symspell-ex
v1.1.10
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Spelling correction & Fuzzy search based on symmetric delete spelling correction algorithm
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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
- [x] Tokenization
- [x] Sentence correction
- [x] Bulk data training
- [ ] Word Segmentation
- [ ] Domain specific correction
References
License
MIT