CKIP.py

CKIP.py is a simple interface for the services provided by CKIP.

Usage

CKIP.py provides two classes, CKIPSegmenter and CKIPParser, to access the Chinese segmenter and the Chinese parser, respectively.

To create an instance of these classes, you must import the CKIPSegmenter class and/or the CKIPParser class from the ckip module, and then pass your username and password to the constructor:

from ckip import CKIPSegmenter, CKIPParser

segmenter = CKIPSegmenter('YOUR USERNAME', 'YOUR PASSWORD')
parser = CKIPParser('YOUR USERNAME', 'YOUR PASSWORD')

Then, you can use the process() method to process the given string:

segmented_result = segmenter.process('這是一隻可愛的小花貓')

or

parsed_result = parser.process('這是一隻可愛的小花貓')

This method returns a dictionary of the processed result:

{
    'status': 'Success',
    'status_code': '0',
    'result':
        [
            [
                {'term': u'這', 'pos': u'DET'},
                {'term': u'是', 'pos': u'Vt'},
                {'term': u'一', 'pos': u'DET'},
                {'term': u'隻', 'pos': u'M'},
                {'term': u'可愛', 'pos': u'Vi'},
                {'term': u'的', 'pos': u'T'},
                {'term': u'小', 'pos': u'Vi'},
                {'term': u'花貓', 'pos': u'N'}
            ]
        ]
}

The status and the status_code indicate whether the process is success or not:

if segmented_result['status_code'] != '0':
    print('Process Failed: ' + segmented_result['status'])

And the result is a list of objects that represent each sentence.

Takes the result of the CKIPSegmenter.process() for example, the sentence is represented by a list of dictionary. Each dictionary contains the Chinese term and the corresponding part-of-speech:

for sentence in segmented_result['result']:
    for term in sentence:
        print(term['term'], term['pos'])

The sentence in the result of the CKIPParser.process(), on the other hand, is represented by a parsing tree:

{
    'punctuation': None,
    'tree':
        {
            'head': {'term': u'是', 'pos': u'Vt'},
            'pos': u'S',
            'child':
                [
                    {
                        'head': {'term': u'這', 'pos': u'DET'},
                        'pos': u'NP',
                        'child':
                            [
                                {'term': u'這', 'pos': u'DET'}
                            ]
                    },
                    {'term': u'是', 'pos': u'Vt'},
                    {
                        'head': {'term': u'花貓', 'pos': u'N'},
                        'pos': u'NP',
                        'child':
                             [
                                 {'term': u'一隻', 'pos': u'DM'},
                                 {
                                     'head': {'term': u'的', 'pos': u'T'},
                                     'pos': u'V‧的',
                                     'child':
                                         [
                                             {'term': u'可愛', 'pos': u'Vi'},
                                             {'term': u'的', 'pos': u'T'}
                                         ]
                                 },
                                 {'term': u'小', 'pos': u'Vi'},
                                 {'term': u'花貓', 'pos': u'N'}
                             ]
                    }
                ]
        }
}

The punctuation is a dictionary like {'term': u'。', 'pos': u'PERIODCATEGORY'}, which represents the symbol that used to separate from next sentence, or None if there was no punctuation in this sentence.

tree is a dictionary that represent the tree structure. Each node has its own part-of-speech, and its children nodes (if this node is an internal node) or term (if this node is a leaf node).

Here is a simple example for traversing all leaf nodes (each of these is a Chinese term) of the parsing tree:

def traverse(root):
    if 'child' in root:
        for child in root['child']:
            for leaf in traverse(child):
                yield leaf
    else:
        yield root

for sentence in parsed_result['result']:
    for term in traverse(sentence['tree']):
        print(term['term'], term['pos'])

License

Copyright (c) 2012-2014, Chi-En Wu.

Distributed under The BSD 3-Clause License.