def augment_with_pretrained(dictionary, ext_emb_path, chars):
"""
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 pretrained embeddings from %s...' % ext_emb_path)
assert os.path.isfile(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 chars is None:
for char in pretrained:
if char not in dictionary:
dictionary[char] = 0
else:
for char in chars:
if any(
x in pretrained for x in
[char, char.lower(),
re.sub('\d', '0', char.lower())]) and char not in dictionary:
dictionary[char] = 0
word_to_id, id_to_word = create_mapping(dictionary)
return dictionary, word_to_id, id_to_word
def tag_mapping(sentences):
"""
Create a dictionary and a mapping of tags, sorted by frequency.
"""
tags = [[char[-1] for char in s] for s in sentences]
dico = create_dico(tags)
tag_to_id, id_to_tag = create_mapping(dico)
print("Found %i unique named entity tags" % len(dico))
return dico, tag_to_id, id_to_tag
def char_mapping(sentences, lower):
"""
Create a dictionary and a mapping of words, sorted by frequency.
"""
chars = [[x[0].lower() if lower else x[0] for x in s] for s in sentences]
dico = create_dico(chars)
dico["<PAD>"] = 10000001
dico['<UNK>'] = 10000000
char_to_id, id_to_char = create_mapping(dico)
print("Found %i unique words (%i in total)" %
(len(dico), sum(len(x) for x in chars)))
return dico, char_to_id, id_to_char