and migrating data will be easy

TODO

• Tokenization
• Sentence correction
• Bulk data training
• Word Segmentation
• Domain specific correction

References

• Symmetric Delete Spelling Correction
• SymSpell

License

MIT