qingqing01
8 years ago
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# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import paddle.v2.dataset.common
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import tarfile
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import gzip
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import itertools
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__all__ = ['test, get_dict', 'get_embedding']
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"""
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Conll 2005 dataset. Paddle semantic role labeling Book and demo use this
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dataset as an example. Because Conll 2005 is not free in public, the default
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downloaded URL is test set of Conll 2005 (which is public). Users can change
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URL and MD5 to their Conll dataset.
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"""
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DATA_URL = 'http://www.cs.upc.edu/~srlconll/conll05st-tests.tar.gz'
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DATA_MD5 = '387719152ae52d60422c016e92a742fc'
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WORDDICT_URL = 'http://paddlepaddle.bj.bcebos.com/demo/srl_dict_and_embedding/wordDict.txt'
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WORDDICT_MD5 = 'ea7fb7d4c75cc6254716f0177a506baa'
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VERBDICT_URL = 'http://paddlepaddle.bj.bcebos.com/demo/srl_dict_and_embedding/verbDict.txt'
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VERBDICT_MD5 = '0d2977293bbb6cbefab5b0f97db1e77c'
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TRGDICT_URL = 'http://paddlepaddle.bj.bcebos.com/demo/srl_dict_and_embedding/targetDict.txt'
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TRGDICT_MD5 = 'd8c7f03ceb5fc2e5a0fa7503a4353751'
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EMB_URL = 'http://paddlepaddle.bj.bcebos.com/demo/srl_dict_and_embedding/emb'
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EMB_MD5 = 'bf436eb0faa1f6f9103017f8be57cdb7'
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UNK_IDX = 0
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def load_dict(filename):
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d = dict()
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with open(filename, 'r') as f:
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for i, line in enumerate(f):
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d[line.strip()] = i
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return d
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def corpus_reader(data_path, words_name, props_name):
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"""
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Read one corpus. It returns an iterator. Each element of
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this iterator is a tuple including sentence and labels. The sentence is
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consist of a list of word IDs. The labels include a list of label IDs.
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:return: a iterator of data.
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:rtype: iterator
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"""
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def reader():
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tf = tarfile.open(data_path)
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wf = tf.extractfile(words_name)
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pf = tf.extractfile(props_name)
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with gzip.GzipFile(fileobj=wf) as words_file, gzip.GzipFile(
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fileobj=pf) as props_file:
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sentences = []
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labels = []
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one_seg = []
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for word, label in itertools.izip(words_file, props_file):
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word = word.strip()
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label = label.strip().split()
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if len(label) == 0: # end of sentence
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for i in xrange(len(one_seg[0])):
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a_kind_lable = [x[i] for x in one_seg]
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labels.append(a_kind_lable)
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if len(labels) >= 1:
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verb_list = []
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for x in labels[0]:
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if x != '-':
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verb_list.append(x)
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for i, lbl in enumerate(labels[1:]):
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cur_tag = 'O'
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is_in_bracket = False
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lbl_seq = []
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verb_word = ''
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for l in lbl:
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if l == '*' and is_in_bracket == False:
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lbl_seq.append('O')
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elif l == '*' and is_in_bracket == True:
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lbl_seq.append('I-' + cur_tag)
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elif l == '*)':
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lbl_seq.append('I-' + cur_tag)
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is_in_bracket = False
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elif l.find('(') != -1 and l.find(')') != -1:
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cur_tag = l[1:l.find('*')]
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lbl_seq.append('B-' + cur_tag)
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is_in_bracket = False
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elif l.find('(') != -1 and l.find(')') == -1:
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cur_tag = l[1:l.find('*')]
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lbl_seq.append('B-' + cur_tag)
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is_in_bracket = True
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else:
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raise RuntimeError('Unexpected label: %s' %
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l)
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yield sentences, verb_list[i], lbl_seq
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sentences = []
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labels = []
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one_seg = []
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else:
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sentences.append(word)
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one_seg.append(label)
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pf.close()
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wf.close()
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tf.close()
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return reader
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def reader_creator(corpus_reader,
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word_dict=None,
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predicate_dict=None,
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label_dict=None):
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def reader():
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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')
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mark = [0] * len(labels)
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if verb_index > 0:
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mark[verb_index - 1] = 1
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ctx_n1 = sentence[verb_index - 1]
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else:
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ctx_n1 = 'bos'
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if verb_index > 1:
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mark[verb_index - 2] = 1
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ctx_n2 = sentence[verb_index - 2]
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else:
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ctx_n2 = 'bos'
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mark[verb_index] = 1
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ctx_0 = sentence[verb_index]
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if verb_index < len(labels) - 1:
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mark[verb_index + 1] = 1
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ctx_p1 = sentence[verb_index + 1]
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else:
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ctx_p1 = 'eos'
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if verb_index < len(labels) - 2:
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mark[verb_index + 2] = 1
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ctx_p2 = sentence[verb_index + 2]
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else:
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ctx_p2 = 'eos'
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word_idx = [word_dict.get(w, UNK_IDX) for w in sentence]
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pred_idx = [predicate_dict.get(predicate)] * sen_len
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ctx_n2_idx = [word_dict.get(ctx_n2, UNK_IDX)] * sen_len
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ctx_n1_idx = [word_dict.get(ctx_n1, UNK_IDX)] * sen_len
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ctx_0_idx = [word_dict.get(ctx_0, UNK_IDX)] * sen_len
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ctx_p1_idx = [word_dict.get(ctx_p1, UNK_IDX)] * sen_len
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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, \
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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():
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word_dict = load_dict(
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common.download(WORDDICT_URL, 'conll05st', WORDDICT_MD5))
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verb_dict = load_dict(
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common.download(VERBDICT_URL, 'conll05st', VERBDICT_MD5))
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label_dict = load_dict(
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common.download(TRGDICT_URL, 'conll05st', TRGDICT_MD5))
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return word_dict, verb_dict, label_dict
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def get_embedding():
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return common.download(EMB_URL, 'conll05st', EMB_MD5)
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def test():
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word_dict, verb_dict, label_dict = get_dict()
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reader = corpus_reader(
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common.download(DATA_URL, 'conll05st', DATA_MD5),
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words_name='conll05st-release/test.wsj/words/test.wsj.words.gz',
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props_name='conll05st-release/test.wsj/props/test.wsj.props.gz')
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return reader_creator(reader, word_dict, verb_dict, label_dict)
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if __name__ == '__main__':
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print get_embedding()
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for f in test():
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print f
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