A text analysis toolkit KyTea binding

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License: MIT License (MIT license)

Author: Aki Ariga

Tags KyTea, NLP, Japanese morphological analyzer

Classifiers

Project description

KyTea wrapper for python

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Mykytea-python is a python wrapper module for KyTea, a general text analysis toolkit. KyTea is developed by KyTea Development Team.

Detailed information of KyTea can be found at http://www.phontron.com/kytea

Install Dependencies

You need to install KyTea before build.

To install Mykytea-python, run

pip install kytea

To build Mykytea-python, run (if you don't want to use pip)

make

If you want to install, run

sudo make install

If you fail to make, please try to install SWIG and run

swig -c++ -python -I/usr/local/include mykytea.i

Or if you still fail on Max OS X, run with some variables

$ ARCHFLAGS="-arch x86_64" CC=gcc CXX=g++ make

If you compiled kytea with clang, you need ARCHFLAGS only.

Or, you can use brew to install kytea.

brew install kytea
KYTEA_DIR=$(brew --prefix) make all

How to use it?

Here is the example code to use Mykytea-python.

import Mykytea

def showTags(t):
    for word in t:
        out = word.surface + "\t"
        for t1 in word.tag:
            for t2 in t1:
                for t3 in t2:
                    out = out + "/" + str(t3)
                out += "\t"
            out += "\t"
        print(out)

def list_tags(t):
    def convert(t2):
        return (t2[0], type(t2[1]))
    return [(word.surface, [[convert(t2) for t2 in t1] for t1 in word.tag]) for word in t]

# Pass arguments for KyTea as the following:
opt = "-model /usr/local/share/kytea/model.bin"
mk = Mykytea.Mykytea(opt)

s = "今日はいい天気です。"

# Fetch segmented words
for word in mk.getWS(s):
    print(word)

# Show analysis results
print(mk.getTagsToString(s))

# Fetch first best tag
t = mk.getTags(s)
showTags(t)

# Show all tags
tt = mk.getAllTags(s)
showTags(tt)

License

MIT License

Project details

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Meta

License: MIT License (MIT license)

Author: Aki Ariga

Tags KyTea, NLP, Japanese morphological analyzer

Classifiers

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0.1.9

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