Show HN: Language detection as a service
getlang.io
getlang.io
If it's your own built service, it's critical how you explain the hows and whys of your forecast availability and scalability numbers for your chosen architecture, given who you are competing with.
[1]https://developers.google.com/translate/v2/using_rest#detect...
The tricky parts are usually political - are users going to be angry if you confuse Indonesian with Malaysian, or so on?
In fact, we had a course for high school students where they learnt how a language guesser works and where they had to change a language guesser. A simplistic method that already works very well is:
* Create an n-gram fingerprint for each language by making a list of character uni-, bi-, and trigrams ordered by their frequency in a text. Retain the (say) 300 most frequent n-grams.
* To categorize a text, create a fingerprint for that text. Then compute for each language the sum n-gram rank differences. If an n-gram does not occur, the difference is the fingerprint size. Finally, pick the language with the lowest sum.
Of course, you can do fancier things, such as training a SVM or logistic regression classifier with n-grams and words as features, etc.
An interesting variation is to be able to distinguish different languages in a text. E.g. a Dutch text with English quotes.
There's a multitude of problems with real-world texts that a robust guesser must deal with gracefully: short texts; texts in none of the languages the "guesser" was trained for (is it able to return "none of the above?" or does it return a random one then?); texts in multiple languages (incl. common noun phrases phrases inserted into text in another language); texts with parts repeated multiple times (web pages and blogs in particular are a bitch!), which skews char/word distributions and messes up statistical models etc.
It's the same thing as with spelling correction, really. "But Norvig did it in 1.5 lines of Python!" See "A Spellchecker Used To Be A Major Feat of Software Engineering" at https://news.ycombinator.com/item?id=3466927 Spoiler: it still is, except for "drive-by ML apps".
A bit sour, are we? ;)
The point is that it is an NLP task where it is relatively easy to get good results on general text (see Cavnar and Trenkle). So, it is a fun and satisfying exercise.
Saying there is difficult noisy data is pointing out the obvious ;).
But HN responses to posts like these overwhelmingly suggest it's far from obvious.
So, it is a fun and satisfying exercise.
I used to be a bit upset when someone claims to have implemented a state-of-the-art POS tagger, when they just took the dictionary and rules produced by Eric Brill's learner verbatim and apply those. Or worse, they take the first ten rules ;).
Nowadays I just prefer to evolution let do its work. The best or the one with the best marketing wins :).
I'm still in the naive do-it-well phase, but seeing the downvotes, it may be time to join the hipsters. Or at least shut up ;)
Obviously, it is highly domain and text length dependent (as I also mentioned in another comment).
But, e.g. Cavnar and Trenkle obtained a 99.8% accuracy on newsgroup articles in 14 languages using the method outlined above.
There are very few NLP tasks where you can achieve such high accuracy with relatively simple and understandable methods. That's why it is a nice subject for an NLP introduction to e.g. high school students.
I have worked in parsing and generation, where it is difficult to obtain satisfying results with many man years of work on newspaper text, let alone tweets or Youtube comments ;).
Do you know any interesting work related to the language distinction idea on the same text?
http://mt-archive.info/IJCNLP-2008-Ehara.pdf http://202.41.85.68/knm-publications/lang_id_jql.pdf
I see three spelling errors in your language list:
- Panjabi should be Punjabi;
- Teligu should be Telugu;
- Ukraininan should be Ukrainian.
There are also a few grammar problems earlier in the document, and style problems (e.g. English doesn't use a space before sentence-ending punctuation marks).
Also, I think it would be clearer if it said "A simple and scalable way to automatically classify text by language" instead of "A simple and scalable way to classify automatically text by language".
Design looks very clean though. Nice work.
EDIT: Also, your social media links at the bottom aren't hooked up yet.
http://www.let.rug.nl/vannoord/TextCat/
Python version:
http://thomas.mangin.com/data/source/ngram.py
It's something that is fun to implement and doesn't take more than a few hours at most.
https://github.com/danieldk/dictomaton
Though, having implemented a language guesser myself, it's only an issue with very short texts (a few words). On longer texts models based on character n-grams achieve very high accuracies.
As others already mentioned, it would be good to have users try examples before signup.
For 20 letters
TR=95.90 EN=94.96
For 50 Letters
TR=99.44 EN=99.53
If 50 letters are used in a Doc, it identifies about 20000 docs per second in a decent desktop.
https://code.google.com/p/chromium-compact-language-detector...
O_O