def getNext(self, normalize_digits=True):
line = self.__source_file.readline()
# skip multiple blank lines.
while len(line) > 0 and len(line.strip()) == 0:
line = self.__source_file.readline()
if len(line) == 0:
return None
line = line.strip().split('\t')
label = line[1]
words = line[0].strip().split(' ')
word_ids = []
for pos, word in enumerate(words):
word = DIGIT_RE.sub("0", word) if normalize_digits else word
if self.refiner is not None:
word_id = self.__word_alphabet.get_index(word)
if word_id < 0:
unk_signature = self.refiner.refine(word, pos)
self.__word_alphabet.add(unk_signature)
word_ids.append(
self.__word_alphabet.get_index(unk_signature))
else:
word_ids.append(word_id)
else:
word_ids.append(self.__word_alphabet.get_index(word))
label_id = int(label)
return NERInstance(Sentence(words, word_ids, None, None), label,
label_id, None, None, None, None)
def expand_vocab():
vocab_set = set(vocab_list)
for data_path in data_paths:
with open(data_path, 'r') as file:
for line in file:
line = line.strip()
if len(line) == 0:
continue
tokens = line.split('\t')[0].split(' ')
for token in tokens:
word = DIGIT_RE.sub("0", token) if normalize_digits else token
if word not in vocab_set and (word in embedd_dict or word.lower() in embedd_dict):
vocab_set.add(word)
vocab_list.append(word)
def expand_vocab():
vocab_set = set(vocab_list)
for data_path in data_paths:
# logger.info("Processing data: %s" % data_path)
with open(data_path, 'r') as file:
for line in file:
line = line.strip()
if len(line) == 0:
continue
tokens = line.split(' ')
word = DIGIT_RE.sub("0", tokens[0]) if normalize_digits else tokens[0]
pos = tokens[1]
chunk = tokens[2]
ner = tokens[3]
pos_alphabet.add(pos)
chunk_alphabet.add(chunk)
ner_alphabet.add(ner)
if word not in vocab_set and (word in embedd_dict or word.lower() in embedd_dict):
vocab_set.add(word)
vocab_list.append(word)
def expand_vocab():
vocab_set = set(vocab_list)
for data_path in data_paths:
# logger.info("Processing data: %s" % data_path)
with open(data_path, 'r') as file:
for line in file:
line = line.strip()
if len(line) == 0:
continue
tokens = line.split('\t')
for char in tokens[1]:
char_alphabet.add(char)
word = DIGIT_RE.sub("0", tokens[1]) if normalize_digits else tokens[1]
pos = tokens[4]
type = tokens[7]
pos_alphabet.add(pos)
type_alphabet.add(type)
if word not in vocab_set and (word in embedd_dict or word.lower() in embedd_dict):
vocab_set.add(word)
vocab_list.append(word)
def getNext(self,
normalize_digits=True,
symbolic_root=False,
symbolic_end=False):
line = self.__source_file.readline()
# skip multiple blank lines.
while len(line) > 0 and len(line.strip()) == 0:
line = self.__source_file.readline()
if len(line) == 0:
return None
lines = []
while len(line.strip()) > 0:
line = line.strip()
lines.append(line.split('\t'))
line = self.__source_file.readline()
length = len(lines)
if length == 0:
return None
words = []
word_ids = []
char_seqs = []
char_id_seqs = []
postags = []
pos_ids = []
types = []
type_ids = []
heads = []
if symbolic_root:
words.append(ROOT)
word_ids.append(self.__word_alphabet.get_index(ROOT))
char_seqs.append([
ROOT_CHAR,
])
char_id_seqs.append([
self.__char_alphabet.get_index(ROOT_CHAR),
])
postags.append(ROOT_POS)
pos_ids.append(self.__pos_alphabet.get_index(ROOT_POS))
types.append(ROOT_TYPE)
type_ids.append(self.__type_alphabet.get_index(ROOT_TYPE))
heads.append(0)
for tokens in lines:
chars = []
char_ids = []
for char in tokens[1]:
chars.append(char)
char_ids.append(self.__char_alphabet.get_index(char))
if len(chars) > MAX_CHAR_LENGTH:
chars = chars[:MAX_CHAR_LENGTH]
char_ids = char_ids[:MAX_CHAR_LENGTH]
char_seqs.append(chars)
char_id_seqs.append(char_ids)
word = DIGIT_RE.sub("0",
tokens[1]) if normalize_digits else tokens[1]
pos = tokens[4]
head = int(tokens[6])
type = tokens[7]
words.append(word)
word_ids.append(self.__word_alphabet.get_index(word))
postags.append(pos)
pos_ids.append(self.__pos_alphabet.get_index(pos))
types.append(type)
type_ids.append(self.__type_alphabet.get_index(type))
heads.append(head)
if symbolic_end:
words.append(END)
word_ids.append(self.__word_alphabet.get_index(END))
char_seqs.append([
END_CHAR,
])
char_id_seqs.append([
self.__char_alphabet.get_index(END_CHAR),
])
postags.append(END_POS)
pos_ids.append(self.__pos_alphabet.get_index(END_POS))
types.append(END_TYPE)
type_ids.append(self.__type_alphabet.get_index(END_TYPE))
heads.append(0)
for position, word in enumerate(words):
# TODO here the position is not correct
if self.refine_unk:
word_idx = self.__word_alphabet.get_index(word)
if word_idx < 0:
unk_signature = self.refiner.refine(word, position)
word_ids[position] = self.__word_alphabet.get_index(
unk_signature)
return DependencyInstance(
Sentence(words, word_ids, char_seqs, char_id_seqs), postags,
pos_ids, heads, types, type_ids)
def getNext(self, normalize_digits=True):
line = self.__source_file.readline()
# skip multiple blank lines.
while len(line) > 0 and len(line.strip()) == 0:
line = self.__source_file.readline()
if len(line) == 0:
return None
lines = []
while len(line.strip()) > 0:
line = line.strip()
lines.append(line.split(' '))
line = self.__source_file.readline()
length = len(lines)
if length == 0:
return None
words = []
word_ids = []
char_seqs = []
char_id_seqs = []
postags = []
pos_ids = []
chunk_tags = []
chunk_ids = []
ner_tags = []
ner_ids = []
for tokens in lines:
chars = []
char_ids = []
for char in tokens[0]:
chars.append(char)
char_ids.append(self.__char_alphabet.get_index(char))
if len(chars) > MAX_CHAR_LENGTH:
chars = chars[:MAX_CHAR_LENGTH]
char_ids = char_ids[:MAX_CHAR_LENGTH]
char_seqs.append(chars)
char_id_seqs.append(char_ids)
word = DIGIT_RE.sub("0",
tokens[0]) if normalize_digits else tokens[0]
pos = tokens[1]
chunk = tokens[2]
ner = tokens[3]
words.append(word)
word_ids.append(self.__word_alphabet.get_index(word))
postags.append(pos)
pos_ids.append(self.__pos_alphabet.get_index(pos))
chunk_tags.append(chunk)
chunk_ids.append(self.__chunk_alphabet.get_index(chunk))
ner_tags.append(ner)
ner_ids.append(self.__ner_alphabet.get_index(ner))
return NERInstance(Sentence(words, word_ids, char_seqs,
char_id_seqs), postags, pos_ids,
chunk_tags, chunk_ids, ner_tags, ner_ids)
def create_alphabets(alphabet_directory, train_path, data_paths=None, max_vocabulary_size=100000, embedd_dict=None,
min_occurrence=1, normalize_digits=True):
def expand_vocab():
vocab_set = set(vocab_list)
for data_path in data_paths:
# logger.info("Processing data: %s" % data_path)
with open(data_path, 'r') as file:
for line in file:
line = line.strip()
if len(line) == 0:
continue
tokens = line.split(' ')
word = DIGIT_RE.sub("0", tokens[0]) if normalize_digits else tokens[0]
pos = tokens[1]
chunk = tokens[2]
ner = tokens[3]
pos_alphabet.add(pos)
chunk_alphabet.add(chunk)
ner_alphabet.add(ner)
if word not in vocab_set and (word in embedd_dict or word.lower() in embedd_dict):
vocab_set.add(word)
vocab_list.append(word)
logger = get_logger("Create Alphabets")
word_alphabet = Alphabet('word', default_value=True, singleton=True)
char_alphabet = Alphabet('character', default_value=True)
pos_alphabet = Alphabet('pos')
chunk_alphabet = Alphabet('chunk')
ner_alphabet = Alphabet('ner')
if not os.path.isdir(alphabet_directory):
logger.info("Creating Alphabets: %s" % alphabet_directory)
char_alphabet.add(PAD_CHAR)
pos_alphabet.add(PAD_POS)
chunk_alphabet.add(PAD_CHUNK)
ner_alphabet.add(PAD_NER)
vocab = defaultdict(int)
with open(train_path, 'r') as file:
for line in file:
line = line.strip()
if len(line) == 0:
continue
tokens = line.split(' ')
for char in tokens[0]:
char_alphabet.add(char)
word = DIGIT_RE.sub("0", tokens[0]) if normalize_digits else tokens[0]
vocab[word] += 1
pos = tokens[1]
pos_alphabet.add(pos)
chunk = tokens[2]
chunk_alphabet.add(chunk)
ner = tokens[3]
ner_alphabet.add(ner)
# collect singletons
singletons = set([word for word, count in vocab.items() if count <= min_occurrence])
# if a singleton is in pretrained embedding dict, set the count to min_occur + c
if embedd_dict is not None:
assert isinstance(embedd_dict, OrderedDict)
for word in vocab.keys():
if word in embedd_dict or word.lower() in embedd_dict:
vocab[word] += min_occurrence
vocab_list = _START_VOCAB + sorted(vocab, key=vocab.get, reverse=True)
logger.info("Total Vocabulary Size: %d" % len(vocab_list))
logger.info("Total Singleton Size: %d" % len(singletons))
vocab_list = [word for word in vocab_list if word in _START_VOCAB or vocab[word] > min_occurrence]
logger.info("Total Vocabulary Size (w.o rare words): %d" % len(vocab_list))
if len(vocab_list) > max_vocabulary_size:
vocab_list = vocab_list[:max_vocabulary_size]
if data_paths is not None and embedd_dict is not None:
expand_vocab()
for word in vocab_list:
word_alphabet.add(word)
if word in singletons:
word_alphabet.add_singleton(word_alphabet.get_index(word))
word_alphabet.save(alphabet_directory)
char_alphabet.save(alphabet_directory)
pos_alphabet.save(alphabet_directory)
chunk_alphabet.save(alphabet_directory)
ner_alphabet.save(alphabet_directory)
else:
word_alphabet.load(alphabet_directory)
char_alphabet.load(alphabet_directory)
pos_alphabet.load(alphabet_directory)
chunk_alphabet.load(alphabet_directory)
ner_alphabet.load(alphabet_directory)
word_alphabet.close()
char_alphabet.close()
pos_alphabet.close()
chunk_alphabet.close()
ner_alphabet.close()
logger.info("Word Alphabet Size (Singleton): %d (%d)" % (word_alphabet.size(), word_alphabet.singleton_size()))
logger.info("Character Alphabet Size: %d" % char_alphabet.size())
logger.info("POS Alphabet Size: %d" % pos_alphabet.size())
logger.info("Chunk Alphabet Size: %d" % chunk_alphabet.size())
logger.info("NER Alphabet Size: %d" % ner_alphabet.size())
return word_alphabet, char_alphabet, pos_alphabet, chunk_alphabet, ner_alphabet
def create_alphabets(alphabet_directory, train_path, data_paths=None, max_vocabulary_size=100000, embedd_dict=None,
min_occurrence=1, normalize_digits=True, unk_rank=5):
def expand_vocab():
vocab_set = set(vocab_list)
for data_path in data_paths:
# logger.info("Processing data: %s" % data_path)
with open(data_path, 'r') as file:
for line in file:
line = line.strip()
if len(line) == 0:
continue
tokens = line.split('\t')
for char in tokens[1]:
char_alphabet.add(char)
word = DIGIT_RE.sub("0", tokens[1]) if normalize_digits else tokens[1]
pos = tokens[4]
type = tokens[7]
pos_alphabet.add(pos)
type_alphabet.add(type)
if word not in vocab_set and (word in embedd_dict or word.lower() in embedd_dict):
vocab_set.add(word)
vocab_list.append(word)
logger = get_logger("Create Alphabets")
word_alphabet = Alphabet('word', singleton=True)
char_alphabet = Alphabet('character')
pos_alphabet = Alphabet('pos')
type_alphabet = Alphabet('type')
logger.info("Creating Alphabets: %s" % alphabet_directory)
char_alphabet.add(PAD_CHAR)
pos_alphabet.add(PAD_POS)
type_alphabet.add(PAD_TYPE)
char_alphabet.add(ROOT_CHAR)
pos_alphabet.add(ROOT_POS)
type_alphabet.add(ROOT_TYPE)
char_alphabet.add(END_CHAR)
pos_alphabet.add(END_POS)
type_alphabet.add(END_TYPE)
vocab = defaultdict(int)
# here we use the list to save every word and position
word_collect = []
with open(train_path, 'r') as file:
words = []
position = 0
for line in file:
line = line.strip()
if len(line) == 0:
position = 0
word_collect.append(words)
words = []
continue
tokens = line.split('\t')
for char in tokens[1]:
char_alphabet.add(char)
word = DIGIT_RE.sub("0", tokens[1]) if normalize_digits else tokens[1]
vocab[word] += 1
words.append((word, position))
position += 1
pos = tokens[4]
pos_alphabet.add(pos)
type = tokens[7]
type_alphabet.add(type)
# collect singletons
singletons = set([word for word, count in vocab.items() if count <= min_occurrence])
# if a singleton is in pretrained embedding dict, set the count to min_occur + c
if embedd_dict is not None:
assert isinstance(embedd_dict, OrderedDict)
for word in vocab.keys():
if word in embedd_dict or word.lower() in embedd_dict:
vocab[word] += min_occurrence
vocab_list = _START_VOCAB + sorted(vocab, key=vocab.get, reverse=True)
logger.info("Total Vocabulary Size: %d" % len(vocab_list))
logger.info("Total Singleton Size: %d" % len(singletons))
multi_vocab = [word for word in vocab_list if word in _START_VOCAB or vocab[word] > min_occurrence]
logger.info("Total Vocabulary Size (w.o rare words): %d" % len(multi_vocab))
if len(vocab_list) > max_vocabulary_size:
vocab_list = vocab_list[:max_vocabulary_size]
if data_paths is not None and embedd_dict is not None:
expand_vocab()
for word in vocab_list:
if word in multi_vocab:
word_alphabet.add(word)
elif word in singletons:
word_alphabet.add_singleton(word_alphabet.get_index(word))
else:
raise ValueError("Error word: " + word)
# unk refiner
unk_refiner = UNKRefiner(level=unk_rank, alphabet=word_alphabet)
for words in word_collect:
for word, position in words:
if word in singletons:
unk_signature = unk_refiner.refine(word, position)
word_alphabet.add(unk_signature)
word_alphabet.close()
char_alphabet.close()
pos_alphabet.close()
type_alphabet.close()
logger.info("Word Alphabet Size (Singleton): %d (%d)" % (word_alphabet.size(), word_alphabet.singleton_size()))
logger.info("Character Alphabet Size: %d" % char_alphabet.size())
logger.info("POS Alphabet Size: %d" % pos_alphabet.size())
logger.info("Type Alphabet Size: %d" % type_alphabet.size())
return word_alphabet, char_alphabet, pos_alphabet, type_alphabet
def load_embedding_dict(embedding, embedding_path, normalize_digits=True):
"""
load word embeddings from file
:param embedding:
:param embedding_path:
:return: embedding dict, embedding dimention, caseless
"""
print("loading embedding: %s from %s" % (embedding, embedding_path))
if embedding == 'word2vec':
# loading word2vec
word2vec = Word2Vec.load_word2vec_format(embedding_path, binary=True)
embedd_dim = word2vec.vector_size
return word2vec, embedd_dim
elif embedding == 'glove':
# loading GloVe
embedd_dim = -1
embedd_dict = OrderedDict()
with gzip.open(embedding_path, 'rt', encoding='utf8') as file:
for line1 in file:
line = line1.strip()
if len(line) == 0:
continue
tokens = line.split(' ')
if embedd_dim < 0:
embedd_dim = len(tokens) - 1
elif embedd_dim + 1 != len(tokens):
continue
# assert (embedd_dim + 1 == len(tokens))
embedd = np.empty([1, embedd_dim], dtype=np.float32)
embedd[:] = tokens[1:]
word = DIGIT_RE.sub(
"0", tokens[0]) if normalize_digits else tokens[0]
embedd_dict[word] = embedd
return embedd_dict, embedd_dim
elif embedding == 'senna':
# loading Senna
embedd_dim = -1
embedd_dict = OrderedDict()
with gzip.open(embedding_path, 'rt') as file:
for line in file:
line = line.strip()
if len(line) == 0:
continue
tokens = line.split()
if embedd_dim < 0:
embedd_dim = len(tokens) - 1
else:
assert (embedd_dim + 1 == len(tokens))
embedd = np.empty([1, embedd_dim], dtype=np.float32)
embedd[:] = tokens[1:]
word = DIGIT_RE.sub(
"0", tokens[0]) if normalize_digits else tokens[0]
embedd_dict[word] = embedd
return embedd_dict, embedd_dim
elif embedding == 'sskip':
embedd_dim = -1
embedd_dict = OrderedDict()
with gzip.open(embedding_path, 'rt') as file:
# skip the first line
file.readline()
for line in file:
line = line.strip()
try:
if len(line) == 0:
continue
tokens = line.split()
if len(tokens) < embedd_dim:
continue
if embedd_dim < 0:
embedd_dim = len(tokens) - 1
embedd = np.empty([1, embedd_dim], dtype=np.float32)
start = len(tokens) - embedd_dim
word = ' '.join(tokens[0:start])
embedd[:] = tokens[start:]
word = DIGIT_RE.sub("0",
word) if normalize_digits else word
embedd_dict[word] = embedd
except UnicodeDecodeError:
continue
return embedd_dict, embedd_dim
elif embedding == 'polyglot':
words, embeddings = pickle.load(open(embedding_path, 'rb'),
encoding='latin1')
_, embedd_dim = embeddings.shape
embedd_dict = OrderedDict()
for i, word in enumerate(words):
embedd = np.empty([1, embedd_dim], dtype=np.float32)
embedd[:] = embeddings[i, :]
word = DIGIT_RE.sub("0", word) if normalize_digits else word
embedd_dict[word] = embedd
return embedd_dict, embedd_dim
else:
raise ValueError(
"embedding should choose from [word2vec, senna, glove, sskip, polyglot]"
)
def create_alphabets(alphabet_directory, train_path, data_paths=None, max_vocabulary_size=100000, embedd_dict=None,
min_occurrence=1, normalize_digits=True):
def expand_vocab():
vocab_set = set(vocab_list)
for data_path in data_paths:
with open(data_path, 'r') as file:
for line in file:
line = line.strip()
if len(line) == 0:
continue
tokens = line.split('\t')[0].split(' ')
for token in tokens:
word = DIGIT_RE.sub("0", token) if normalize_digits else token
if word not in vocab_set and (word in embedd_dict or word.lower() in embedd_dict):
vocab_set.add(word)
vocab_list.append(word)
logger = get_logger("Create Alphabets")
word_alphabet = Alphabet('word', singleton=True)
if not os.path.isdir(alphabet_directory):
logger.info("Creating Alphabets: %s" % alphabet_directory)
vocab = defaultdict(int)
with open(train_path, 'r') as file:
for line in file:
line = line.strip()
if len(line) == 0:
continue
tokens = line.split('\t')[0].split(' ')
for token in tokens:
word = DIGIT_RE.sub("0", token) if normalize_digits else token
vocab[word] += 1
# collect singletons
singletons = set([word for word, count in vocab.items() if count <= min_occurrence])
# if a singleton is in pretrained embedding dict, set the count to min_occur + c
if embedd_dict is not None:
assert isinstance(embedd_dict, OrderedDict)
for word in vocab.keys():
if word in embedd_dict or word.lower() in embedd_dict:
vocab[word] += min_occurrence
vocab_list = _START_VOCAB + sorted(vocab, key=vocab.get, reverse=True)
logger.info("Total Vocabulary Size: %d" % len(vocab_list))
logger.info("Total Singleton Size: %d" % len(singletons))
multi_vocab = [word for word in vocab_list if word in _START_VOCAB or vocab[word] > min_occurrence]
logger.info("Total Vocabulary Size (w.o rare words): %d" % len(multi_vocab))
if len(vocab_list) > max_vocabulary_size:
vocab_list = vocab_list[:max_vocabulary_size]
if data_paths is not None and embedd_dict is not None:
expand_vocab()
for word in vocab_list:
if word in multi_vocab:
word_alphabet.add(word)
elif word in singletons:
word_alphabet.add_singleton(word_alphabet.get_index(word))
else:
raise ValueError("Error word: " + word)
refiner = UNKRefiner(0, word_alphabet)
# TODO fix the pos here
for word in singletons:
unk_signature = refiner.refine(word, 0)
word_alphabet.add(unk_signature)
word_alphabet.save(alphabet_directory)
else:
word_alphabet.load(alphabet_directory)
word_alphabet.close()
logger.info("Word Alphabet Size (Singleton): %d (%d)" % (word_alphabet.size(), word_alphabet.singleton_size()))
return word_alphabet