Containers¶
Containers Prototypes¶
All the container objects can be convert from/to other formats:
from_text(),to_text()for plain-text conversions;from_list(),to_list()for list-like python object conversions;from_dict(),to_dict()for dictionary-like python object (key-value mappings) conversions;from_json(),to_json()for JSON format conversions (based-on dictionary-like format conversions).
Here are the interfaces, where CONTAINER_CLASS refers to the container class.
obj = CONTAINER_CLASS.from_text(plain_text)
plain_text = obj.to_text()
obj = CONTAINER_CLASS.from_list([ value1, value2 ])
list_obj = obj.to_list()
obj = CONTAINER_CLASS.from_dict({ key: value })
dict_obj = obj.to_dict()
obj = CONTAINER_CLASS.from_json(json_str)
json_str = obj.to_json()
Note that not all container provide all above conversions. Here is the table of implemented methods. Please refer the documentation of each container for format details.
Container |
Item |
from/to text |
from/to list, dict, json |
|---|---|---|---|
|
✔ |
✔ |
|
|
✔ |
✔ |
|
✔ |
✔ |
||
✘ |
✔ |
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✘ |
✔ |
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✘ |
✔ |
||
only to |
✔ |
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only to |
✔ |
||
only to |
✔ |
||
only to |
✔ |
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only to |
✔ |
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only to |
✔ |
WS with POS¶
There are also conversion routines for word-segmentation and part-of-speech containers jointly. For example, WsPosToken provides routines for a word (str) with POS-tag (str):
ws_obj, pos_obj = WsPosToken.from_text('中文字(Na)')
plain_text = WsPosToken.to_text(ws_obj, pos_obj)
ws_obj, pos_obj = WsPosToken.from_list([ '中文字', 'Na' ])
list_obj = WsPosToken.to_list(ws_obj, pos_obj)
ws_obj, pos_obj = WsPosToken.from_dict({ 'word': '中文字', 'pos': 'Na', })
dict_obj = WsPosToken.to_dict(ws_obj, pos_obj)
ws_obj, pos_obj = WsPosToken.from_json(json_str)
json_str = WsPosToken.to_json(ws_obj, pos_obj)
Similarly, WsPosSentence/WsPosParagraph provides routines for word-segmented and POS sentence/paragraph (SegSentence/SegParagraph) respectively.
Parse Tree¶
In addition to ParseClause, there are also tree utilities base on TreeLib.
ParseTree is the tree structure of a parse clause. One may use from_text() and to_text() for plain-text conversion; from_dict(), to_dict() for dictionary-like object conversion; and also from_json(), to_json() for JSON string conversion.
ParseTree also provide from_penn() and to_penn() methods for Penn Treebank conversion. One may use to_penn() together with SvgLing to generate SVG tree graphs.
ParseTree is a TreeLib tree with ParseNode as its nodes. The data of these nodes is stored in a ParseNodeData (accessed by node.data), which is a tuple of role (semantic role), pos (part-of-speech tagging), word.
ParseTree provides useful methods: get_heads() finds the head words of the clause; get_relations() extracts all relations in the clause; get_subjects() returns the subjects of the clause.
from ckipnlp.container import ParseClause, ParseTree
# 我的早餐、午餐和晚餐都在那場比賽中被吃掉了
clause = ParseClause('S(goal:NP(possessor:N‧的(head:Nhaa:我|Head:DE:的)|Head:Nab(DUMMY1:Nab(DUMMY1:Nab:早餐|Head:Caa:、|DUMMY2:Naa:午餐)|Head:Caa:和|DUMMY2:Nab:晚餐))|quantity:Dab:都|condition:PP(Head:P21:在|DUMMY:GP(DUMMY:NP(Head:Nac:比賽)|Head:Ng:中))|agent:PP(Head:P02:被)|Head:VC31:吃掉|aspect:Di:了)')
tree = clause.to_tree()
print('Show Tree')
tree.show()
print('Get Heads of {}'.format(tree[5]))
print('-- Semantic --')
for head in tree.get_heads(5, semantic=True): print(repr(head))
print('-- Syntactic --')
for head in tree.get_heads(5, semantic=False): print(repr(head))
print()
print('Get Relations of {}'.format(tree[0]))
print('-- Semantic --')
for rel in tree.get_relations(0, semantic=True): print(repr(rel))
print('-- Syntactic --')
for rel in tree.get_relations(0, semantic=False): print(repr(rel))
print()
# 我和食物真的都很不開心
tree_text = 'S(theme:NP(DUMMY1:NP(Head:Nhaa:我)|Head:Caa:和|DUMMY2:NP(Head:Naa:食物))|evaluation:Dbb:真的|quantity:Dab:都|degree:Dfa:很|negation:Dc:不|Head:VH21:開心)'
tree = ParseTree.from_text(tree_text)
print('Show Tree')
tree.show()
print('Get get_subjects of {}'.format(tree[0]))
print('-- Semantic --')
for subject in tree.get_subjects(0, semantic=True): print(repr(subject))
print('-- Syntactic --')
for subject in tree.get_subjects(0, semantic=False): print(repr(subject))
print()