Py-CKIP is a python API for CKIP Chinese Parser and Chinese Segmentator. The parser analyses the internal structure of Chinese words using CKIP services. CKIP(Chinese Knowledge and Information Processing Group) was invented by Taiwan Academia Sinica. In general, we can call through web services or crawl their websites to use. I found many implementations on the Internet, so I referred to the internet for implementations. There are two usages to provide. Please read the following instructions carefully.
Tested on
- Ubuntu 16.04.2 LTS Xenial Xerus (Python 2.7.12)
Tree:
├── __init__.py
├── PyCCP.py
├── PyCKIP.py
└── PyCSS.py-
PyCKIP.py is a simple interface for CKIP services. It provides two classes,
CKIPSegmenterandCKIPParser, to access the Chinese Segmenter and the Chinese Parser, respectively. -
PyCCS.py is a independent Python API for CKIP Chinese Segmentator. Because CKIP provides a online Chinese Segmentation tool, we can write a script that uses online tool to implement Chinese Segmentator using CKIP services.
-
PyCCP.py is a independent Python API for CKIP Chinese Parser. CKIP provides a online Chinese Parser tool. Because CKIP provides a online Chinese Parsation tool, we can write a script that uses online tool to implement Chinese Parser by CKIP services.
pip install lxml 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 PyCKIP import *
segmenter = PyCKIP.CKIPSegmenter('YOUR USERNAME', 'YOUR PASSWORD')
parser = PyCKIP.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'])###PyCCS:
>>> from PyCCS import seg
>>> result = seg('台灣大學語言學研究所')
>>> print result.text()
台灣/Nc 大學/Nc 語言學/Na 研究所/Nc
>>> result.raw
[(u'\u53f0\u7063', u'Nc'),
(u'\u5927\u5b78', u'Nc'),
(u'\u8a9e\u8a00\u5b78', u'Na'),
(u'\u7814\u7a76\u6240', u'Nc')]All html-like tags will be segmentated normally. At CKIP Online Demo, if you input strings containing html-like tag, the results might be weird.
For example:
>>> print seg('<h1>這是html tag</h1>').text()
<h1>/FW 這/Nep 是/SHI html/FW tag</h1>/FW Limitations: Input encoding should can only be CP950/BIG5. If words contains "堃" or "瑠", it will not be segmentated.
###PyCCP:
>>> from PyCCP import parseTree
>>> res = parseTree('蟹老闆好帥氣。')
>>> print res[0].encode('utf-8').decode('utf8')
1.[0] S(theme:NP(property:Nab:蟹|Head:Nab:老闆)|degree:Dfa:好|Head:VH11:帥氣)#。(PERIODCATEGORY)This software is licensed under the [MIT license](_MIT license: http://en.wikipedia.org/wiki/MIT_License).
MIT License
Copyright (c) 2016 Carson Wang <kiki86151@hotmail.com>
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SOFTWARE.
© 2016 Carson Wang.