usaddress
SymSpell
Our great sponsors
usaddress | SymSpell | |
---|---|---|
5 | 16 | |
1,485 | 3,034 | |
0.7% | - | |
0.0 | 6.0 | |
4 months ago | 21 days ago | |
Python | 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.
usaddress
-
Which of your favorite Python 3.11 packages lack Python 3.11 support?
Usaddress https://github.com/datamade/usaddress
-
Script to split addresses in Google Sheets?
Assuming you’re working with addresses in the US, here’s a Python package that should help: https://github.com/datamade/usaddress
-
PyWhat: Identify Anything
Some great probabilistic python libraries:
https://github.com/datamade/usaddress - "usaddress is a Python library for parsing unstructured address strings into address components, using advanced NLP methods."
https://github.com/datamade/probablepeople - "probablepeople is a python library for parsing unstructured romanized name or company strings into components, using advanced NLP methods."
- Turning unstructured address data into a structure Salesforce Address Field
-
Fuzzy Name Matching in Postgres
For address parsing, I've had good luck with this package: https://github.com/datamade/usaddress
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?
libpostal - A C library for parsing/normalizing street addresses around the world. Powered by statistical NLP and open geo data.
JamSpell - Modern spell checking library - accurate, fast, multi-language
pyWhat - 🐸 Identify anything. pyWhat easily lets you identify emails, IP addresses, and more. Feed it a .pcap file or some text and it'll tell you what it is! 🧙♀️
hunspell - The most popular spellchecking library.
probablepeople - :family: a python library for parsing unstructured western names into name components.
wtpsplit - Code for Where's the Point? Self-Supervised Multilingual Punctuation-Agnostic Sentence Segmentation
DataProfiler - What's in your data? Extract schema, statistics and entities from datasets
languagetool - Style and Grammar Checker for 25+ Languages
ctparse - Parse natural language time expressions in python
SymSpell - A JavaScript implementation of the Symmetric Delete spelling correction algorithm.
FuckIt.py - The Python error steamroller.
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.