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# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved | ||
# | ||
# Licensed under the Apache License, Version 2.0 (the "License"); | ||
# you may not use this file except in compliance with the License. | ||
# You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
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import paddle.v2.dataset.common | ||
import tarfile | ||
import gzip | ||
import itertools | ||
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__all__ = ['test, get_dict', 'get_embedding'] | ||
""" | ||
Conll 2005 dataset. Paddle semantic role labeling Book and demo use this | ||
dataset as an example. Because Conll 2005 is not free in public, the default | ||
downloaded URL is test set of Conll 2005 (which is public). Users can change | ||
URL and MD5 to their Conll dataset. | ||
""" | ||
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DATA_URL = 'http://www.cs.upc.edu/~srlconll/conll05st-tests.tar.gz' | ||
DATA_MD5 = '387719152ae52d60422c016e92a742fc' | ||
WORDDICT_URL = 'http://paddlepaddle.bj.bcebos.com/demo/srl_dict_and_embedding/wordDict.txt' | ||
WORDDICT_MD5 = 'ea7fb7d4c75cc6254716f0177a506baa' | ||
VERBDICT_URL = 'http://paddlepaddle.bj.bcebos.com/demo/srl_dict_and_embedding/verbDict.txt' | ||
VERBDICT_MD5 = '0d2977293bbb6cbefab5b0f97db1e77c' | ||
TRGDICT_URL = 'http://paddlepaddle.bj.bcebos.com/demo/srl_dict_and_embedding/targetDict.txt' | ||
TRGDICT_MD5 = 'd8c7f03ceb5fc2e5a0fa7503a4353751' | ||
EMB_URL = 'http://paddlepaddle.bj.bcebos.com/demo/srl_dict_and_embedding/emb' | ||
EMB_MD5 = 'bf436eb0faa1f6f9103017f8be57cdb7' | ||
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UNK_IDX = 0 | ||
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def load_dict(filename): | ||
d = dict() | ||
with open(filename, 'r') as f: | ||
for i, line in enumerate(f): | ||
d[line.strip()] = i | ||
return d | ||
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def corpus_reader(data_path, words_name, props_name): | ||
""" | ||
Read one corpus. It returns an iterator. Each element of | ||
this iterator is a tuple including sentence and labels. The sentence is | ||
consist of a list of word IDs. The labels include a list of label IDs. | ||
:return: a iterator of data. | ||
:rtype: iterator | ||
""" | ||
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def reader(): | ||
tf = tarfile.open(data_path) | ||
wf = tf.extractfile(words_name) | ||
pf = tf.extractfile(props_name) | ||
with gzip.GzipFile(fileobj=wf) as words_file, gzip.GzipFile( | ||
fileobj=pf) as props_file: | ||
sentences = [] | ||
labels = [] | ||
one_seg = [] | ||
for word, label in itertools.izip(words_file, props_file): | ||
word = word.strip() | ||
label = label.strip().split() | ||
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if len(label) == 0: # end of sentence | ||
for i in xrange(len(one_seg[0])): | ||
a_kind_lable = [x[i] for x in one_seg] | ||
labels.append(a_kind_lable) | ||
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if len(labels) >= 1: | ||
verb_list = [] | ||
for x in labels[0]: | ||
if x != '-': | ||
verb_list.append(x) | ||
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for i, lbl in enumerate(labels[1:]): | ||
cur_tag = 'O' | ||
is_in_bracket = False | ||
lbl_seq = [] | ||
verb_word = '' | ||
for l in lbl: | ||
if l == '*' and is_in_bracket == False: | ||
lbl_seq.append('O') | ||
elif l == '*' and is_in_bracket == True: | ||
lbl_seq.append('I-' + cur_tag) | ||
elif l == '*)': | ||
lbl_seq.append('I-' + cur_tag) | ||
is_in_bracket = False | ||
elif l.find('(') != -1 and l.find(')') != -1: | ||
cur_tag = l[1:l.find('*')] | ||
lbl_seq.append('B-' + cur_tag) | ||
is_in_bracket = False | ||
elif l.find('(') != -1 and l.find(')') == -1: | ||
cur_tag = l[1:l.find('*')] | ||
lbl_seq.append('B-' + cur_tag) | ||
is_in_bracket = True | ||
else: | ||
raise RuntimeError('Unexpected label: %s' % | ||
l) | ||
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yield sentences, verb_list[i], lbl_seq | ||
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sentences = [] | ||
labels = [] | ||
one_seg = [] | ||
else: | ||
sentences.append(word) | ||
one_seg.append(label) | ||
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pf.close() | ||
wf.close() | ||
tf.close() | ||
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return reader | ||
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def reader_creator(corpus_reader, | ||
word_dict=None, | ||
predicate_dict=None, | ||
label_dict=None): | ||
def reader(): | ||
for sentence, predicate, labels in corpus_reader(): | ||
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sen_len = len(sentence) | ||
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verb_index = labels.index('B-V') | ||
mark = [0] * len(labels) | ||
if verb_index > 0: | ||
mark[verb_index - 1] = 1 | ||
ctx_n1 = sentence[verb_index - 1] | ||
else: | ||
ctx_n1 = 'bos' | ||
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if verb_index > 1: | ||
mark[verb_index - 2] = 1 | ||
ctx_n2 = sentence[verb_index - 2] | ||
else: | ||
ctx_n2 = 'bos' | ||
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mark[verb_index] = 1 | ||
ctx_0 = sentence[verb_index] | ||
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if verb_index < len(labels) - 1: | ||
mark[verb_index + 1] = 1 | ||
ctx_p1 = sentence[verb_index + 1] | ||
else: | ||
ctx_p1 = 'eos' | ||
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if verb_index < len(labels) - 2: | ||
mark[verb_index + 2] = 1 | ||
ctx_p2 = sentence[verb_index + 2] | ||
else: | ||
ctx_p2 = 'eos' | ||
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word_idx = [word_dict.get(w, UNK_IDX) for w in sentence] | ||
pred_idx = [predicate_dict.get(predicate)] * sen_len | ||
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ctx_n2_idx = [word_dict.get(ctx_n2, UNK_IDX)] * sen_len | ||
ctx_n1_idx = [word_dict.get(ctx_n1, UNK_IDX)] * sen_len | ||
ctx_0_idx = [word_dict.get(ctx_0, UNK_IDX)] * sen_len | ||
ctx_p1_idx = [word_dict.get(ctx_p1, UNK_IDX)] * sen_len | ||
ctx_p2_idx = [word_dict.get(ctx_p2, UNK_IDX)] * sen_len | ||
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label_idx = [label_dict.get(w) for w in labels] | ||
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yield word_idx, pred_idx, ctx_n2_idx, ctx_n1_idx, \ | ||
ctx_0_idx, ctx_p1_idx, ctx_p2_idx, mark, label_idx | ||
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return reader() | ||
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def get_dict(): | ||
word_dict = load_dict( | ||
common.download(WORDDICT_URL, 'conll05st', WORDDICT_MD5)) | ||
verb_dict = load_dict( | ||
common.download(VERBDICT_URL, 'conll05st', VERBDICT_MD5)) | ||
label_dict = load_dict( | ||
common.download(TRGDICT_URL, 'conll05st', TRGDICT_MD5)) | ||
return word_dict, verb_dict, label_dict | ||
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def get_embedding(): | ||
return common.download(EMB_URL, 'conll05st', EMB_MD5) | ||
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def test(): | ||
word_dict, verb_dict, label_dict = get_dict() | ||
reader = corpus_reader( | ||
common.download(DATA_URL, 'conll05st', DATA_MD5), | ||
words_name='conll05st-release/test.wsj/words/test.wsj.words.gz', | ||
props_name='conll05st-release/test.wsj/props/test.wsj.props.gz') | ||
return reader_creator(reader, word_dict, verb_dict, label_dict) | ||
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if __name__ == '__main__': | ||
print get_embedding() | ||
for f in test(): | ||
print f |
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