def augment_with_pretrained(dictionary, ext_emb_path, words):
"""
Augment the dictionary with words that have a pretrained embedding.
If `words` is None, we add every word that has a pretrained embedding
to the dictionary, otherwise, we only add the words that are given by
`words` (typically the words in the development and test sets.)
"""
print('Loading pre-trained embeddings from {}...'.format(ext_emb_path))
assert os.path.isfile(ext_emb_path), '{} not found'.format(ext_emb_path)
# Load pretrained embeddings from file
pretrained = set([
line.rstrip().split()[0].strip()
for line in codecs.open(ext_emb_path, 'r', 'utf-8')
if len(ext_emb_path) > 0
])
# We either add every word in the pretrained file,
# or only words given in the `words` list to which
# we can assign a pretrained embedding
if words is None:
for word in pretrained:
if word not in dictionary:
dictionary[word] = 0
else:
for word in words:
if any(
x in pretrained for x in
[word, word.lower(),
re.sub('\d', '0', word.lower())]) and word not in dictionary:
dictionary[word] = 0
word_to_id, id_to_word = create_mapping(dictionary)
return dictionary, word_to_id, id_to_word
def tag_mapping(sentences, idx=-1):
"""
Create a dictionary and a mapping of tags, sorted by frequency.
"""
tags = [[word[idx] for word in s] for s in sentences]
dico = create_dico(tags)
tag_to_id, id_to_tag = create_mapping(dico)
print("Found {} unique named entity tags".format(len(dico)))
return dico, tag_to_id, id_to_tag
def pos_mapping(sentences):
"""
Create a dictionary and a mapping of POS tags, sorted by frequency.
"""
tags = [[word[2] for word in s] for s in sentences]
dico = create_dico(tags)
dico['<UNK>'] = 10000000
tag_to_id, id_to_tag = create_mapping(dico)
print("Found {} unique POS tags".format(len(dico)))
return dico, tag_to_id, id_to_tag
def char_mapping(sentences):
"""
Create a dictionary and mapping of characters, sorted by frequency.
"""
chars = ["".join([w[1] for w in s]) for s in sentences]
dico = create_dico(chars)
dico['<UNK>'] = 10000000
char_to_id, id_to_char = create_mapping(dico)
print("Found {} unique characters".format(len(dico)))
return dico, char_to_id, id_to_char
def word_mapping(sentences, lower):
"""
Create a dictionary and a mapping of words, sorted by frequency.
"""
words = [[x[1].lower() if lower else x[1] for x in s] for s in sentences]
dico = create_dico(words)
dico['<UNK>'] = 10000000
dico['<PADDING>'] = 500000
word_to_id, id_to_word = create_mapping(dico)
print('Found {} unique words ({} in total)'.format(
len(dico), sum(len(x) for x in words)))
return dico, word_to_id, id_to_word
def segment_mapping(sentences, only_i_o_tag=True):
"""
Create a dictionary and a mapping of ner segmentation tags, sorted by frequency.
"""
if only_i_o_tag:
tags = [[word[4].replace('B-', 'I-') for word in s] for s in sentences]
else:
tags = [[word[4] for word in s] for s in sentences]
dico = create_dico(tags)
tag_to_id, id_to_tag = create_mapping(dico)
print("Found {} unique segmentation tags".format(len(dico)))
return dico, tag_to_id, id_to_tag