def find_ambiguous_examples2():
raw = read_file('resources/farsnetWSDDataset.json')
parsed_json = json.loads(raw)
output = json.loads(read_file('resources/FNWords2.json'))
ambigs = json.loads(read_file('resources/ambigs2.json'))
parsed = read_file('parsed_synsets2.json')
parsed = json.loads(parsed)
for item in parsed_json[:len(parsed) + 2000]:
id = int(item['id'])
if id in parsed:
continue
example = item['example']
for sentence in example.split('*'):
if len(sentence) < 30:
continue
entry = item.copy()
all_syns, candid_syns = extract_synsets(sentence)
entry['example'] = sentence
entry['words'] = candid_syns
for key in candid_syns:
if id in candid_syns[key] and len(candid_syns[key]) > 1:
entry['ambig_word'] = key
ambigs.append(entry)
output.append(entry)
parsed.append(id)
if (len(output) % 10) == 0:
print("Parsed {} examples, {} ambigs found".format(
len(output), len(ambigs)))
write_file('resources/FNWords2.json', json.dumps(output,
ensure_ascii=False))
write_file('resources/ambigs2.json', json.dumps(ambigs,
ensure_ascii=False))
write_file('parsed_synsets2.json', json.dumps(parsed, ensure_ascii=False))
def find_gloss_relations(name):
base_path = 'resources/' + name + '/'
words_path = base_path + 'all_synsets.json'
relations_path = base_path + 'gloss_relations.json'
processed_path = base_path + 'gloss_parsed.json'
parsed_synsets = json.loads(read_file(words_path))
wordnet = prepare_wordnet(name)
relations = json.loads(read_file(relations_path))
processed_syns = json.loads(read_file(processed_path))
i = 0
j = len(processed_syns)
for syn_id in parsed_synsets:
j += 1
if syn_id in processed_syns:
continue
processed_syns.append(syn_id)
i += 1
syn_data = parsed_synsets[syn_id]
temp, words = wordnet.extract_synsets(syn_data['gloss'])
for word_tag in words:
if len(words[word_tag]) == 1:
relations.append((syn_id, str(words[word_tag][0])))
if i % 10 == 0:
print('{} relations found so far(iteration:{})'.format(
len(relations), j))
if i > 150:
break
write_file(relations_path, json.dumps(relations))
write_file(processed_path, json.dumps(processed_syns))
print("{} gloss relations found and written to {} folder".format(
len(relations), name))
def find_ambiguous_examples(name):
base_path = 'resources/' + name + '/'
raw = read_file(base_path + 'all_synsets.json')
dataset_synsets = json.loads(raw)
words_path = base_path + 'examples_words.json'
output = json.loads(read_file(words_path))
ambigs_path = base_path + 'ambigs.json'
ambigs = json.loads(read_file(ambigs_path))
processed_path = base_path + 'parsed_synsets.json'
parsed = json.loads(read_file(processed_path))
wordnet = prepare_wordnet(name)
i = 0
j = len(parsed)
for syn_id in dataset_synsets:
j += 1
item = dataset_synsets[syn_id]
if syn_id in parsed or 'example' not in item:
continue
i += 1
example = item['example']
for sentence in example.split('*'):
entry = item.copy()
all_syns, candid_syns = wordnet.extract_synsets(sentence)
entry['example'] = sentence
entry['words'] = candid_syns
#@todo: @bug: If an example contains "*", current code only keeps the last sentence
output[syn_id] = entry
if len(sentence) < 30:
continue
#If a long enough example has more than one meaning for the word that is
#representative of syn_id(sense_snapshot), it is a good item for wsd dataset:
for key in candid_syns:
if int(syn_id) in candid_syns[key] and len(
candid_syns[key]) > 1:
entry['ambig_word'] = key
#@todo: @bug: If an example contains "*", current code only keeps the last sentence
ambigs[syn_id] = entry
#We found anchor word, do not iterate over other words of the example
break
parsed.append(syn_id)
if (i % 10) == 0:
print("Parsed {} examples, {} ambigs found".format(j, len(ambigs)))
if i > 200:
break
write_file(words_path, json.dumps(output, ensure_ascii=False))
write_file(ambigs_path, json.dumps(ambigs, ensure_ascii=False))
write_file(processed_path, json.dumps(parsed, ensure_ascii=False))
def find_almost_certain_examples(name):
""" Tries to find examples in which all words have only one synsets except the main word"""
ambigs = json.loads(read_file('resources/' + name + '/ambigs.json'))
certain_sens = 0
num_certains = []
certain_examples = []
for syn_id in ambigs:
item = ambigs[syn_id]
certain_words = 0
for key in item['words']:
if len(item['words'][key]) == 1:
certain_words += 1
num_certains.append(certain_words)
if certain_words == len(item['words']) - 1:
certain_sens += 1
certain_examples.append(item)
import numpy as np
import matplotlib.pyplot as plt
print(np.histogram(num_certains))
plt.hist(num_certains, bins=np.arange(12) - .5)
plt.ylabel('Number of sentences')
plt.xlabel('Number of certain words')
plt.show()
print(len(ambigs), certain_sens, sum(num_certains))
write_file('resources/' + name + '/certains.json',
json.dumps(certain_examples, ensure_ascii=False))
def senses_snapshot(self, syn_id):
if self._corpus is None:
self._corpus = json.loads(
read_file('resources/Farsnet/all_synsets.json'))
if syn_id in self._corpus:
return self._corpus[syn_id]['senses_snapshot']
return ''
def fetch_definition(self, syn_id):
if self._synsets == {}:
# fp = open('resources/Wordnet/words.json', '+w')
# self._synsets = json.load(fp)
self._synsets = json.loads(
read_file('resources/Wordnet/all_synsets.json'))
return self._synsets[str(syn_id)]
def find_ambig_by_id(self, syn_id):
if self._ambigs == {}:
# fp = open('resources/Wordnet/words.json', '+w')
# self._synsets = json.load(fp)
self._ambigs = json.loads(
read_file('resources/Wordnet/ambigs.json'))
return self._ambigs[syn_id]
def find_ambiguous_examples():
raw = read_file('resources/farsnetWSDDataset.json')
parsed_json = json.loads(raw)
output = json.loads(read_file('resources/FNWords.json'))
ambigs = json.loads(read_file('resources/ambigs.json'))
parsed = read_file('parsed_synsets.json')
parsed = json.loads(parsed)
for item in parsed_json[:len(parsed) + 200]:
id = int(item['id'])
if id in parsed:
continue
example = normalizer.normalize(item['example'])
#Remove punctuation, keep halfspace:
example = re.sub(r'[^\w\s\u200c]', '', example)
words = word_tokenize(example)
words = remove_stop_words(words)
tags = tagger.tag(words)
synsets = {}
for (w, tag) in tags:
syns = fetch_synsets(w, tag)
if len(syns) == 0:
root = stemmer.stem(w)
if root is not "" and root != w:
syns = fetch_synsets(root, tag)
#only save ambigiues words:
if len(syns) > 0:
synsets[w + '_' + tag] = syns
if id in syns:
ambigs.append(
item
) #this will take the key "words" too, even it is set later
item['words'] = synsets
output.append(item)
parsed.append(id)
if (len(output) % 10) == 0:
print("Parsed {} examples, {} ambigs found".format(
len(output), len(ambigs)))
write_file('resources/FNWords.json',
json.dumps(
output,
ensure_ascii=False,
))
write_file('resources/ambigs.json', json.dumps(ambigs, ensure_ascii=False))
write_file('parsed_synsets.json', json.dumps(parsed, ensure_ascii=False))
def graph(self):
if self._graph is None:
import networkx as nx
self._graph = nx.Graph()
#self._graph = importFromPaj("resources/Farsnet/synset_relation.paj")
#self._graph = importFromPaj("resources/Farsnet/synset_related_to.paj")
#self._graph = importFromPaj("resources/Farsnet/synset_hypernyms.paj")
gloss_relations = json.loads(
read_file('resources/Farsnet/gloss_relations.json'))
for (src, dst) in gloss_relations:
self._graph.add_node(src,
Value=self.senses_snapshot(src) + ';' +
src)
self._graph.add_node(dst,
Value=self.senses_snapshot(dst) + ';' +
dst)
self._graph.add_edge(src, dst, Relation='gloss')
return self._graph
def find_ambig_by_id(self, syn_id):
if self._ambigs is None:
self._ambigs = json.loads(
read_file('resources/Farsnet/examples_words.json'))
return self._ambigs[syn_id]
import re
from Utility import read_file
stop_words = read_file("resources/stop-words.txt").split()
'''and re.sub("\s|\u200c", "", word).isalnum()'''
def remove_stop_words(words):
return [word for word in words if word not in stop_words]
def find_ambig_by_id(syn_id):
samples = json.loads(read_file('resources/FNWords2.json'))
for item in samples:
if item['id'] == str(syn_id):
return item
from Utility import read_file, write_file
import json
from DoWSD import run_wsd
import numpy as np
test_set_size = 100
ambs = json.loads(read_file('resources/ambigs2.json'))
#ambs = json.loads(read_file('resources/certains.json'))
test_set = np.random.choice(ambs, test_set_size, False)
#test_set = ambs[:test_set_size]
#write_file('debug/last_test_set2.json', json.dumps(list(test_set), ensure_ascii=False))
#test_set = json.loads(read_file('debug/last_test_set2.json'))
total_true = 0
i = 0
baseline = 0
fatals = 0
isolated_syns = 0
zero_edges = {'positive': 0, 'negative': 0}
sum_edges = 0
sum_nodes = 0
for item in test_set:
i += 1
all_syns = [str(j) for k in item['words'] for j in item['words'][k]]
answer, report = run_wsd((all_syns, item['words']), True, item['id'])
ranks = report['ranks']
ambig_key = item['ambig_word']
baseline += 1 / len(item['words'][ambig_key])
if ambig_key not in answer:
