@hackage nanq3.0.1

Performs 漢字検定 (Japan Kanji Aptitude Test) level analysis on given Kanji.

NANQ

Author: Colin Woodbury colingw@gmail.com

NanQ is a Japanese Kanji analysation program written in Haskell. Its main function is to tell what Kanji belong to what Level of the Japanese National Kanji Examination (漢字検定).

NanQ can be used to:

  • determine what Level individual Kanji belong to
  • determine the average Level (difficulty, in other words) of a group of Kanji
  • apply the above to whole files of Japanese

INSTALLING NANQ

First, get the source files from:

https://github.com/fosskers/nanq

NanQ is written in Haskell and uses the stack tool. Once stack is installed, move to the source directory and perform:

stack build
stack install

USAGE

Assuming you've made it so that you can run the executable, the following command-line options are available:

ANALYSIS OPTIONS

Short Long Description
-a --average Given Japanese input, finds the average Level of all Kanji present
-u --unknowns Reports Kanji whose Level could not be determined
-h --help Prints a message explaining these options
-l --leveldist Find the % distribution of Levels in given Japanese
-s --splits Show what Level each Kanji belongs to
-d --density Determines how much of the input is made up of Kanji
-e --elementary Determines how much of the input is made up of Kanji learned in Japanese Elementary School

INPUT SOURCE OPTIONS

Short Long Description
none none Analyse a String of Japanese given from the command line
-f --file Get input from a given file

NOTES ON CLOs

  • All options above can be mixed to include their analysis result in the output JSON.
  • -h will over-ride any other options or arguments, discarding them and printing a help message.

Examples

Single Kanji

$> nanq -s 日
{
  "levelSplit": {
    "Ten": "日"
  }
}

A Japanese sentence

$> nanq -s これは日本語
{
    "levelSplit": {
      "Nine": "語",
      "Ten": "本日"
    }
}

All options

$> nanq -leadus これは日本語
{
    "levelSplit": {
      "Nine": "語",
      "Ten": "本日"
    },
    "elementary": 1,
    "average": 9.666667,
    "density": 0.5,
    "unknowns": "",
    "distributions": {
      "Nine": 0.33333334,
      "Ten": 0.6666667
    }
}