steno-dictionaries VS SymSpell

https://steno.sammdot.ca/emily-symbols.png

for these, where it says starter, you just press all those keys down, and then on the other side you press the keys listed for what you want. so for example, I can enter like ~104 symbols without moving my hands. the average sybmol layer has like 20. the crossplatform movement dict lets me move around much easier in any text field. (note that you don't really even need to know what the key names you are pressing are as its all a pattern) I currently have six other dictionaries that I use some of the time. you can see more here: https://www.openstenoproject.org/stenodict/.

any cli program would be very easy to add most of the commands to a dictionary if you wanted. for example, a basic git dictionary: https://github.com/didoesdigital/steno-dictionaries/blob/mas...

plover has made using a computer much more fun. its a bit of a hard sell for a lot of people, but I recommend trying out some of the other dictionaries to see what you can do besides type words fast. its seriously really crazy that we are only pressing one key at a time using a keyboard.

In the spirit of reduction, I look another look at https://github.com/didoesdigital/steno-dictionaries/blob/master/dictionaries/dict.json. In the same way that I asked how many of the 128 left-hand (four fingers only) states are actually used by the dictionary, I can also ask how many of the 4 million available chords are actually used?

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.

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!

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?

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.

This library claims to be orders of magnitude faster: https://github.com/wolfgarbe/SymSpell

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\ (6 comments)