I am using Python 3.1, but I can downgrade if needed.
I have an ASCII file containing a short story written in one of the languages the alphabet of which can be represented with upper and or lower ASCII. I wish to:
1) Detect an encoding to the best of my abilities, get some sort of confidence metric (would vary depending on the length of the file, right?)
2) Automatically translate the whole thing using some free online service or a library.
Additional question: What if the text is written in a language where it takes 2 or more bytes to represent one letter and the byte order mark is not there to help me?
Finally, how do I deal with punctuation and misc characters such as space? It will occur more frequently than some letters, right? How about the fact that punctuation and characters can be sometimes mixed – there might be two representations of a comma, two representations for what looks like an “a”, etc.?
Yes, I have read the article by Joel Spolsky on Unicode. Please help me with at least some of these items.
Thank you!
P.S. This is not a homework, but it is for self-educational purposes. I prefer using a letter frequency library that is open-source and readable as opposed to the one that is closed, efficient, but gets the job done well.
Essentially there are three main tasks to implement the described application:
For 1a, you may want to take a look at decodeh.py, aside from the script itself, it provides many very useful resources regarding character sets and encoding at large. CharDet, mentioned in other answer also seems to be worthy of consideration.
Once the character encoding is known, as you suggest, you may solve 1b) by calculating the character frequency profile of the text, and matching it with known frequencies. While simple, this approach typically provide a decent precision ratio, although it may be weak on shorter texts and also on texts which follow particular patterns; for example a text in French with many references to units in the metric system will have an unusually high proportion of the letters M, K and C.
A complementary and very similar approach, use bi-grams (sequences of two letters) and tri-grams (three letters) and the corresponding tables of frequency distribution references in various languages.
Other language detection methods involve tokenizing the text, i.e. considering the words within the text. NLP resources include tables with the most used words in various languages. Such words are typically articles, possessive adjectives, adverbs and the like.
An alternative solution to the language detection is to rely on the online translation service to figure this out for us. What is important is to supply the translation service with text in a character encoding it understands, providing it the language may be superfluous.
Finally, as many practical NLP applications, you may decide to implement multiple solutions. By using a strategy design pattern, one can apply several filters/classifiers/steps in a particular order, and exit this logic at different points depending on the situation. For example, if a simple character/bigram frequency matches the text to English (with a small deviation), one may just stop there. Otherwise, if the guessed language is French or German, perform another test, etc. etc.