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1. jll29 · 1790067648 · focus · HN ↗

   Yes: you can classify a test file by topic with gzip as follows:

     gzip -9 sports.txt   testfile.txt
   
     gzip -9 politics.txt testfile.txt
   
     gzip -9 business.txt testfile.txt
   

   (ass. sports.txt politics.txt and business.txt are text docs pertaining from the sports, politics and business domains, respectively, and have equal size)

   The test file belongs to the topic with the smallest size *.gz file.

   Witten's group at Waikato uni were perhaps the first to work on this.

   Also check out the Hutter prize if you are interested in this.

   1. stingraycharles · 1790073336 · focus · HN ↗

      Back in the day - maybe two decades ago - I implemented language detection like this.

      I seeded gzip compressors’ dictionaries with Wikipedia articles in different languages.

      I would then try to use said dictionaries on any random text, and the one that was best able to compress it, was the correct language.

      Absolutely totally not the best approach, but very fast and super simple to implement.

      1. ape4 · 1790085725 · focus · HN ↗

         Or maybe make a list of the most used 1000 words in each language. And see which list has the most occurrences.

         1. wongarsu · 1790087162 · focus · HN ↗

            That requires you to decide what a "word" is, which is not trivial (if you think that ignoring punctuation gets you to a clean "letters surrounded by spaces" you will get lots of issues with various Asian languages)

            Also some languages have a lot of prefixes and suffixes on their verbs or even nouns, which dilutes your list of 1000 words by just adding the same common words over and over again with different suffixes designating grammatical tense, grammatical gender, etc.

            The gzip version sounds more general and more obviously correct

            1. kragen · 1790088619 · focus · HN ↗

               You can just use 2-grams of Unicode code points.

               1. npb · 1790214362 · focus · HN ↗

                  Very much agree. Character bigrams will solve this perfectly with tiny amounts of training data (like a couple KB).

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