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Show HN: Language detection as a service
- captn3m0 13y agoFor those who thought (like me) that this was a programming language detection service, you can take a look at github/linguist.
- diasks2 13y agoLooks interesting. Why not have a input on the landing page where someone can try it out without even signing up? I think then people could give it a spin before they give away their email address. Otherwise, the user just has to trust your 99% figure, which it might be helpful to give some data around, even if it is a footnote (on a corpus of x, over x period of time, etc.) 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.
- himal 13y agoHint: You can enter any email address you want.you don't have to validate it.(well, at least for now)
- mdemare 13y agoWhy is this better than the Google or Bing translate APIs, which also offer language detection?
- donutdan4114 13y ago"test it out" comes back as french...
- oedj 13y agoMaybe you've fallen in the 1% error rate ?
- afsina 13y agoLanguage guessing is rather hard when few letters are used especially if you use statistical methods. I think after 20 something letters you enter >%95 accuracy zone. In a simple library I wrote ( https://github.com/ahmetaa/zemberek-nlp/tree/master/lang-id https://github.com/ahmetaa/zemberek-nlp/tree/master/lang-id Works for 60 languages but no docs yet) , for Turkish and English test results are: 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.
- RBerenguel 13y agoSome day I have to rewrite whatlanguageis.com (currently not working) with all the great ideas I had to improve it...
- mdemare 13y agoHmm, it takes 5+ seconds to get a response, and it chokes on the same test phrase as Google, thinking "Ik hou van vette lettertypes." is Norwegian...
- ma2rten 13y agoIt's probably overloaded because it's on hackernews and is based on the same features (character n-grams) as Google Translate. Your text is simply too short for character n-grams to be 100% reliable.
- redox_ 13y agoYou should also consider full-non-ambiguous words before trying with trigrams. "marché" is only available in French, whereas "mar", "arc", ... are available in lots of languages. This should drastically improve your results.
- redox_ 13y agoStore only the top N common non-ambiguous words if the RAM consumption matters ;)
- danieldk 13y agoOr store the lexicon in a determinisitic acyclic finite state automaton. E.g. (shameless plug): https://github.com/danieldk/dictomaton 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.
- danieldk 13y agoAlso, for those who would like to know how you can implement a language guesser (sources + link to paper): http://www.let.rug.nl/vannoord/TextCat/ http://www.let.rug.nl/vannoord/TextCat/ Python version: http://thomas.mangin.com/data/source/ngram.py 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.
- davidjgraph 13y agoI'll ask plainly what others are hinting at : Is this actually your own built service, or are you a proxy for something like Google Translate API[1]? 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-language https://developers.google.com/translate/v2/using_rest#detect...
- phpnode 13y agohow does this compare in accuracy to chromium's Compact Language Detector? https://code.google.com/p/chromium-compact-language-detector/ https://code.google.com/p/chromium-compact-language-detector... https://github.com/mzsanford/cld https://github.com/mzsanford/cld
- himal 13y agoYou guys might want to handle GET requests for /try URL(https://getlang.io/try https://getlang.io/try) as well.currently it's returning "Server Error (500)" for GET requests.
- alexott 13y agoApache Tika (http://tika.apache.org/ http://tika.apache.org/) also has language detector, although it maybe not so good as CLD...
- razvvan 13y agoIf I were to implement this I'd rather use google's prediction api. At least with that you get a bit of control over what goes into the training data.
- alexott 13y agoAnd it looks like that they are using the following library: http://code.google.com/p/language-detection/ http://code.google.com/p/language-detection/ - at least the number & list of languages is very similar :-)
- ma2rten 13y agoor just the same training data...
- beering 13y agoAlternatively, people can just download langid.py[1] and do language detection locally. This is not a particularly hard problem - I think it's doable by undergrad ML or NLP classes. The tricky parts are usually political - are users going to be angry if you confuse Indonesian with Malaysian, or so on? [1] https://github.com/saffsd/langid.py https://github.com/saffsd/langid.py
- danieldk 13y agoI think it's doable by undergrad ML or NLP classes. 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.
- ma2rten 13y agoIt's easy to write a language guesser, but's not easy to write a good one. Even Google Translate is not prefect (see below).
- Radim 13y agoGreat point. Often overlooked by people who only know what I call "drive-by machine learning" (finished an online ML course or something). 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 https://news.ycombinator.com/item?id=3466927 Spoiler: it still is, except for "drive-by ML apps".
- ssiddharth 13y agoIt might be mild OCD but it'd be great if the list of supported languages is ordered in some logical way.
- efeamadasun 13y agoI don't know why I can't stand this sentence "A simple and scalable way to classify automatically text by language". "Classify" and "automatically" need to switch places.
- bkamapantula 13y agoIt's Telugu not Teligu. By Panjabi, do you mean Punjabi? As others already mentioned, it would be good to have users try examples before signup.
- chrismorgan 13y agoThe design is fine, but the language used on the page itself isn't quite right. 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).
- jhull 13y agoI wonder how this performs on short text posts like tweets. At my last gig where we did social media text analysis we used a few different packages (chromium, guess-language, and our own ngram classifier) and still had pretty low accuracy for tweets.
- AznHisoka 13y agoHave you look at the metadata returned by a tweet? They also returned language, as well as location of the tweeter, which gives you some clues.
- m4tthumphrey 13y agocurl -XPOST -d 'hello' 'https://getlang.io/get?token=...' https://getlang.io/get?token=...' { "code": "fi", "name": "suomi, suomen kieli", "name_en": "Finnish" } O_O
- martingordon 13y agoMatthew Kirk spoke about a neural network language predictor at RubyConf a few weeks ago. Here are his slides and code: http://modulus7.com/rubyconf/ http://modulus7.com/rubyconf/
- web64 13y agoI've used detectlanguage.com[1] in the past, which seems like a very similar service to getlang.io. With both of them it is hard to know what is behind the scenes... [1] http://detectlanguage.com/ http://detectlanguage.com/
- deleted 13y ago[deleted]
- ismaelc 13y agoWhere's the login page? I need to get my token