polyleven
SymSpell
polyleven | SymSpell | |
---|---|---|
1 | 16 | |
76 | 3,051 | |
- | - | |
10.0 | 5.8 | |
over 1 year ago | about 2 months ago | |
C | C# | |
MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
polyleven
-
Spellcheck and Levenshtein distance
polyleven is the fastest Levenshtein distance library I've been able to find. It also has a threshold parameter which can be used to speed up the calculations. That being said, I've had a lot more success speeding up the processing of large text datasets by converting the words to a vector space (using e.g. word2vec) then calculating euclidean distance, which is much faster than calculating Levenshtein distance (assuming you are using vectorized operations). The fastest solution would probably be to use approximate nearest neighbor search (see for example the faiss library), but again you'll have to embed your words in a vector space and you'll need to decide if this is viable for your use case.
SymSpell
-
Should you combine edit distance "spell check" algorithms with phonetic matching algorithms for robust keyword finding?
The SimSpell algorithm uses deletions to determine edit distance of the input query word compared to a dictionary of correctly spelled words. The Double Metaphone algorithm (or other phonetic algorithms) convert the words to phonetic versions (phonetic "hashes" basically), and you then search based on the input phonetic hash matching the dictionary of phonetic hashes.
- Show HN: I automated 1/2 of my typing
-
Learn more about spell checkers
Books: a. "Speech and Language Processing" by Daniel Jurafsky and James H. Martin (3rd Edition) - This book covers various aspects of natural language processing, including a section on spelling correction that provides a comprehensive introduction to the topic. b. "Foundations of Statistical Natural Language Processing" by Christopher D. Manning and Hinrich Schütze - This book provides an overview of statistical approaches in NLP, including a chapter on spelling correction. Articles: a. "How to Write a Spelling Corrector" by Peter Norvig - This article demonstrates the development of a simple spelling corrector using statistical algorithms. It's a great starting point for understanding the basics of spell checkers. (Link: https://norvig.com/spell-correct.html) b. "The Design of a Proofreading Software Service" by Michael D. Garris and James L. Blue - This article presents the design and implementation of a spelling correction system that can be integrated into various applications. (Link: https://www.nist.gov/system/files/documents/itl/iad/89403123.pdf) c. "A Fast and Flexible Spellchecker" by Atkinson, K. (2006) - This article details the design of a spell checker that uses a combination of rule-based and statistical approaches for improved performance. (Link: https://aspell.net/0.60.6.1/aspell-0.60.6.1.pdf) Online Resources: a. The Natural Language Toolkit (NLTK) - This is a popular Python library for natural language processing. It includes a spell checker module and various examples of how to use it. (Link: https://www.nltk.org/) b. SymSpell - This is an open-source spell checking library that uses a Symmetric Delete spelling correction algorithm for high performance and accuracy. The GitHub repository includes a detailed description of the algorithm and examples of how to use it. (Link: https://github.com/wolfgarbe/SymSpell) These resources should provide a solid foundation for understanding the design, algorithms, and usage of spell checkers. Happy learning!
-
Turn the spellchecker into autocorrection software
Can this github.com/wolfgarbe/SymSpell or this github.com/ruby/did_you_mean or any of these github.com/topics/spell-check?o=desc&s=forks spellcheckers be used as an autocorrection software?
-
Help with deep learning project "autocorrection"
Do you absolutely need to use deep learning? There are tons of way faster autocorrect implementations that use levenshtein distances and non-DL techniques such as SymSpell or Norvig’s algorithm. DL is both expensive and requires tons of data to train on, I would stay away from that unless you’re doing it for your own enrichment or a school project.
-
Spellcheck and Levenshtein distance
This library claims to be orders of magnitude faster: https://github.com/wolfgarbe/SymSpell
-
Auto correct/Auto complete feature
If you want to do both at the same time (prefix search, allowing for misspellings), you can use a trie, but rather than just putting all your words in it, you can put everything in the "deletion neighborhood" of each word (that is, each possible variant of each word that has one character deleted), in an approach sort of like what's described here. Fair warning, though, that this gets a little hairy, and you'll have to decide how to weight prefix matches vs. misspellings in your rankings.
- SymSpell: 1M times faster spelling correction
-
Hacker News top posts: Mar 6, 2022
SymSpell: 1M times faster spelling correction\ (6 comments)
What are some alternatives?
distlib - Distance related functions (Damerau-Levenshtein, Jaro-Winkler , longest common substring & subsequence) implemented as SQLite run-time loadable extension. Any UTF-8 strings are supported.
JamSpell - Modern spell checking library - accurate, fast, multi-language
Java String Similarity - Implementation of various string similarity and distance algorithms: Levenshtein, Jaro-winkler, n-Gram, Q-Gram, Jaccard index, Longest Common Subsequence edit distance, cosine similarity ...
hunspell - The most popular spellchecking library.
RapidFuzz - Rapid fuzzy string matching in Python using various string metrics
wtpsplit - Code for Where's the Point? Self-Supervised Multilingual Punctuation-Agnostic Sentence Segmentation
lev - Levenshtein distance function as C Extension for Python 3
languagetool - Style and Grammar Checker for 25+ Languages
SymSpell - A JavaScript implementation of the Symmetric Delete spelling correction algorithm.
NLP-progress - Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.
ruby-spellchecker - Fast English spelling and grammar checker that can be used for autocorrection.
NetSpell - Spell Checker for .NET