suggest
SymSpell
suggest | SymSpell | |
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2 | 16 | |
14 | 3,043 | |
- | - | |
3.6 | 5.8 | |
10 months ago | about 1 month ago | |
Nim | C# | |
ISC 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.
suggest
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Self Hosted SaaS Alternatives
You are welcome. Thanks are too rarely offered. :-)
You may also be interested in word stemming ( such as used by snowball stemmer in https://github.com/c-blake/nimsearch ) or other NLP techniques, but I don't know how internationalized/multi-lingual that stuff is, but conceptually you might want "series of stemmed words" to be the content fragments of interest.
Similarity scores have many applications. Weights on graph of cancelled downloads ranked by size might be one. :)
Of course, for your specific "truncation" problem, you might also be able to just do an edit distance against the much smaller filenames and compare data prefixes in files or use a SHA256 of a content-based first slice. ( There are edit distance algos in Nim in https://github.com/c-blake/cligen/blob/master/cligen/textUt.... as well as in https://github.com/c-blake/suggest ).
Or, you could do a little program like ndup/sh/ndup to create a "mirrored file tree" of such content-based slices then you could use any true duplicate-file finder (like https://github.com/c-blake/bu/blob/main/dups.nim) on the little signature system to identify duplicates and go from path suffixes in those clusters back to the main filesystem. Of course, a single KV store within one or two files would be more efficient than thousands of tiny files. There are many possibilities.
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SymSpell: 1M times faster spelling correction
As jamra correctly points out, the entry point to this (which gets a lot of traction on HN) is indeed attacking a strawman tutorial-written-on-an-airplane algorithm. So, the 1M speed-up is majorly over-hyped.
That said, the technique is not wholly without merit, but does carry certain "risk-reward" trade offs related to latency in the memory/storage system because of SymSpell's reliance upon large hash tables. For details see https://github.com/c-blake/suggest
SymSpell
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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
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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!
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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?
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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.
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Spellcheck and Levenshtein distance
This library claims to be orders of magnitude faster: https://github.com/wolfgarbe/SymSpell
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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
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Hacker News top posts: Mar 6, 2022
SymSpell: 1M times faster spelling correction\ (6 comments)
What are some alternatives?
nimsearch - A nascent tutorial/intro to search engine ideas in Nim
JamSpell - Modern spell checking library - accurate, fast, multi-language
abydos - Abydos NLP/IR library for Python
hunspell - The most popular spellchecking library.
ordiri
wtpsplit - Code for Where's the Point? Self-Supervised Multilingual Punctuation-Agnostic Sentence Segmentation
jsymspell - Java 8+ zero-dependency port of SymSpell: 1 million times faster through Symmetric Delete spelling correction algorithm
languagetool - Style and Grammar Checker for 25+ Languages
home-ops - Wife approved HomeOps driven by Kubernetes and GitOps using Flux
SymSpell - A JavaScript implementation of the Symmetric Delete spelling correction algorithm.
core - OPNsense GUI, API and systems backend
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.