KeenWrite VS SymSpell

KeenWrite is my free, open-source, cross-platform desktop Markdown editor that can produce beautifully typeset PDFs. I started working on it years ago to help write a novel that has a complex timeline and I couldn't find a text editor that would allow me to integrate a character sheet with the story itself.

https://github.com/DaveJarvis/keenwrite

Tutorials:

* https://www.youtube.com/playlist?list=PLB-WIt1cZYLm1MMx2FBG9...

Here's what I mean by using variables directly:

* https://www.youtube.com/watch?v=CFCqe3A5dFg

CommonMark doesn't propose a standard for bibliographic references. Would anyone find the editor more appealing if it had cross-references and citations?

My cross-platform desktop text editor, KeenWrite, allows users to define variables in an external YAML file. The editor calls out to Kroki[1] to convert text-based diagrams to SVG. The diagrams can reference variables and are rendered using EchoSVG[2].

KeenWrite[3] can produce PDF documentation from Markdown documents that has PlantUML diagrams with elements stored in an external, machine-readable file. Here are screenshots showing variables on the left, diagram text in the middle, and a real-time render on the right:

* https://raw.githubusercontent.com/DaveJarvis/KeenWrite/main/...

* https://raw.githubusercontent.com/DaveJarvis/KeenWrite/main/...

KeenWrite supports all diagrams offered by Kroki, which includes "diagram-plantuml".

[1]: https://kroki.io/

[2]: https://github.com/css4j/echosvg/

[3]: https://github.com/DaveJarvis/keenwrite

Although not interactive, I've created a video series that shows advanced usage of Markdown. Namely R, external variables, diagrams, math, annotations, and a different approach to metadata:

* https://www.youtube.com/playlist?list=PLB-WIt1cZYLm1MMx2FBG9...

Tutorial 4 shows basic Markdown:

* https://www.youtube.com/watch?v=qNbGSiRzx-0

The top-right of each video shows keyboard and mouse clicks to help follow along.[1] My desktop text editor, KeenWrite[2], is used in the tutorials.

[1]: https://github.com/DaveJarvis/kmcaster

[2]: https://github.com/DaveJarvis/keenwrite

https://github.com/DaveJarvis/keenwrite/blob/main/scripts/bu...

My template script provides a way to make user-friendly shell scripts. In a script that uses the template, you define the dependencies and their sources:

    DEPENDENCIES=(

I didn't create the fork, nor am I affiliated with the project. I use it in my text editor, KeenWrite to rasterize SVG.

KeenWrite Themes[1] are instructions that tell ConTeXt how to typeset XHTML documents (content) into PDF files (presentation). I made a tutorial that shows how my FOSS desktop text editor, KeenWrite[3], allows users to write in Markdown to typeset a document against a particular theme.

Before it can be used for scientific papers, it needs cross-references, which, unfortunately, aren't part of the CommonMark specification.

I posit that the vast majority of LaTeX users don't grok how to separate content from presentation. When I asked a question on TeX.SE about how to adjust the line spacing between enumerated items (spanning a couple dozen enumerated lists), the vast majority of people voted for the answer of using `\itemsep0em` to tweak each list ... individually.[4] The correct answer, IMO, is to fix the problem globally, and not waste time tweaking individual lists.

[1]: https://github.com/DaveJarvis/keenwrite-themes

[2]: https://www.youtube.com/watch?v=3QpX70O5S30

[3]: https://github.com/DaveJarvis/keenwrite

[4]: https://tex.stackexchange.com/questions/6081/reduce-space-be...

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)