def addNonTerminal(nodes,node):
name=str(node.get('Cat')) + ('_'+ str(node.get('Rule')) if node.get('Rule')!=None else '') + ('_' + str(node.get('ClType')) if node.get('ClType')!=None else '')
nodeNumber = whichNode(name,nodes)
if nodeNumber == -1:
nodes.append(NonTerminal(name))
children = []
for child in node.getchildren():
if 'Unicode' in child.keys():
features = FeatStruct()
for key in child.keys():
if key in ['Person','Tense','Voice','Mood','Case','Number','Gender','Degree']:
features = features.unify(FeatStruct('['+str(key)+'=\''+str(child.get(key))+'\']'))
children.append('{' + str(child.get('Cat')) + '_' + stringFeatures(features) + '}')
else:
children.append(str(child.get('Cat')) + ('_'+ str(child.get('Rule')) if child.get('Rule')!=None else '') + ('_' + str(child.get('ClType')) if child.get('ClType')!=None else ''))
nodes[len(nodes)-1].addPath(children,nodes)
else:
children = []
for child in node.getchildren():
if 'Unicode' in child.keys():
features = FeatStruct()
for key in child.keys():
if key in ['Person','Tense','Voice','Mood','Case','Number','Gender','Degree']:
features = features.unify(FeatStruct('['+str(key)+'=\''+str(child.get(key))+'\']'))
children.append('{' + str(child.get('Cat')) + '_' + stringFeatures(features) + '}')
else:
children.append(str(child.get('Cat')) + ('_'+ str(child.get('Rule')) if child.get('Rule')!=None else '') + ('_' + str(child.get('ClType')) if child.get('ClType')!=None else ''))
nodes[nodeNumber].addPath(children,nodes)
def get_instance_featstructs(root):
instances = get_instances(root)
events = get_events(root)
for i in instances:
try:
i.set(
'event',
next(
FeatStruct({
**{k: e.attrib[k]
for k in ['class', 'stem']},
**{
'text': e.text
}
}) for e in events if e.get('eid') == i.get('eventID')))
except KeyError:
i.set(
'event',
next(
FeatStruct({
**{k: e.attrib[k]
for k in ['class']},
**{
'text': e.text
}
}) for e in events if e.get('eid') == i.get('eventID')))
return [
FeatStruct({k: i.attrib[k]
for k in set(i.keys()) - set(['eventID'])})
for i in instances
]
def smartUnify(*featstructs):
'''Unifies two or more feature structures based on what they have
in common. For example, [person=1,number=2] and [case=3,number=2]
will return [number=2], not [person=1,number=2,case=3] as
featStruct.unify() would do. Return None if unable to unify.
Arguments:
*featstructs -- Any number of feature structures to unify.
'''
# Create a list of lists of feature structures in each overall
# structure.
lis = []
for struct in featstructs:
lis.append([])
for item in struct:
lis[len(lis)-1].append(item)
# Create a set based on the first structure list, and then form
# the intersection of the remaining lists, leaving only what is
# left in common between all lists.
s = set(lis[0])
f = FeatStruct()
for l in lis:
s = s.intersection(l)
# Unify the set together to form the resulting FeatStruct
for struct in featstructs:
for i in struct:
if i in s:
f = f.unify(FeatStruct('['+str(i)+'='+str(struct[i])+']'))
if f == None:
return None
return f
def debug_test_contain():
fs100 = FeatStruct()
fs100['__or_wzq'] = 'wzq'
#fs100['__or_qwe'] = 'qwe'
fs101 = FeatStruct()
fs101['__or_123'] = '123'
fs101['__or_wzq'] = 'wzq'
print test_contain(fs100, fs101)
def addTerminal(nodes,node):
features = FeatStruct()
for key in node.keys():
if key in ['Person','Tense','Voice','Mood','Case','Number','Gender','Degree']:
features = features.unify(FeatStruct('['+str(key)+'=\''+str(node.get(key))+'\']'))
name = '{' + str(node.get('Cat')) + '_' + stringFeatures(features) + '}'
nodeNumber = whichNode(name,nodes)
if nodeNumber == -1:
nodes.append(Terminal(name[1:name.find('_')],features))
def match_feature(feature, regexp, operation=0):
"""
match_feature(feature,regexp,operation=0) -> FeatStruct
This function is used to filter a feature structure with a regular exression.
The regular expression should be written in the form that XTAG system uses,
which has a '__value__' entry at the last level of indexing.
feature: The feature that you would like to filter
regexp: An acceptable regular expression by module re
operation: 0 if positive filtering will be done, 1 if negative filtering.
positive filtering means that all RHS values that match the regexp will be
retained, while negative filtering means that all RHS values that doesn't
match will be retained.
"""
new_feature = FeatStruct()
count = 0
for i in feature.keys():
val = feature[i]
if val.has_key('__value__'):
#search_ret = re.search(regexp,val['__value__'])
search_ret = re.search(regexp, i)
if operation == 0 and search_ret != None:
new_feature[i] = val
count += 1
elif operation == 1 and search_ret == None:
new_feature[i] = val
count += 1
else:
search_ret = re.search(regexp, i)
if operation == 0 and search_ret != None:
new_feature[i] = val
count += 1
elif operation == 1 and search_ret == None:
ret = match_feature(val, regexp, operation)
if ret != None:
new_feature[i] = ret
else:
new_feature[i] = FeatStruct()
count += 1
elif operation == 1 and search_ret != None:
pass
else:
ret = match_feature(val, regexp, operation)
#print ret,'\n'
if ret != None:
new_feature[i] = ret
count += 1
#print new_feature,'\n'
if count == 0:
return None
else:
return new_feature
def topic_features(self, article):
word_occurence = self.get_word_occurence(article)
feature_list = FeatStruct(word_occurence)
feature_list.freeze()
feature = FeatStruct(word_occurence=feature_list)
feature.freeze()
return dict([(word, True) for word in word_occurence])
def make_fs(lhs,rhs):
# This function makes a feature structure using a list of lhs which are nested
# e.g. if lhs = ['a','b','c','d'] and rhs = 'wzq' then the
# fs shoule be [a = [b = [c = [d = 'wzq']]]]
new_fs = FeatStruct()
if len(lhs) == 1:
inner = FeatStruct()
inner['__value__'] = rhs
rhs = inner
new_fs[lhs[0]] = rhs
else:
new_fs[lhs[0]] = make_fs(lhs[1:],rhs)
return new_fs
def nested_frozen_fs(dictionary):
if not isinstance(dictionary, FeatStruct):
ret = FeatStruct()
for k, v in dictionary.items():
v_new = v
if isinstance(v_new, set):
v_new = frozenset(v_new)
elif isinstance(v_new, dict):
v_new = nested_frozen_fs(v_new)
ret[k] = v_new
ret.freeze()
return ret
else:
dictionary.freeze()
return dictionary
def make_rhs_using_or(rhs):
"""
:param rhs: The right hand string which may contain the 'or' relationship
:type rhs: str
:return: A feature structure using '__or_' structure
:rtype: FeatStruct
This function will return a feature structure which satisfies the
requirement for implementing the 'or' relationship in the xtag grammar.
rhs must be a string, whose value will be used to construct the lhs
inside the new feature structure.
For example,
rhs = a/b/c -> [ __or_a = a ]
[ __or_b = b ]
[ __or_c = c ]
"""
new_fs = FeatStruct()
slash = rhs.find('/')
if slash == -1:
rhs = [rhs]
else:
rhs = rhs.split('/')
# After this rhs is a list containing the entities in the 'or' relation
for i in rhs:
lhs = make_leaf_str(i)
new_fs[lhs] = i
return new_fs
def fourth_pass(xtag_trees):
"""
fourth_pass() -> list
Given the result of third_pass(), this function will make
use of FeatStruct, and build a feature structure dictionary.
"""
for xtag_entry in xtag_trees:
features = {}
for feature_entry in xtag_entry[1]:
lhs = feature_entry[0]
rhs = feature_entry[1]
l_separator = lhs.find(':')
r_separator = rhs.find(':')
if r_separator == -1:
l_id = lhs[:l_separator]
l_space = lhs.find(' ')
feat_rhs = FeatStruct()
feat_rhs["__value__"] = rhs
#feat_rhs = rhs
if (l_space == -1):
l_feature = lhs[l_separator + 2:-1]
add_two_feature(features, l_id, feat_rhs, l_feature)
else:
l_feature1 = lhs[l_separator + 2:l_space]
l_feature2 = lhs[l_space + 1:-1]
add_two_feature(features, l_id, feat_rhs, l_feature1,
l_feature2)
xtag_entry[4] = features
return xtag_trees
def _naive_unify(fstruct1:FeatStruct, fstruct2:FeatStruct):
newfs = copy.copy(fstruct1)
if _is_mapping(fstruct1) and _is_mapping(fstruct2):
# Unify any values that are defined in both fstruct1 and
# fstruct2. Copy any values that are defined in fstruct2 but
# not in fstruct1 to fstruct1. Note: sorting fstruct2's
# features isn't actually necessary; but we do it to give
# deterministic behavior, e.g. for tracing.
for fname, fval2 in sorted(fstruct2.items()):
if fname in fstruct1:
newfs[fname] = _naive_unify(fstruct1[fname], fval2)
else:
newfs[fname] = fval2
return newfs # Contains the unified value.
# Unifying two sequences:
elif _is_sequence(fstruct1) and _is_sequence(fstruct2):
# Concatenate the values !!
# Don't unify corresponding values in fstruct1 and fstruct2.
newfs += fstruct2
newfs = tuple([t for t in newfs if not isinstance(t, Variable)])
return newfs # Contains the unified value.
else:
return None
def remove_or_tag(feature):
"""
:param feature: The feature structure that you want to remove the '__or_' tag
:type feature: FeatStruct
:return: A new feature structure with "__or_" removed and combined
:rtype: FeatStruct
Given a feature structure in the internal repersentation of our xtag system
(i.e. each leaf is wrapped with an '__or_' + lhs feature struct), this function
will get rid of the __or_ tag, and produce a feature structure where no __or_
is there, and the multiple or relation is represented as [__or_1]/[__or_2]/ ...
e.g. for fs = [apple = [__or_a = 'a']]
[ [__or_b = 'b']]
[ [__or_c = 'c']]
remove_or_tag(fs) will return:
fs_return = [apple = 'a/b/c']
"""
new_feature = FeatStruct()
for key in feature.keys():
entry = feature[key]
entry_keys = entry.keys()
if test_leaf(entry) == True:
str_or_removed = entry[entry_keys[0]]
if len(entry_keys) > 1:
for i in entry_keys[1:]:
str_or_removed += '/' + entry[i]
new_feature[key] = str_or_removed
else:
new_feature[key] = remove_or_tag(feature[key])
return new_feature
def find_probability(list1,list2,targword):
c=0
prob_list=[]
sense_word_list=[]
for i in range(len(list1)):
for j in range(len(list1[i])):
for k in range(len(list1[i][j])):
#print(type(list1[i][j][k]),type(list2[0][0][k]))
if(list1[i][j][k]==list2[0][0][k]):
c+=1
prob=c/(5*len(list1)*len(list1[i]))
prob_list.append(prob)
max_prob=max(prob_list)
sensetag=prob_list.index(max_prob)
with codecs.open(targword + '/Senses00' + str(sensetag) + '.txt', encoding='utf-16') as f:
sense_word_list.append(f.read())
print("\n")
print(sense_word_list)
#print(sense_word_list[sense_word_list.index('&'):sense_word_list.index('!') ])
try:
LinearSVC_classifier = SklearnClassifier(LinearSVC())
LinearSVC_classifier.train(list1)
print("LinearSVC_classifier accuracy percent:", (nltk.classify.accuracy(LinearSVC_classifier, list2)) * 100)
except:
#classifier=nltk.NaiveBayesClassifier.train(FeatStruct('["a","b" , "c"]'))
print("Classifier accuracy percent :", "{0:.3f}".format(value((FeatStruct('[1,2,3]')))))
def remove_value_tag(feature):
new_feature = FeatStruct()
for i in feature.keys():
if feature[i].has_key('__value__'):
new_feature[i] = feature[i]['__value__']
else:
new_feature[i] = remove_value_tag(feature[i])
return new_feature
def test_contain(fs1, fs2):
"""
Test if one feature structure contains another, i.e. is the super set of another.
:param fs1: The first feature structure you want to test
:type fs1: FeatStruct
:param fs2: The second feature structure you wang to test
:type fs2: FeatStruct
:return: 0 if they are equal to each other
1 if fs1 is a subset of fs2
-1 if fs2 is a subset of fs1
FeatStruct if there is some intersection
None if there is no intersection
One exception is that, if the two fs are both empth then we will return
equal instead of None.
:rtype: integer/FeatStruct/None
This function requires that fs1 and fs2 are leaf nodes, if they are not then an
an exception will be raised. Besides, since in a leaf node the left hand side
is actually derivable from the right hang side, so if we know one we can know
another. Based on this observation we just make comparisions to the left hand
side, i.e. keys().
"""
if test_leaf(fs1) == False or test_leaf(fs2) == False:
raise ValueError('Two arguments must be leaf nodes.')
key_1 = fs1.keys()
key_2 = fs2.keys()
new_key_1 = []
new_key_2 = []
for i in key_1:
if i in key_2:
new_key_1.append(i)
for i in key_2:
if i in key_1:
new_key_2.append(i)
len_1 = len(key_1)
len_2 = len(key_2)
new_len_1 = len(new_key_1)
new_len_2 = len(new_key_2)
# Now new_len_1 and new_len_2 are the same keys in both fs, or both []
# means no same keys
#print new_key_1
#print new_key_2
if new_len_1 == 0 and new_len_2 == 0:
return None
elif len_1 != new_len_1 and len_2 != new_len_2:
ret = FeatStruct()
for i in new_key_1:
ret[i] = fs1[i]
return ret
elif len_1 == new_len_1 and len_2 != new_len_2:
return 1 # len_1 not changed, it is contained in len_2
elif len_1 != new_len_1 and len_2 == new_len_2:
return -1 # len_2 contained in len_1
else:
return 0 # Neigher has changed, so they are equal
def add_two_feature(features, l_id, rhs, l_feature1, l_feature2=None):
if l_feature2 == None:
if features.has_key(l_id):
features[l_id][l_feature1] = rhs
else:
features[l_id] = FeatStruct()
features[l_id][l_feature1] = rhs
else:
if features.has_key(l_id):
if features[l_id].has_key(l_feature1):
features[l_id][l_feature1][l_feature2] = rhs
else:
features[l_id][l_feature1] = FeatStruct()
features[l_id][l_feature1][l_feature2] = rhs
else:
features[l_id] = FeatStruct()
features[l_id][l_feature1] = FeatStruct()
features[l_id][l_feature1][l_feature2] = rhs
return
def fifth_pass(xtag_trees):
"""
fifth_pass() -> list
Given the result of fourth_pass(), this function will continue
to build the feature structure, and in this phase we must add all values
even if they are not defined by the tree grammar.
"""
for xtag_entry in xtag_trees:
features = xtag_entry[4]
for feature_entry in xtag_entry[1]:
lhs = feature_entry[0]
rhs = feature_entry[1]
l_separator = lhs.find(':')
r_separator = rhs.find(':')
if r_separator != -1:
l_id = lhs[:l_separator]
r_id = rhs[:r_separator]
r_feature = rhs[r_separator + 2:-1]
print r_feature
l_space = lhs.find(' ')
if not features.has_key(
r_id): # Make sure features[r_id] exists
features[r_id] = FeatStruct()
features[r_id][r_feature] = FeatStruct(__value__='')
elif not features[r_id].has_key(
r_feature
): # Make sure features[r_id][r_feature] exists
features[r_id][r_feature] = FeatStruct(__value__='')
if (l_space == -1):
l_feature = lhs[l_separator + 2:-1]
add_two_feature(features, l_id, features[r_id][r_feature],
l_feature)
else:
l_feature1 = lhs[l_separator + 2:l_space]
l_feature2 = lhs[l_space + 1:-1]
add_two_feature(features, l_id, features[r_id][r_feature],
l_feature1, l_feature2)
return xtag_trees
def create_fact(
pred: FeatStruct, arg_0: Union[List[FeatStruct], FeatStruct], arg_1: Union[List[FeatStruct], FeatStruct]
) -> FeatStruct:
"""
Construct feature structure than represent fact.
ex: know(Gael, [Bas, Justine]), return a feature structure of the type :
[pred=[sem=know],
arg0 = [head=Gael, tail=na],
arg1 = [head=Bas, tail=[head=Justine, tail=na]]]
"""
arg_0 = arg_0 if isinstance(arg_0, list) else [arg_0]
arg_1 = arg_1 if isinstance(arg_1, list) else [arg_1]
return FeatStruct(arg0=format_list(arg_0), arg1=format_list(arg_1), pred=pred)
def parse_feature_in_catalog(s):
# This function parses the string in catalog file, i.e. english.gram
# with the option 'start-feature', into a FeatStruct. We MUST write
# separate parsers for different strings from different files, since
# these features are represented in different forms.
"""
:param s: The string repersenting start feature in the catalog file
:type s: str
:return: A feature structure which is the start feature
:rtype: FeatStruct
Given the string, this function will return a feature structure parsed
from that string. The feature structure should be encoded like this:
<mode> = ind/imp <comp> = nil <wh> = <invlink> <punct term> = per/qmark/excl <punct struct> = nil
All tokens shall be separated by a single space, no comma and period and
semicolon is used. This parses is designed specially for the string from
the catalog (i.e. english.gram) file, since there are multiple ways to
represent the FS in xtag grammar, so we need multiple parsers.
"""
# token _list is a list of tuples, the element of which is the LHS and
# RHS of a feature structure definition, i.e. [('mode','ind/imp'),('comp','nil')]
token_list = []
while True:
equal_sign = s.find('=')
if equal_sign == -1:
break
# find between '=' and '<', which is the RHS if no "<LHS> = <RHS>"
# is used. If it is then we can know that the no-white string between
# '=' and '<' is an empty string.
angle_bracket = s.find('<', equal_sign)
if angle_bracket == -1:
rhs = s[equal_sign + 1:].strip()
else:
rhs = s[equal_sign + 1:angle_bracket].strip()
if rhs == '':
angle_bracket = s.find('<', angle_bracket + 1)
if angle_bracket == -1:
rhs = s[equal_sign + 1:].strip()
else:
rhs = s[equal_sign + 1:angle_bracket].strip()
lhs = s[:equal_sign].strip()[1:-1]
token_list.append((lhs, rhs))
s = s[angle_bracket:]
new_fs = FeatStruct()
for token in token_list:
add_new_fs(new_fs, token[0].split(), token[1])
return new_fs
def add_new_fs(fs, lhs, rhs, ref=0):
"""
:param fs: The feature structure that we are going to add to it.
:type fs: FeatStruct
:param lhs: The path defined for the new node
:type lhs: list(str)
:param rhs: The value of the node
:type rhs: str / Any object
:param ref: Controls whether rhs should be treated as a string or other object
:type ref: 0 / 1
This function will add the feature structure defined by lhs and rhs
into an existing feature fs. The lhs of the lowest level is defined
to be '__or_' + rhs to facilitate other procedures.
If any of the paths defined by lhs has already existed in fs, then
it will be merged into that existing path, instead of erasing the existing
one and make a new one, so it is safe to use this function to merge two
feature structures.
For example,
fs = [a = ['www']]
lhs = ['a','b','c','d','e']
rhs = 'wzq'
->
[a = [['www'] ]
[ [b = [c = [d = [e = [__or_wzq = 'wzq']]]]
"""
if len(lhs) == 1:
#inner = FeatStruct()
#inner['__value__'] = rhs
#fs[lhs[0]] = inner
# ref means reference. If we are not making reference, then rhs must
# be a string, and we will process that string
# But if ref == 1 then we are just making references, so we will not
# process rhs, but only attach it to the existing feature structure
if ref == 0:
fs[lhs[0]] = make_rhs_using_or(rhs)
elif ref == 1:
fs[lhs[0]] = rhs
else:
raise ValueError('Undefined ref value %d' % (ref))
else:
if fs.has_key(lhs[0]):
add_new_fs(fs[lhs[0]], lhs[1:], rhs, ref)
else:
fs[lhs[0]] = FeatStruct()
add_new_fs(fs[lhs[0]], lhs[1:], rhs, ref)
return
def make_fs(lhs, rhs, ref=0):
# This function makes a feature structure using a list of lhs which are nested
# e.g. if lhs = ['a','b','c','d'] and rhs = 'wzq' then the
# fs shoule be [a = [b = [c = [d = 'wzq']]]]
"""
:param lhs: The path on the left hand side
:type lhs: list(str)
:param rhs: The string on the right hand side / Any object
:type rhs: str / object
:param ref: Control whether to treat rhs as a string or as an abitrary object
:type ref: 0 / 1
:return: A constructed feature structure
:rtype: FeatStruct
Given the path and the right hand side of a feature structure this function
will return a feature structure exactly has the path defined in lhs and the
value inside it is the rhs. There are two choices, we can either pass in a
string as the rhs to let the code to deal with the 'or' problem. or just
pass in an object and the code will not touch that (ref = 1 needed).
lhs = ['a','b','c','d']
rhs = 'wzq'
->FeatStruct = [a = [b = [c = [d = [__or_wzq = 'wzq']]]]]
"""
new_fs = FeatStruct()
if len(lhs) == 1:
#inner = FeatStruct()
#inner['__value__'] = rhs
#rhs = inner
# if ref == 0 then we are not making references so we will process
# rhs, and it must be a string
if ref == 0:
rhs = make_rhs_using_or(rhs)
elif ref == 1:
pass # Do nothing
else:
raise ValueError('Undefined ref value %d' % (ref))
new_fs[lhs[0]] = rhs
else:
new_fs[lhs[0]] = make_fs(lhs[1:], rhs, ref) # Recursively call
return new_fs
def debug():
a = FeatStruct()
b = FeatStruct()
c = FeatStruct()
d = FeatStruct()
a['__value__'] = 'OKWANGZiqi'
b['__value__'] = 'WANGYunpeng'
c['__value__'] = "WWA!!!"
d['__value__'] = 'WZQ(*&YTG'
e = FeatStruct()
e['first'] = a
e['second'] = b
e['third'] = c
e['fourth'] = d
f = FeatStruct()
f['nested'] = e
g = FeatStruct()
g['__value__'] = "WAAAAAAAAH!"
f['single'] = g
print f
print ''
print remove_value_tag(match_feature(f, 'i', 1))
def get_timex_featstructs(root):
timexs = get_timexs(root)
return [FeatStruct(t.attrib) for t in timexs]
def __hash__(self):
self.freeze()
return FeatStruct.__hash__(self)
path_1 = get_all_path(fs1)
for i in path_1:
item_2 = get_element_by_path(fs2, i)
if item_2 == None:
item_1 = get_element_by_path(fs1, i)
add_new_fs(new_fs, i, item_1, 1)
if tree1 != None:
correct_other_nodes(correction_list, tree1)
if tree2 != None:
correct_other_nodes(correction_list, tree2)
return new_fs
fs1 = FeatStruct()
fs2 = FeatStruct()
fs3 = FeatStruct()
fs4 = FeatStruct()
fs4['more'] = fs3
fs2['__or_a'] = 'a'
fs2['__or_wzq'] = 'wzq'
fs2['__or_qwe'] = 'qwe'
fs1['apple'] = fs2
fs1['orange'] = fs4
fs3['__or_zxcv'] = 'zxcv'
fs3['__or_4567'] = '4567'
debug_start_feature = parse_feature_in_catalog(
'<mode> = ind/imp <comp> = nil <wh> = <invlink> <punct term> = per/qmark/excl <punct struct> = nil'
)
empty_feature = FeatStruct()
def __hash__(self):
self.freeze()
return FeatStruct.__hash__(self)
class Terminal:
def __init__(self,name,value,features):
self.name = name
self.value = value
self.features = features
nodes = []
tree = ET.parse('65.xml')
root = tree.getroot()
for sentence in root.iter('Sentence'):
for tree in sentence.iter('Tree'):
for node in tree.iter('Node'):
if 'Unicode' in node.keys():
features = FeatStruct()
for key in node.keys():
if key in ['Person','Tense','Voice','Mood','Case','Number','Gender','Degree']:
features = features.unify(FeatStruct('['+str(key)+'=\''+str(node.get(key))+'\']'))
nodes.append(Terminal(node.get('Cat'),node.get('Unicode'),features))
else:
name = str(node.get('Cat')) + ('_'+ str(node.get('Rule')) if node.get('Rule')!=None else '') + ('_' + str(node.get('ClType')) if node.get('ClType')!=None else '')
children = []
for child in node.getchildren():
children.append(child.get('Cat') + ('_'+ str(child.get('Rule')) if child.get('Rule')!=None else '') + ('_' + str(child.get('ClType')) if child.get('ClType')!=None else ''))
nodes.append(NonTerminal(name,children))
nodes.append('------')
def printCFG(nodes):
s = ''
for node in nodes:
def special_unify(fs1, fs2, tree1=None, tree2=None):
"""
:param fs1: One of the feature structures you want to unify
:type fs1: FeatStruct
:param fs2: Another feature structure
:type fs2: FeatStruct
:param tree1: The tree that you want to restore inter-node reference
:type tree1: TAGTree
:param tree2: Another tree, optional.
:type tree2: TAGTree
This function will do a unify just like what the normal unify() does, but
in addition to a normal unification we also have the following features:
1. Disjunction is considered, e.g. [x = a/b/c] and [x = b/c/d] should yield
[x = b/c]; [x = a/b/c] and [x = a/b] should yield [x = a/b]
2. The result is a new feature structure, but the leaf node is not new;
actually we will make reference to the leaf nodes in fs1 and fs2
3. When the path and the value are both the same, we will make new nodes,
and then fix the references in the trees given by parameters tree1 and tree2
to make the entry point to the new node.
"""
new_fs = FeatStruct()
correction_list = []
path_2 = get_all_path(fs2) # To save time, no path_1
for i in path_2:
item_1 = get_element_by_path(fs1, i)
if item_1 == None:
item_2 = get_element_by_path(fs2, i)
add_new_fs(new_fs, i, item_2,
1) # ref == 1, we only do reference!!
else:
item_2 = get_element_by_path(fs2, i)
tc = test_contain(
item_1, item_2) # Single entry is the same as multiple entry
if tc == 1: # t1 is a subset of t2, we always use the smaller one
add_new_fs(new_fs, i, item_1, 1)
elif tc == -1:
add_new_fs(new_fs, i, item_2, 1)
elif tc == 0: # Two entries are the same, we create a new one
corr_check = search_correction(correction_list, item_1)
if corr_check == None:
new_entry = copy.deepcopy(item_1)
# This tuple is used to correct the reference in tree(s)
# Enumerating all paths, check whether the id of the value is
# equal to either item1 or item2, if it is then change the
# reference to new_entry
correction_tuple = (new_entry, item_1, item_2)
correction_list.append(correction_tuple)
else:
# The return value is the new entry stored if three is not None
new_entry = corr_check
# Add new reference (new entry or existing entry)
add_new_fs(new_fs, i, new_entry, 1)
elif tc == None:
return None # Conflict
# Partial intersection, return value is a new FeatStruct only contains
# the intersection. But we do not need to correct this, since it
# is brand-new
else:
add_new_fs(new_fs, i, tc, 1)
#if i[0] == 'comp': print tc
# We do not need to check when item_2 != None, because we have already
# done it in the first loop. In other words, we have processed the overlapping
# paths, and what is left is to add those in fs1 but not in fs2 into the
# new feature structure
path_1 = get_all_path(fs1)
for i in path_1:
item_2 = get_element_by_path(fs2, i)
if item_2 == None:
item_1 = get_element_by_path(fs1, i)
add_new_fs(new_fs, i, item_1, 1)
if tree1 != None:
correct_other_nodes(correction_list, tree1)
if tree2 != None:
correct_other_nodes(correction_list, tree2)
return new_fs
def featStruct(gapUp,
semUp,
varUp,
arriveFlag=False,
destVpFlag=False,
sourceVpFlag=False,
busNameNpFlag=False,
destNpFlag=False,
departFlag=False,
departVpFlag=False):
gap = Variable('?gap')
if arriveFlag and not (destVpFlag) and not (sourceVpFlag):
vp = FeatStruct(arrive=FeatStruct(
a=Variable('?a'),
f=Variable('?f'),
t=FeatStruct(t_var=Variable('?t'), time_var=Variable('?time'))))
elif departFlag and departVpFlag:
vp = FeatStruct(depart=FeatStruct(
d=Variable('?d'),
f=Variable('?fDep'),
t=FeatStruct(t_var=Variable('?t_var_dep'),
time_var=Variable('?timeDepart'))),
source=FeatStruct(bus=Variable('?h'),
sourceName=FeatStruct(
f=Variable('?h'),
name=Variable('?nameSource'))))
else:
vp = FeatStruct(
depart=FeatStruct(d=Variable('?d'),
f=Variable('?fDep'),
t=FeatStruct(t_var=Variable('?t_var_dep'),
time_var=Variable('?timeDepart'))),
source=FeatStruct(bus=Variable('?h'),
sourceName=FeatStruct(
f=Variable('?h'),
name=Variable('?nameSource'))),
arrive=FeatStruct(a=Variable('?a'),
f=Variable('?fArr'),
t=FeatStruct(t_var=Variable('?t_var_arr'),
time_var=Variable('?timeArrive'))),
dest=FeatStruct(destName=FeatStruct(
f=Variable('?f'),
name=FeatStruct(h=Variable('?hDest'),
name=Variable('?nameDest')))))
if destNpFlag and not (busNameNpFlag):
np = FeatStruct(dest=FeatStruct(
bus=Variable('?f'),
dest=FeatStruct(f=Variable('?f'),
name=FeatStruct(h=Variable('?h'),
name=Variable('?name')))))
else:
np = FeatStruct(
the=FeatStruct(bus=Variable('?b'),
busname=FeatStruct(h=Variable('?h_BusName'),
name=Variable('?busName'))))
wh = FeatStruct(
whType=FeatStruct(f=Variable('?f'), type=Variable('?type')))
sem = FeatStruct(query=FeatStruct(vp=vp, np=np, wh=wh))
var = Variable('?a')
para = FeatStruct(gap=gap, sem=sem, var=var)
paraUpdate = FeatStruct(gap=gapUp, sem=semUp, var=varUp)
# print('$$$$$$$$$$$$$$$$$$$$$$$$$$$$$')
# print(paraUpdate)
# paraUpdate.unify(para)['sem']['query']['vp']['arrive']['f']
return paraUpdate.unify(para)
def get_trigram_featstruct(trigram_tuple):
return FeatStruct(x=trigram_tuple[0],y=trigram_tuple[1],z=trigram_tuple[2])
def analyze_template(s):
# The return value of this function is a tuple. The first element of the tuple is a dictionary
# using identifiers from morph.flat, and the entries are feature structures
# with proper values set. The second element is a dictionary using keys from
# syntax-coded.flat, which will return a list containing all feature structures
# from a given identifier.
lines = s.splitlines()
feature_list = {}
feature_list2 = {}
for l in lines:
#print l
l = l.strip()
if l == '' or l[0] == ';':
continue
index = l.find(';')
if index != -1:
l = l[:index]
l = l.strip()
if l[0] == '@':
if l[-1] != '!':
raise TypeError("Each line should be terminated with a '!'")
l = l[1:-1]
# we only split the name and the other part
temp = l.split(None,1)
name = temp[0]
l = l[len(name):].strip()
features = l.split(',')
fs = FeatStruct()
for f in features:
f = f.strip()
index = f.find('=')
if f[0] == '@' and feature_list.has_key(f[1:]):
fs = fs.unify(feature_list[f[1:]]) # unify() does not change in-place
elif index != -1:
lhs = f[:index].strip()
rhs = f[index + 1:].strip()
if rhs[0] == '@': # rhs can also be reference
rhs = feature_list[rhs[1:]]
if lhs[0] != '<' or lhs[-1] != '>':
raise TypeError('The left hand side of a feature structure must be wrapped with <>')
lhs = lhs[1:-1]
path = lhs.split()
#path.reverse() # This method is in-place
fs = fs.unify(make_fs(path,rhs))
feature_list[name] = fs
#print name
#print fs,'\n'
elif l[0] == '#':
if l[-1] != '!':
raise TypeError('Invalid input line, must be terminated by "!"')
l = l[1:-1]
tokens = l.split(None,1) # Split for once using space character
word_pos = tokens[0].strip()
features = tokens[1].split(',')
new_fs = FeatStruct()
for fs in features:
tokens = fs.split(':',1)
node_type = tokens[0].strip()
tokens = tokens[1].split('=',1)
lhs = tokens[0].strip()[1:-1] # Remove <>
rhs = tokens[1].strip()
lhs = lhs.split()
if new_fs.has_key(node_type):
new_fs[node_type] = new_fs[node_type].unify(make_fs(lhs,rhs))
else:
new_fs[node_type] = make_fs(lhs,rhs)
if feature_list2.has_key(word_pos) == False:
feature_list2[word_pos] = new_fs
else:
#feature_list2[word_pos].append(new_fs)
raise KeyError('Duplicate defitinion detected: %s.' % (word_pos))
else:
raise TypeError('Cannot recognize line: %s.' % (l))
return (feature_list,feature_list2)
pred: FeatStruct, arg_0: Union[List[FeatStruct], FeatStruct], arg_1: Union[List[FeatStruct], FeatStruct]
) -> FeatStruct:
"""
Construct feature structure than represent fact.
ex: know(Gael, [Bas, Justine]), return a feature structure of the type :
[pred=[sem=know],
arg0 = [head=Gael, tail=na],
arg1 = [head=Bas, tail=[head=Justine, tail=na]]]
"""
arg_0 = arg_0 if isinstance(arg_0, list) else [arg_0]
arg_1 = arg_1 if isinstance(arg_1, list) else [arg_1]
return FeatStruct(arg0=format_list(arg_0), arg1=format_list(arg_1), pred=pred)
# STATIC LIST OF ALL PREDICATES
know = FeatStruct(sem="know")
know_r = FeatStruct(sem="know_r")
member = FeatStruct(sem="member")
# INPUT DATA
Gael = FeatStruct(proper="Gael", is_proper=True, explicit=True, is_noun=False, gender="male", form="singular")
Bas = FeatStruct(proper="Bas", is_proper=True, explicit=True, is_noun=False, gender="male", form="singular")
Justine = FeatStruct(proper="Justine", is_proper=True, explicit=True, is_noun=False, gender="female", form="singular")
club = FeatStruct(
proper="na", is_proper=False, is_noun=True, explicit=True, noun="tennis_club", gender="neuter", form="singular"
)
# Dynamic rules that are specific to this input data
# for instance "ProperName[proper=Bas] -> "Bas"
dynamic_productions = [entity_specific_rule(entity) for entity in [Gael, Bas, Justine, club]]
def mainLogic(doc):
departFlag = False
departVpFlag = False
arriveFlag = False
sourceVpFlag = False
destVpFlag = False
busNameNpFlag = False
destNpFlag = False
d = ''
t = ''
a = ''
time = ''
nameDepart = ''
nameArrive = ''
bVar = ''
h_BusName = ''
busName = ''
timeDepart = ''
cityTokenText = ['Hồ_Chí_Minh', 'Hà_Nội', 'Huế', 'Đà_nẵng', 'Đà_Nẵng']
busTokenText = ['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8']
cityTokenDep = ['compound', 'nmod', 'obl']
(f, typeWh) = ('f2', 'WHICH1') if (
subtree_matcher(doc, 'det', text='nào') != []) else ('h1', 'HOWLONG1')
if (typeWh == 'WHICH1'):
gap = f
else:
#Runtime (HOWLONG1 case)
gap = 'r2'
if (gap == 'f2'):
if subtree_matcher(doc, 'case', text='đến') != [] or subtree_matcher(
doc, 'ccomp', text='đến') != []:
arriveFlag = True
a = 'a3'
# time=subtree_matcher(doc,'nummod')[0] if (len(subtree_matcher(doc,'nummod'))==1) else subtree_matcher(doc,'nummod')
# print([i.text for i in searchChild(doc,'ROOT')])
try:
time = [
i.text for i in searchChild(doc, 'ROOT') if 'HR' in i.text
][0]
except:
time = ''
if time != '':
t = 't2'
else:
t = '?t'
################################################
if subtree_matcher(doc, 'ROOT', text='đi') != []:
departFlag = True
d = 'd3'
for cT in cityTokenText:
for cD in cityTokenDep:
temp = subtree_matcher(doc, cD, cT)
tempHead = checkHead(doc, temp)
try:
tempChild = [i.text for i in searchChild(doc, cD)]
except:
tempChild = ''
if (temp != []) and (tempHead != 'đi'):
destVpFlag = True
nameArrive = temp
elif (temp != []) and (tempHead
== 'đi') and ('từ'
in tempChild):
sourceVpFlag = True
nameDepart = temp
elif (temp != []) and (tempHead
== 'đi') and ('đến'
in tempChild):
destVpFlag = True
nameArrive = temp
# elif subtree_matcher(doc,'ROOT',text='xuất_phát')!=[]:
# departFlag=True
# d='d3'
# for cT in cityTokenText:
# for cD in cityTokenDep:
# temp=subtree_matcher(doc,cD,cT)
# tempHead=checkHead(doc,temp)
# # try:
# # tempChild=[i.text for i in searchChild(doc,cD)]
# # except:
# # tempChild=''
# if (temp !=[]) and (tempHead!='xuất_phát'):
# destVpFlag=True
# nameDepart= temp
# # elif (temp !=[]) and (tempHead=='xuất_phát') and ('từ' in tempChild):
# # sourceVpFlag=True
# # nameDepart= temp
# # elif (temp !=[]) and (tempHead=='xuất_phát') and ('đến' in tempChild):
# # destVpFlag=True
# # nameArrive= temp
else:
for cT in cityTokenText:
for cD in cityTokenDep:
temp = subtree_matcher(doc, cD, cT)
if temp != []:
destNpFlag = True
nameArrive = temp
break
else:
# Continue if the inner loop wasn't broken.
continue
# Inner loop was broken, break the outer.
break
elif subtree_matcher(doc, 'ROOT', text='xuất_phát') != []:
departFlag = True
d = 'd3'
for cT in cityTokenText:
for cD in cityTokenDep:
temp = subtree_matcher(doc, cD, cT)
tempHead = checkHead(doc, temp)
# try:
# tempChild=[i.text for i in searchChild(doc,cD)]
# except:
# tempChild=''
if (temp != []) and (tempHead != 'xuất_phát'):
departVpFlag = True
nameDepart = temp
# elif (temp !=[]) and (tempHead=='xuất_phát') and ('từ' in tempChild):
# sourceVpFlag=True
# nameDepart= temp
# elif (temp !=[]) and (tempHead=='xuất_phát') and ('đến' in tempChild):
# destVpFlag=True
# nameArrive= temp
# print(destNpFlag)
# Variable('?hDest'),name=Variable('?nameDest')
elif (gap == 'r2'):
if (subtree_matcher(doc, 'ROOT', text='đến')) != []:
arriveFlag = True
a = 'a3'
time = '?time'
t = '?t'
nameArrive = subtree_matcher(doc, 'obj')[0] if (len(
subtree_matcher(doc, 'obj')) == 1) else subtree_matcher(
doc, 'obj')
if type(nameArrive) == list:
for obj in nameArrive:
if obj in cityTokenText:
nameArrive = obj
break
if nameArrive != '':
h = 'h4'
destVpFlag = True
else:
h = '?h'
if subtree_matcher(doc, 'case', text='từ') != []:
d = 'd3'
nameDepart = checkHead(doc, 'từ')
if (nameDepart != ''):
sourceVpFlag
listObj = subtree_matcher(doc, 'obj')
listCompound = subtree_matcher(doc, 'compound')
for sub in listObj:
if sub in busTokenText:
busName = sub
if busName == '':
for sub in listCompound:
if sub in busTokenText:
busName = sub
if busName != '':
busNameNpFlag = True
bVar = 'f2'
h_BusName = 'h3'
if arriveFlag and not (destVpFlag) and not (sourceVpFlag):
vp = FeatStruct(
arrive=FeatStruct(a=a, f=f, t=FeatStruct(t_var=t, time_var=time)))
elif departFlag and departVpFlag:
vp = FeatStruct(
depart=FeatStruct(d='d3',
f='f1',
t=FeatStruct(t_var=t, time_var=time)),
source=FeatStruct(bus='h3',
sourceName=FeatStruct(f=Variable('?h'),
name=nameDepart)))
else:
vp = FeatStruct(
depart=FeatStruct(d='d3',
f='f1',
t=FeatStruct(t_var=t, time_var=time)),
source=FeatStruct(bus='h4',
sourceName=FeatStruct(f=Variable('?h'),
name=nameDepart)),
arrive=FeatStruct(a='a3',
f='f2',
t=FeatStruct(t_var=t, time_var=time)),
dest=FeatStruct(destName=FeatStruct(
f=Variable('?f'), name=FeatStruct(h='h6', name=nameArrive))))
if destNpFlag and not (busNameNpFlag):
np = FeatStruct(dest=FeatStruct(
bus=Variable('?f'),
dest=FeatStruct(f=Variable('?f'),
name=FeatStruct(h='h3', name=nameArrive))))
else:
np = FeatStruct(the=FeatStruct(
bus=bVar, busname=FeatStruct(h=h_BusName, name=busName)))
wh = FeatStruct(whType=FeatStruct(f=f, type=typeWh))
sem = FeatStruct(query=FeatStruct(vp=vp, np=np, wh=wh))
var = Variable('?a')
result = featStruct(gap,
sem,
var,
arriveFlag=arriveFlag,
destVpFlag=destVpFlag,
sourceVpFlag=sourceVpFlag,
busNameNpFlag=busNameNpFlag,
destNpFlag=destNpFlag,
departFlag=departFlag,
departVpFlag=departVpFlag)
print(result)
return result
def _applyConstraints(self, parent, child):
""" Constraint step:
Given a satisfied state ``child'' and the updated state ``parent,'' compositional constraints
are applied in the form of unification between the semantic heads of the two states.
A successful unification implies the two states are compatible given the compositional
constraints.
In which case, the semantic bodies are merged and updated to reflect the unification.
Lastly, the string of the ``parent'' is updated.
Because unification is a tricky process, care needs to be taken to ensure that it is properly
performed.
Variables that have the same name are assumed to be the same, but because the grammar generates
feature structures for compositional constraints using a small set of variable names, often
variables can have the same name despite their being independent entities.
One workaround is to rename the variables in one of the semantic heads before unification.
It is important that the renamed variables are updated in the semantic bodies as well.
"""
if LWFG.is_terminal(child.prod.lhs()): # child is a special lexical state, and thus contains no semantic info
return [True, parent] # skip
###################################################
# Unification of Semantic Heads
##################################################
# The compositional constraints are represented in such a way that there a separate set of constraints for each
# term in the production rule.
# Each set of constraints is a separate feature structure.
# The set of constraints for the LHS are indexed with a feature identifier: `h'
# The set of constraints for the ith term in the RHS are indexed with a feature indentifier: `hi'
#
# Thus, when unification is performed, the constraints of the LHS of the child state (indexed by `h') need
# to be retrieved.
# Also, because NLTK unifies embedded feature structures only when their feature identifiers are the same,
# we need to have the feature id of the retrieved set of constraints to match the corresponding feature id
# in the parent state.
# Again, that would be `hi' where $i$ is the index of the child's LHS in the parent's RHS.
hidx = 'h'+str(parent.dotIdx)
childHead = FS() # make a new Feature Structure to get around the copy-by-ref issues
childHead[hidx] = child.head['h']
parentHead = parent.head
# Rename variables in childHead to avoid confusion
# step 0: check if child.body and parent.body share variable names, change any that are shared
renamedVarMap1 = {}
usedVars = []
if parent.body:
pBodVars = parent.body.variables()
cBodVars = child.body.variables()
for v in cBodVars:
if v in pBodVars: # change v
nv = self._newVarName(v)
while nv in pBodVars+cBodVars:
nv = self._newVarName(nv)
usedVars.append(Variable(nv))
renamedVarMap1[Variable(v)] = Variable(nv)
else: # not shared by parent
usedVars.append(Variable(v))
for v in pBodVars:
usedVars.append(Variable(v))
else:
for v in child.body.variables():
usedVars.append(Variable(v))
# step 1: find used variables from parent's semantic head
usedVars += list(parentHead.variables())
# step 2: rename variables in child's semantic head
renamedVarMap2 = {}
childHead = childHead.rename_variables(used_vars=usedVars, new_vars=renamedVarMap2)
# check if features align with each other
childFeats = set(childHead[hidx].keys())
parentFeats = set(parentHead[hidx].keys())
if not (parentFeats <= childFeats): # True if the relevant set of features of the parent state
# are **not** a subset of those of the child state.
return [False, parent] # If True, the states are incompatible.
# perform unification
bindings = {}
parentHead = parentHead.unify(childHead, bindings)
if not parentHead: # failed to unify
return [False, parent] # the states are incompatible
# update pState's rule
parent.head = parentHead
###################################################
# Updating Semantic Bodies
##################################################
childBody = LWFG.OntoSeR(str(child.body)) # create new sem body to get around copy-by-ref issues
# The next part is a bit confusing and needs spelling out.
# We want to change variables names in the child's semantic body that are shared with
# the parent's semantic body.
# However, we can't just do it willy-nilly, we need to ensure that only those variable names
# that are not linked with the parent's body are changed.
# For example, in "the smart girl" -- when "n -> det n" is completed, the body corresponding to "the"
# is linked with the body corresponding to "smart girl".
# If we change variables names without consideration, that link is lost.
#
# To maintain the link, a roundabout method is employed.
# We first look at all renamed var names in "renamedVarMap2" -- which results from changing vars in
# the child's semantic head.
# We then look at all renamed names and see whether they were involved in unification with the parent's
# head.
# We can do that by going through the unification "bindings" and checking to see if the renamed names
# are keys.
# If they were involved in unification, we check to see if they were bound to a variable in the parent's
# head that we were about to rename.
# We can do that by looking to see if the bound variable is a key in "renamedVarMap1" -- which we got
# from renaming variable names in the child's body that were present in the parent's body.
for var in renamedVarMap2.keys():
if renamedVarMap2[var] in bindings.keys() and bindings[renamedVarMap2[var]] in renamedVarMap1.keys():
renamedVarMap1.pop(bindings[renamedVarMap2[var]])
childBody.substituteBindings(renamedVarMap2)
childBody.substituteBindings(renamedVarMap1)
if not parent.body:
## print parent, child
## print parent.body, child.body
## print renamedVarMap1
## print renamedVarMap2
## print bindings
## print " "
parentBody = childBody
else:
parentBody = LWFG.OntoSeR(str(parent.body) + ',' + str(childBody))
parentBody.substituteBindings(bindings)
parent.body = parentBody
###################################################
# Updating Strings
##################################################
if not parent.string:
parentString = child.string
else:
parentString = parent.string + ' ' + child.string
parent.string = parentString
return [True, parent]
