# 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. """ Conll05 dataset. Paddle semantic role labeling Book and demo use this dataset as an example. Because Conll05 is not free in public, the default downloaded URL is test set of Conll05 (which is public). Users can change URL and MD5 to their Conll dataset. And a pre-trained word vector model based on Wikipedia corpus is used to initialize SRL model. """ import tarfile import gzip import itertools import paddle.dataset.common from six.moves import zip, range __all__ = ['test, get_dict', 'get_embedding', 'convert'] 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' UNK_IDX = 0 def load_label_dict(filename): d = dict() tag_dict = set() with open(filename, 'r') as f: for i, line in enumerate(f): line = line.strip() if line.startswith("B-"): tag_dict.add(line[2:]) elif line.startswith("I-"): tag_dict.add(line[2:]) index = 0 for tag in tag_dict: d["B-" + tag] = index index += 1 d["I-" + tag] = index index += 1 d["O"] = index return d def load_dict(filename): d = dict() with open(filename, 'r') as f: for i, line in enumerate(f): d[line.strip()] = i return d 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 """ 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 zip(words_file, props_file): word = word.strip() label = label.strip().split() if len(label) == 0: # end of sentence for i in range(len(one_seg[0])): a_kind_lable = [x[i] for x in one_seg] labels.append(a_kind_lable) if len(labels) >= 1: verb_list = [] for x in labels[0]: if x != '-': verb_list.append(x) 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) yield sentences, verb_list[i], lbl_seq sentences = [] labels = [] one_seg = [] else: sentences.append(word) one_seg.append(label) pf.close() wf.close() tf.close() return reader def reader_creator(corpus_reader, word_dict=None, predicate_dict=None, label_dict=None): def reader(): for sentence, predicate, labels in corpus_reader(): sen_len = len(sentence) 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' if verb_index > 1: mark[verb_index - 2] = 1 ctx_n2 = sentence[verb_index - 2] else: ctx_n2 = 'bos' mark[verb_index] = 1 ctx_0 = sentence[verb_index] if verb_index < len(labels) - 1: mark[verb_index + 1] = 1 ctx_p1 = sentence[verb_index + 1] else: ctx_p1 = 'eos' if verb_index < len(labels) - 2: mark[verb_index + 2] = 1 ctx_p2 = sentence[verb_index + 2] else: ctx_p2 = 'eos' word_idx = [word_dict.get(w, UNK_IDX) for w in sentence] 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 pred_idx = [predicate_dict.get(predicate)] * sen_len label_idx = [label_dict.get(w) for w in labels] yield word_idx, ctx_n2_idx, ctx_n1_idx, \ ctx_0_idx, ctx_p1_idx, ctx_p2_idx, pred_idx, mark, label_idx return reader def get_dict(): """ Get the word, verb and label dictionary of Wikipedia corpus. """ word_dict = load_dict( paddle.dataset.common.download(WORDDICT_URL, 'conll05st', WORDDICT_MD5)) verb_dict = load_dict( paddle.dataset.common.download(VERBDICT_URL, 'conll05st', VERBDICT_MD5)) label_dict = load_label_dict( paddle.dataset.common.download(TRGDICT_URL, 'conll05st', TRGDICT_MD5)) return word_dict, verb_dict, label_dict def get_embedding(): """ Get the trained word vector based on Wikipedia corpus. """ return paddle.dataset.common.download(EMB_URL, 'conll05st', EMB_MD5) def test(): """ Conll05 test set creator. Because the training dataset is not free, the test dataset is used for training. It returns a reader creator, each sample in the reader is nine features, including sentence sequence, predicate, predicate context, predicate context flag and tagged sequence. :return: Training reader creator :rtype: callable """ word_dict, verb_dict, label_dict = get_dict() reader = corpus_reader( paddle.dataset.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) def fetch(): paddle.dataset.common.download(WORDDICT_URL, 'conll05st', WORDDICT_MD5) paddle.dataset.common.download(VERBDICT_URL, 'conll05st', VERBDICT_MD5) paddle.dataset.common.download(TRGDICT_URL, 'conll05st', TRGDICT_MD5) paddle.dataset.common.download(EMB_URL, 'conll05st', EMB_MD5) paddle.dataset.common.download(DATA_URL, 'conll05st', DATA_MD5) def convert(path): """ Converts dataset to recordio format """ paddle.dataset.common.convert(path, test(), 1000, "conl105_train") paddle.dataset.common.convert(path, test(), 1000, "conl105_test")