def wnToBn(wn_labels, wordNet_to_babelNet, wordnetCompression):
"""
Retrieve the list of labels containing BabelNet synsets
:param wn_labels: list of labels with wordnet synsets
:param wordNet_to_babelNet: dictionary containing the mapping wn_synset => bn_synset
:param wordnetCompression: boolean variable used to check whether is required to pass before from sensekey => wn_synsets and then to wn_synset => bn_synset or not.
:return bn_label_synsets: list of labels with babelnet synsets
"""
bn_label_synsets = []
for label_sequence in wn_labels:
temp = []
for label in label_sequence:
if wordnetCompression:
if 'wn:' in label:
temp.append(wordNet_to_babelNet[label])
else:
temp.append(label)
else:
if re.search(r'(%[1-9])', label):
pos = wn.lemma_from_key(label).synset().pos()
offset = wn.lemma_from_key(label).synset().offset()
synset_id = "wn:" + str(offset).zfill( 8) + pos
temp.append(wordNet_to_babelNet[synset_id])
else:
temp.append(label)
bn_label_synsets.append(temp)
return bn_label_synsets
def build_vocab_synset_SemCor():
target_file = open(
"../../WSD_Evaluation_Framework/Training_Corpora/SemCor/semcor.gold.key.txt",
"r")
# Create a vocab wrapper and add some special tokens.
vocab = Vocabulary()
# iterate through all definitions in the SemCor
for line in target_file:
# synset and literal definition from the WN
key = line.replace('\n', '').split(' ')[-1]
synset = wn.lemma_from_key(key).synset()
# convert '.' to '__' for hashing
synset = synset.name().replace('.', '__')
vocab.add_word(synset)
# add SemEval synsets
semeval_file = open(
"../../WSD_Evaluation_Framework/Evaluation_Datasets/semeval2007/semeval2007.gold.key.txt",
"r")
for line in semeval_file:
key = line.replace('\n', '').split(' ')[-1]
synset = wn.lemma_from_key(key).synset()
synset = synset.name().replace('.', '__')
vocab.add_word(synset)
print("Total vocabulary size: {} {}".format(vocab.idx, len(vocab)))
return vocab
def add_word(vocab, word, production_rule, keys = None, aliases = None):
"""
Get the synset for a word given its lemma key
"""
if word is None or len(word.strip()) == 0:
raise ConfigException("word can't be blank")
word = word.strip().lower()
import nltk, re
from nltk.corpus import wordnet as wn
from word import BaseWord
synsets = []
if keys != None:
# Create a list of synsets for this word, if any lemmas were provided.
if isinstance(keys, (list, tuple)):
for k in keys:
k = k.strip()
s = wn.lemma_from_key(k).synset
if s is None:
raise ConfigException('lemma_from_key("%s") returned None' % (keys.strip()))
elif s not in synsets:
synsets.append(s)
else:
s = wn.lemma_from_key(keys.strip()).synset
if s is None:
raise ConfigException('lemma_from_key("%s") returned None' % (keys.strip()))
elif s not in synsets:
synsets.append(s)
alias_list = []
try:
# Make sure that the word itself is in its own alias list,
# assuming it's a valid regular expression.
re.compile(word)
alias_list.append(word)
except:
pass
# Create a list of aliases if applicable
if aliases != None:
if isinstance(aliases, (list, tuple)):
for a in aliases:
a = a.strip()
if len(a) > 0 and a not in alias_list:
alias_list.append(a)
else:
a = aliases.strip()
if len(a) > 0 and a not in alias_list:
alias_list.append(a)
# Finally create a BaseWord object based on all of this stuff
baseword = BaseWord(word, production_rule, synsets, aliases)
vocab.append(baseword)
def patched_lemma_from_key(key, wordnet=wordnet):
try:
lemma = wordnet.lemma_from_key(key)
except WordNetError as e:
if key in patching_data:
lemma = wordnet.lemma(patching_data[key])
elif '%3' in key:
lemma = wordnet.lemma_from_key(key.replace('%3', '%5'))
else:
raise e
return lemma
def parseXsl(self, fname):
f = xlrd.open_workbook(fname)
f_sheet = f.sheet_by_index(0)
translator = {}
for row in range(1, f_sheet.nrows):
verb_obj = f_sheet.cell(row, 0)
noun_obj = f_sheet.cell(row, 3)
v = verb_obj.value
n = noun_obj.value
translator[wn.lemma_from_key(v).synset()] = wn.lemma_from_key(
n).synset()
return translator
def make_wn2_wn3():
key_map = {}
id_map = {}
curr_path = os.getcwd() + "/"
_rel_path = curr_path.split('/data/')[0]
print(_rel_path)
_path = _rel_path + '/data/wn2.1_3.0'
print("PTH", _path)
mono_files = glob.glob(os.path.join(_path, '*.mono'))
poly_files = glob.glob(os.path.join(_path, '*.poly'))
for file_name in mono_files:
with open(file_name) as fp:
for each in fp.readlines():
wn2_sense_key , wn2_synset_offs ,\
wn3_sense_key , wn3_synset_offs = each.strip().split(' ')
ref_syn = wn.lemma_from_key(wn3_sense_key).synset()
wn2_word = ref_syn.name().split('.')[0]
wn2_pos = ref_syn.pos()
key_map[wn2_sense_key] = [wn3_sense_key]
id_map[(wn2_word , wn2_pos ,1)] = (wn3_sense_key , \
wn3_synset_offs , 1)
for file_name in poly_files:
with open(file_name) as fp:
for each in fp.readlines():
all_keys = each.strip().split(' ')
wn2_sense_key , wn2_synset_offs , wn2_sense_number = \
all_keys[1].split(';')
wn3_vals = [tuple(each.split(';')) \
for each in all_keys[2:]]
key_vals = []
id_vals = []
for wn3_sense_key, wn3_synset_offs, wn3_sense_number in wn3_vals:
if wn3_sense_key:
id_vals.append((wn3_sense_key ,wn3_synset_offs ,\
wn3_sense_number))
key_vals.append(wn3_sense_key)
if key_vals:
ref_syn = wn.lemma_from_key(key_vals[0]).synset()
wn2_word = ref_syn.name().split('.')[0]
wn2_pos = ref_syn.pos()
key_map[wn2_sense_key] = key_vals
id_map[(wn2_word, wn2_pos, wn2_sense_number)] = id_vals
with open(_rel_path + "/data/wn3_id_map", "wb") as file_handle:
pickle.dump(id_map, file_handle)
with open(_rel_path + "/data/wn3_key_map", "wb") as file_handle:
pickle.dump(key_map, file_handle)
return id_map, key_map
def lemmaToWNsynset(sentences, labels, isTrain):
"""
Transform every lemma associated to a sensekey in a wordnet synset.
Then add the mapping lemma => wn_synset, wn_synset => sensekey in two dictionaries dictionary that will be saved as a .txt file.
:param sentences: list of sentences
:param labels: list of labels composed by lemmas and sensekeys
:param isTrain: boolean variable used to distinguish between training and dev set operations
:param wordnetCompression: boolean variable used to check whether the wordnet_synset => sensekeys mapping must be saved
:return updated_labels = list of labels with lemmas and wn_synsets
"""
lemma_to_wn = {} #Dictionary containing the mapping lemma => wordnet synset
updated_labels = []
#For every sentence of the dataset
for i in range(len(sentences)):
temp = []
current_label_sequence= labels[i]
current_sentence = sentences[i]
#For every token in the current sentence
for j in range(len(current_sentence)):
lemma = current_sentence[j]
label = current_label_sequence[j]
#Check if the label is a sensekey
if re.search(r'(%[1-9])', label):
#From the sensekey extract the synset
pos = wn.lemma_from_key(label).synset().pos()
offset = wn.lemma_from_key(label).synset().offset()
wn_synset = "wn:" + str(offset).zfill( 8) + pos
#Add pair (lemma, wordnet_synset) to the dictionary
if not lemma in lemma_to_wn:
lemma_to_wn[lemma] = [wn_synset]
else:
if not wn_synset in lemma_to_wn[lemma]:
lemma_to_wn[lemma].append(wn_synset)
lemma = wn_synset
temp.append(lemma)
updated_labels.append(temp)
#If we worked on the training set, save the dictionary into two files
if isTrain:
if not os.path.exists("../../resource/Mapping_Files/lemma_to_wn.txt"):
with open("../../resource/Mapping_Files/lemma_to_wn.txt", 'w') as file:
for elem in lemma_to_wn:
line = elem + " " + " ".join(lemma_to_wn[elem])
file.write(line + "\n")
return updated_labels
def extractLabelIdentifier(sentences, ids, lemmas_mapping, vocab_identifier,
wordnetCompression):
"""
Every lemma to disambiguate is associated to a label which is mapped to an integer.
This mapping is contained in the vocab_identifier variable- Lemmas to disambiguate are first searched in the "lemmas_mapping" dictionary.
If they are not present there the MSF (most frequent sense) method is applied. Thus their label is recovered from the WordNet interface.
:param sentences: list of tokenized sentences
:param ids: list of identifier for lemmas to predict
:param lemmas_mapping: lemma => label mapping
:param vocab_identifier: label => integer mapping
:param wordnetCompression: boolean variable used to check if a model with the wordnet synset compression method has been used
:return identifiers_list: list of list of integers and sensekey; each internal list correspond to all the sensekey identifier associated to a lemma
"""
identifiers_list = []
for i in range(len(sentences)):
sentence = sentences[i]
id_sequence = ids[i]
sentence_ids = []
for j in range(len(sentence)):
word = sentence[j]
id = id_sequence[j]
word_ids = []
#Check if the current word was met during training
if not id == '0':
if word in lemmas_mapping:
#If it is, ectract the sensekeys associated to the lemma
sensekeys = lemmas_mapping[word]
#Then search for all the sensekeys their identifier
for sensekey in sensekeys:
word_ids.append(vocab_identifier[sensekey])
else:
#Take the most frequent sense from wordnet
mfs = str(wn.synsets(word)[0])[8:-2]
#Retrieve the correspondent sensekey
sensekey = wn.synset(mfs).lemmas()[0].key()
if wordnetCompression:
#Transform the senekey into a wordnet synset
pos = wn.lemma_from_key(sensekey).synset().pos()
offset = wn.lemma_from_key(sensekey).synset().offset()
wn_synset = "wn:" + str(offset).zfill(8) + pos
word_ids.append(wn_synset)
else:
word_ids.append(sensekey)
if word_ids:
sentence_ids.append(word_ids)
identifiers_list.append(sentence_ids)
return identifiers_list
def create_lookup_entry(lemma, dante_answer, answer_id):
"""
Returns True if it failed
"""
try:
if lemma == "U":
wn_definition = "NO DEFINITION FOUND - CHECK CONTEXT"
else:
wn_definition = wn.lemma_from_key(lemma).synset.definition
write_string = str("For answer: " + answer_id +
"\nAre these definitions of " +
dante_answer["headword"] + " the same?\n" +
wn_definition + "\n AND\n" +
dante_answer["meaning"] + "\n")
yes_or_no = None
while yes_or_no != "y" and yes_or_no != "n":
stdout.write("\r%s" % write_string)
yes_or_no = raw_input("y or n?: ")
stdout.flush()
sleep(1)
if yes_or_no == "y":
DanteAPI.write_to_lookup(lemma, dante_answer["meaning"], True)
elif yes_or_no == "n":
DanteAPI.write_to_lookup(lemma, dante_answer["meaning"], False)
else:
raise ValueError("Wrong input squeezed through")
stdout.write("\n")
return True
except WordNetError as err:
synsets = wn.synsets(dante_answer["headword"])
print "head word " + dante_answer[
"headword"] + " and lemma " + lemma + " caused error: " + err.message
print "Synsets: " + str(synsets)
print "FIX IT BEFORE NEXT RUN"
return False
def _disambiguation():
ev = [
'noun.phenomenon', 'noun.act', 'noun.event', 'noun.attribute',
'adj.all', 'adv.all'
]
true_splits = []
with cd(path.join('lib', 'ims_0.9.2.1')):
pattern = '<x(.+?)</x>'
with open('out_.txt', 'r',
encoding='utf-8') as f, open("test_.txt",
'r',
encoding='utf-8') as f1:
for line, line1 in zip(f, f1):
matches = re.finditer(pattern, line)
lexnames = []
for m in matches:
key = re.search('(?<=\s)([^ ]+?)(?=\|)', m[0]).group(
0) # for ' natural%3:00:03::|'
try:
lexname = wn.lemma_from_key(key).synset().lexname()
lexnames.append(lexname)
except WordNetError:
print(key)
print(lexnames)
print(line1)
if set(lexnames).intersection(set(ev)):
true_splits.append(line1.strip())
print('Disambiguation: ', true_splits)
return true_splits
def convert_goldkey2domain(input_path: str,
output_path: str,
bn2domain: Dict[str, str],
is_bn: bool = True) -> None:
"""
This method is used to convert a goldkey map
:param input_path: path of goldkey to convert
:param output_path: path where it writes
:param bn2domain: a map Babelnet 2 domain
:param is_bn: if True, it converts in Babelnet format
if False, it converts in coarse-grained format
:return: None
"""
wn2bn = read_map(config.BABELNET2WORDNET_TR, reverse=True)
with open(str(output_path), mode="w") as out_file:
with open(str(input_path), mode="r") as in_file:
for line in in_file:
line = line.strip().split()
syn = wn.lemma_from_key(line[1]).synset()
syn = "wn:" + str(syn.offset()).zfill(8) + syn.pos()
syn = _get_frequent_sense(syn, wn2bn, bn2domain, is_bn)
out_file.write(line[0] + " " + syn + "\n")
def create_labels_words(path=path_dataset + "semcor+omsti.gold.key.txt"):
###############################################################################
# This function, given the gold file, creates a dictionary of labels (babelnet id, Wndomain, Lexname)
# for each ambiguous words
#
# Input:
# path: path of the gold file
#
# Output:
# dict_sensekey: dictionary of labels
###############################################################################
sense_keys = [sensekey.split() for sensekey in utils.load_txt(path)]
dict_sensekey = {}
for list_info in sense_keys:
# take the synset from the sense key
synset = wn.lemma_from_key(list_info[1]).synset()
# take the wordnet id from the sense key
wn_id = "wn:" + str(synset.offset()).zfill(8) + synset.pos()
bn_id = Wordnet_match[wn_id]
try:
dict_sensekey[list_info[0]] = [
bn_id, Wndomain_match[bn_id], lexname_match[bn_id]
]
# add the factotum label to all the words which don't have a wndomain label
except:
dict_sensekey[list_info[0]] = [
bn_id, "factotum", lexname_match[bn_id]
]
return dict_sensekey
def parse_corpus(path_name='Training_Corpora/SemCor/'):
"""This function parse the training data"""
keys_path = glob.glob(os.path.join(path_name, '*gold.key.txt'))[0]
sentences_path = glob.glob(os.path.join(path_name, '*data.xml'))[0]
keys = dict()
with open(keys_path, 'r') as f:
for line in f:
line = line.strip().split(' ')
id_ = line[0]
synset_keys = line[1:]
synsets = [wn.lemma_from_key(k).synset() for k in synset_keys]
keys[id_] = synsets
with open(sentences_path, 'r') as f:
tree = etree.parse(f)
training = []
for sentence in tree.xpath('//sentence'):
sent_id = sentence.attrib['id']
tags = []
words = []
for chunck in sentence[:]:
type_ = chunck.tag
if type_ == 'instance':
tags.append(keys[chunck.attrib['id']])
if chunck.attrib['pos'] != '.' and chunck.attrib['lemma'] not in STOPWORD:
words.append(chunck.attrib['lemma'])
training.append((words, list(set(itertools.chain.from_iterable(tags)))))
return training
def lemma_from_key(key):
"""
This function returns lemma object given the lemma key.
This is similar to :func:`lemma` but it needs to supply the key
of lemma instead of the name.
.. note::
Support only English language (*eng*).
:param str key: key of the lemma object
:return: lemma object with the given key
:rtype: :class:`Lemma`
:Example:
>>> from pythainlp.corpus.wordnet import lemma, lemma_from_key
>>>
>>> practice = lemma('practice.v.01.exercise')
>>> practice.key()
exercise%2:41:00::
>>> lemma_from_key(practice.key())
Lemma('practice.v.01.exercise')
"""
return wordnet.lemma_from_key(key)
def calc_fiwn_counts():
en2fi = {}
for (fi_synset_key, fi_lemma_str, en_synset_key, en_lemma_str, rel,
extra) in get_transl_iter():
if rel != "synonym":
continue
fi_lemma = get_lemma(fiwn, fi_synset_key, fi_lemma_str)
assert fi_lemma is not None
en_lemma = get_lemma(wordnet, en_synset_key, en_lemma_str)
assert en_lemma is not None
en2fi.setdefault(en_lemma.key(), []).append(fi_lemma.key())
divisors = set()
counts = {}
for en, fis in en2fi.items():
for fi in fis:
counts.setdefault(fi, 0.0)
try:
en_lemma = wordnet.lemma_from_key(en)
except WordNetError:
# The following lemmas are not in the PWN sense index for some reason:
# ['earth%1:17:02::', 'ddc%1:06:01::', 'kb%1:23:01::', 'sun%1:17:02::',
# 'moon%1:17:03::', 'earth%1:15:01::', 'ddi%1:06:01::', 'kb%1:23:03::']
pass
else:
div = len(fis)
divisors.add(div)
counts[fi] += en_lemma.count() / div
mult = reduce(lcm, divisors)
for lemma, cnt in counts.items():
counts[lemma] = int((cnt * mult) + 0.5)
return counts
def create_lookup_entry(lemma, dante_answer, answer_id):
"""
Returns True if it failed
"""
try:
if lemma == "U":
wn_definition = "NO DEFINITION FOUND - CHECK CONTEXT"
else:
wn_definition = wn.lemma_from_key(lemma).synset.definition
write_string = str("For answer: " + answer_id + "\nAre these definitions of " + dante_answer["headword"] +
" the same?\n" + wn_definition + "\n AND\n" + dante_answer["meaning"] + "\n")
yes_or_no = None
while yes_or_no != "y" and yes_or_no != "n":
stdout.write("\r%s" % write_string)
yes_or_no = raw_input("y or n?: ")
stdout.flush()
sleep(1)
if yes_or_no == "y":
DanteAPI.write_to_lookup(lemma, dante_answer["meaning"], True)
elif yes_or_no == "n":
DanteAPI.write_to_lookup(lemma, dante_answer["meaning"], False)
else:
raise ValueError("Wrong input squeezed through")
stdout.write("\n")
return True
except WordNetError as err:
synsets = wn.synsets(dante_answer["headword"])
print "head word " + dante_answer["headword"] + " and lemma " + lemma + " caused error: " + err.message
print "Synsets: " + str(synsets)
print "FIX IT BEFORE NEXT RUN"
return False
def get_synset_from_sense_key(sense_key):
try:
lemma = wn.lemma_from_key(sense_key)
except:
# print("no synset found for: " + sense_key)
return "NA"
return str(lemma.synset())
def wsd_input_format(wsd_src_data, eval_result):
'''
test_data[0] {'target_word': u'art#n', 'target_sense': None, 'id': 'senseval2.d000.s000.t000', 'context': ['the', '<target>', 'of', 'change_ringing', 'be', 'peculiar', 'to', 'the', 'english', ',', 'and', ',', 'like', 'most', 'english', 'peculiarity', ',', 'unintelligible', 'to', 'the', 'rest', 'of', 'the', 'world', '.'], 'poss': ['DET', 'NOUN', 'ADP', 'NOUN', 'VERB', 'ADJ', 'PRT', 'DET', 'NOUN', '.', 'CONJ', '.', 'ADP', 'ADJ', 'ADJ', 'NOUN', '.', 'ADJ', 'PRT', 'DET', 'NOUN', 'ADP', 'DET', 'NOUN', '.']}
'''
wsd_input = []
senses_input = []
for i in range(len(eval_result)):
block = i / 32
src_word1, src_word2 = backward_step1_in[
2 * block], backward_step1_in[2 * block + 1]
tgt_sent = wsd_src_data[i].decode().encode(
'utf-8') + ' ' + eval_result[i]
tgt_word = src_word1
synset = wn.lemma_from_key(tgt_word).synset()
s = synset.name()
target_word = '#'.join(s.split('.')[:2])
context = tgt_sent.split(' ')
for j in range(len(context)):
if context[j] == tgt_word:
context[j] = '<target>'
poss_list = ['.' for _ in range(len(context))]
tmp_dict = {
'target_word': target_word,
'target_sense': None,
'id': None,
'context': context,
'poss': poss_list
}
wsd_input.append(tmp_dict)
senses_input.append((src_word1, src_word2))
return wsd_input, senses_input
def parse_corpus(path_name='Training_Corpora/SemCor/'):
"""This function parse the training data"""
keys_path = glob.glob(os.path.join(path_name, '*gold.key.txt'))[0]
sentences_path = glob.glob(os.path.join(path_name, '*data.xml'))[0]
keys = dict()
with open(keys_path, 'r') as f:
for line in f:
line = line.strip().split(' ')
id_ = line[0]
synset_keys = line[1:]
synsets = [wn.lemma_from_key(k).synset() for k in synset_keys]
keys[id_] = synsets
with open(sentences_path, 'r') as f:
tree = etree.parse(f)
training = []
for sentence in tree.xpath('//sentence'):
sent_id = sentence.attrib['id']
tags = []
words = []
for chunck in sentence[:]:
type_ = chunck.tag
if type_ == 'instance':
tags.append(keys[chunck.attrib['id']])
if chunck.attrib['pos'] != '.' and chunck.attrib[
'lemma'] not in STOPWORD:
words.append(chunck.attrib['lemma'])
training.append(
(words, list(set(itertools.chain.from_iterable(tags)))))
return training
def wordnet_process(filename):
'''
{'target_word': u'picture#n', 'target_sense': None, 'id': None, 'context': [u'<unk>', u'is', u'the', '<target>', u'of', u'the', u'<unk>', u'and', u'the', u'<unk>'], 'poss': ['.', '.', '.', '.', '.', '.', '.', '.', '.', '.']}
'''
dict_list = []
with open(filename) as f1:
for i, line in enumerate(f1):
wordlist = line.strip().split()
dict = {}
target_id = -1
target = ''
for id, word in enumerate(wordlist):
if '%' in word:
name = wn.lemma_from_key(word).synset().name().encode('utf-8').split('.')
target_id = id
# target = name[0]+'#'+name[1]
target = word[:word.index('%')]+'#'+name[1]
break
dict['target_word'] = target
dict['target_sense'] = None
dict['id'] = None
dict['context'] = []
for id, word in enumerate(wordlist):
if id == target_id:
dict['context'].append('<target>')
else:
dict['context'].append(word)
dict['poss'] = ['.' for i in range(0, len(wordlist))]
dict_list.append(dict)
with open(filename+'.pickle', 'wb') as fp:
pickle.dump(dict_list, fp)
def sk_to_ss():
"""Update sensekey in tag file to synsetID (offset-pos)"""
all_sk = set()
print("Reading tag file ...")
with open(SEMCOR_TAG, 'r') as semcor_tag:
lines = [ x.split() for x in semcor_tag.readlines() ]
for line in lines:
sk = line[3]
scloc = sk.find(';')
if scloc > -1:
sk = sk[:scloc] # only consider the first sensekey
all_sk.add(sk)
print(len(all_sk))
print("Loading WordNet ...")
from nltk.corpus import wordnet as wn
all_sk_notfound = set()
with open(SS_SK_MAP, 'w') as mapfile:
for sk in all_sk:
try:
if sk not in all_sk_notfound:
ss = wn.lemma_from_key(sk).synset()
sid = '%s-%s' % (ss.offset(), ss.pos())
mapfile.write('%s\t%s\n' % (sk, sid))
except nltk.corpus.reader.wordnet.WordNetError:
all_sk_notfound.add(sk)
except ValueError:
print("Invalid sk: %s" % (sk,))
all_sk_notfound.add('[INVALID]\t' + sk)
with open(SK_NOTFOUND, 'w') as notfoundfile:
for sk in all_sk_notfound:
notfoundfile.write(sk)
notfoundfile.write('\n')
print("Map file has been created")
def sc2ss(sensekey):
'''Look up a synset given the information from SemCor'''
### Assuming it is the same WN version (e.g. 3.0)
try:
return wn.lemma_from_key(sensekey).synset()
except:
pass
def evaluate(instance, sentence, golds):
preds = predict(instance, sentence)
res = []
for p, g in zip(preds, golds):
g = [wordnet.lemma_from_key(i).synset() for i in g[1]]
res.append(p in g)
return res
def lemmas(self):
'''Return lemmas tied to the synsets'''
if self.__BNLANG:
return [s.getLemmas(self.__BNLANG) for s in self.synsets()]
else:
from nltk.corpus import wordnet as wn
print(self.nearest)
return [wn.lemma_from_key(k) for k, d in self.nearest]
def lemma_to_synset_key(keyin, keyout):
for line in keyin:
inst_id, lemma_ids = line.split(" ", 1)
keyout.write(inst_id)
for lemma_id in lemma_ids.split():
keyout.write(
" " + wordnet.ss2of(wordnet.lemma_from_key(lemma_id).synset()))
keyout.write("\n")
def get_tokens(self, in_file):
root = ET.parse(in_file).getroot()
for i, s in enumerate(root.findall('document/paragraph/sentence')):
for t in s:
synset_labels, lexname_labels = [], []
if 'wn30_key' in t.attrib:
sensekey = t.attrib['wn30_key']
try:
synset = wn.lemma_from_key(sensekey).synset()
except Exception as e:
sensekey = sensekey.replace(
'%3', '%5') # a fix for unprocessable satellites
synset = wn.lemma_from_key(sensekey).synset(
) # now, we should be able to find the modified sensekey in WN
synset_labels.append(synset.name())
lexname_labels.append(synset.lexname())
yield synset_labels, lexname_labels, t.attrib['surface_form']
def create_dictionary(dataset_name, gold2dic, train=False):
words = []
wordnet = []
# get an iterable
context = etree.iterparse(dataset_name)
sentence = []
sentenceNet = []
deletedSentences = 0
dictionary = {}
for event, elem in iter(context):
if elem.tag == "sentence":
#if(int(elem.attrib['id'])%10 == 0)
print('\t' + elem.attrib['id'])
if len(sentence) < 1:
deletedSentences += 1
else:
words.append(' '.join(sentence))
wordnet.append(' '.join(sentenceNet))
sentence = []
sentenceNet = []
elif elem.tag == "wf" or elem.tag == "instance":
lemma = elem.attrib["lemma"].lower()
sentence.append(lemma)
if elem.tag == "instance":
dataset_id = elem.attrib["id"]
synset = wn.lemma_from_key(gold2dic[dataset_id]).synset()
synset_id = "wn:" + str(
synset.offset()).zfill(8) + synset.pos()
sentenceNet.append(synset_id)
if lemma not in dictionary:
dictionary[lemma] = [synset_id]
elif synset_id not in dictionary[lemma]:
dictionary[lemma].append(synset_id)
else:
sentenceNet.append(lemma)
elem.clear()
if train:
save(dictionary, '../resources/' + 'synsetsdic')
flag = 'train'
else:
flag = 'dev'
save(words, '../resources/' + 'words_' + flag)
save(wordnet, '../resources/' + 'wordnet_' + flag)
print('\nSentences removed:', deletedSentences)
return words, wordnet
def build_vocab_decoder_SemCor(threshold):
# Create a vocab wrapper and add some special tokens.
counter = Counter()
target_file = open(
"../../WSD_Evaluation_Framework/Training_Corpora/SemCor/semcor.gold.key.txt",
"r")
# iterate through all definitions in the SemCor
for line in target_file:
# synset and literal definition from the WN
key = line.replace('\n', '').split(' ')[-1]
synset = wn.lemma_from_key(key).synset()
definition = synset.definition()
def_tokens = nltk.tokenize.word_tokenize(definition)
counter.update(def_tokens)
# add SemEval synsets
semeval_file = open(
"../../WSD_Evaluation_Framework/Evaluation_Datasets/semeval2007/semeval2007.gold.key.txt",
"r")
for line in semeval_file:
key = line.replace('\n', '').split(' ')[-1]
synset = wn.lemma_from_key(key).synset()
definition = synset.definition()
def_tokens = nltk.tokenize.word_tokenize(definition)
counter.update(def_tokens)
# If the word frequency is less than 'threshold', then the word is discarded.
words = [word for word, cnt in counter.items() if cnt >= threshold]
# Create a vocab wrapper and add some special tokens.
vocab = Vocabulary()
vocab.add_word('<pad>')
vocab.add_word('<start>')
vocab.add_word('<end>')
vocab.add_word('<unk>')
# Add the words to the vocabulary.
for i, word in enumerate(words):
vocab.add_word(word)
print("Total vocabulary size: {}".format(vocab.idx))
return vocab
def return_synset_id(key):
'''
Method used to return the wordnet synset_id from a sense_key of a specific word given in input.
:param key the input sense_key
:return the synset_id of the sense_key
'''
synset = wn.lemma_from_key(key).synset()
synset_id = "wn:" + str(synset.offset()).zfill(8) + synset.pos()
return synset_id
def wn_id_from_sense_key(sense_key):
"""
Returns a WordNet ID built out of offset and POS from a given WordNet sense key.
:param sense_key: WordNet sense key
:return: WordNet ID corresponding to the given sense key
"""
synset = wn.lemma_from_key(sense_key).synset()
return wn_id_from_synset(synset)
def semcor_to_offset(sensekey):
"""
Converts SemCor sensekey IDs to synset offset.
>>> print semcor_to_offset('live%2:42:06::')
02614387-v
"""
synset = wn.lemma_from_key(sensekey).synset
offset = '%08d-%s' % (synset.offset, synset.pos)
return offset
def readAnswers(ifile="corpora/answers+misc/tasks/english-all-words/key"):
answers = {}
pattern = re.compile("(?P<id>d\S*)\s+(?P<sense>\S+:\S+)")
for i in pattern.finditer(open(ifile).read()):
try:
answers[i.group("id")] = wn.lemma_from_key(i.group("sense"))
except:
pass
return answers
def sk2lemma(self, sk, use_ws=False):
try:
lemma_name = wn.lemma_from_key(sk).name()
except:
lemma_name = sk.split('%')[0]
if use_ws:
lemma_name = lemma_name.replace('_', ' ')
return lemma_name
def semcor_to_offset(sensekey):
"""
Converts SemCor sensekey IDs to synset offset.
>>> print semcor_to_offset('live%2:42:06::')
02614387-v
"""
synset = wn.lemma_from_key(sensekey).synset
offset = '%08d-%s' % (synset.offset, synset.pos)
return offset
def get_rel_lemmas(lemma_str):
ret_lemmas = []
syn = wn.lemma_from_key(lemma_str).synset
ret_lemmas = [i.name.lower() for i in syn.lemmas]
hyns = set(get_all_hypernyms(syn,3))
for h in hyns:
ret_lemmas += [i.name.lower() for i in h.lemmas]
ret_lemmas += [i.lower() for i in lemmatize(nltk.word_tokenize(h.definition))]
return ret_lemmas
def get_sense(word):
try:
synset = wn.lemma_from_key(word).synset().lemma_names()
except Exception:
return word
for w in synset:
if w != word:
return w
return word
def semcor_to_synset(sensekey):
"""
Look up a synset given the information from SemCor sensekey format.
(Thanks for @FBond, see http://moin.delph-in.net/SemCor)
>>> ss = semcor_to_offset('live%2:42:06::')
>>> print '%08d-%s' % (ss.offset, ss.pos)
>>> print ss, ss.definition
02614387-v
Synset('live.v.02') lead a certain kind of life; live in a certain style
"""
return wn.lemma_from_key(sensekey).synset
def get_mfs(self, lemma, sensekeys = None):
senseid = None
try:
if(not sensekeys):
sensekeys = self.get_sense_keys(lemma)
sense_freqs = {}
for i in sensekeys:
lemma_obj = wn.lemma_from_key(i)
sense_freqs[i] = lemma_obj.count()
senseid = max(sense_freqs.iteritems(), key=operator.itemgetter(1))[0]
except(ValueError):
print "Error: No senses for %s" % (lemma)
return senseid
def semcor2synset(self, sense):
return wn.lemma_from_key(sense).synset()
def lemma_from_key(key): return wordnet.lemma_from_key(key)
from nltk.corpus import wordnet as wn
from nltk.corpus.reader.wordnet import WordNetError
with open("EnglishAW.test.key") as answer_file:
answers = answer_file.read().split('\n')[:-1]
for answer in answers:
answer = answer.split()
word_id = answer[1]
lemmas = answer[2:]
for lemma in lemmas:
try:
if lemma != "U":
synset = wn.lemma_from_key(lemma).synset
except WordNetError:
print("word id: {} lemma: {}".format(word_id, lemma))
def semcor_to_offset(sensekey):
synset = wn.lemma_from_key(sensekey).synset
offset = '%08d-%s' % (synset.offset, synset.pos)
return offset
def slesk(self, wordforms):
sensepreds = {}
sw_hash = {i:1 for i in stopwords.words('english')}
open_wfs = [i for i in wordforms if i.tag != 'punc' and not ('cmd' in i.attrib and i.attrib['cmd'] == "ignore")] # remove stop words and punctuation first
# open_wfs = [i for i in wordforms if i.tag != 'punc'] # remove only punctuation
for wf in wordforms:
if('cmd' in wf.attrib and wf.attrib['cmd'] == 'done'):
lemma = wf.attrib['lemma'] # may be need to automatically lemmatize here
sensekeys = self.get_sense_keys(lemma)
if(len(sensekeys) == 0):
print "Error: No senses for %s" % lemma
continue
synsets = {k: wn.lemma_from_key(k).synset for k in sensekeys}
idfs = text_utils.get_idfs()
# get the window now
window = 2
idx = [i for i,x in enumerate(open_wfs) if x == wf][0]
lbound = idx-window if idx-window > 0 else 0
ubound = idx+window if idx+window < len(open_wfs) else len(open_wfs)-1
# all_context = set(text_utils.lemmatize(([i.text.lower() for i in open_wfs[lbound:(ubound+1)] if ('cmd' not in i.attrib or (i.attrib['cmd'] != "ignore" and i.attrib['id'] != wf.attrib['id']))]))) # this one keeps stopwords in window count
all_context = set(text_utils.lemmatize(([i.text.lower() for i in open_wfs[lbound:(ubound+1)] if (i.attrib['id'] != wf.attrib['id'])]))) # this one keeps stopwords in window count
jc_th = 0.1
context = [i for i in all_context if text_utils.compute_jc_sim(i,lemma) > jc_th] # lexical chain selection algorithm
outstr = "-------------------"
outstr += "\ncontext: "+str(context)
# best = self.get_mfs(lemma)
max = 0
cands = []
for k in synsets.keys():
synset = synsets[k]
wntext = text_utils.lemmatize(nltk.word_tokenize(synset.definition))
for ex in synset.examples:
wntext += text_utils.lemmatize(nltk.word_tokenize(ex))
# wntext += text_utils.lemmatize(text_utils.get_rel_lemmas(k)) # related lemmas from hypernyms etc.
wntext = [i.lower() for i in wntext]
lenlog = math.log(len(wntext))
normalizer = 1/ lenlog if lenlog > 0 else 0
outstr += "\n"+k+":"+str(wntext)
wn_hash = {i:1 for i in wntext}
matches = {}
score = 0
for i in context:
if(i in sw_hash): continue
if i in wntext:
score += 1
# if i in idfs:
# score += idfs[i]
# else:
# score += 3
matches[i] = 1 #idfs[i]
outstr += "\nScore: %s:%f" % (matches,score)
# score = score * normalizer
outstr += "\nNorm score: %s:%f" % (matches,score)
if score > max:
cands = [k]
max = score
elif score == max:
cands.append(k)
if(len(cands) > 1):
best = self.get_mfs(lemma, cands)
else:
best = cands[0]
mfs_id = self.get_mfs(lemma)
true_id = lemma+"%"+wf.attrib['lexsn']
if mfs_id == true_id and best != mfs_id:
print "stat:leskbad"
print outstr
print "MFS: %s, LESK: %s, CORRECT: %s" % (self.get_mfs(lemma), best, wf.attrib['lexsn'])
elif mfs_id != true_id and best == true_id:
print "stat:leskgood"
print outstr
print "MFS: %s, LESK: %s, CORRECT: %s" % (self.get_mfs(lemma), best, wf.attrib['lexsn'])
elif max == 0:
print "stat:nolesk"
else:
print "stat:lesksame"
if(best):
m = re.match("^"+lemma+"\%(\d+:\d+:\d+:(.*))", best)
sensepreds[wf.attrib['id']] = m.group(1)
else:
sensepreds[wf.attrib['id']] = "gibberish"
return sensepreds
def semcor_to_synset(sensekey):
return wn.lemma_from_key(sensekey).synset
def getSSFromLemma(l):
return str(wordnet.lemma_from_key(l).synset().name())
