def exe_extract_windows(argv):
topic_path, judge_path, text_db_path, windows_db_path = argv;
text_db = bsddb.hashopen(text_db_path);
window_db = bsddb.hashopen(windows_db_path, 'w');
judge_file = QRelFile(judge_path);
topics = StandardFormat().read(topic_path);
topic_chain = TextChain([TextTokenizer(word_tokenize), TextStopRemover('data/stoplist.dft'), TextStemmer(EnglishStemmer()), TextTokenNormalizer()]);
sentence_chain = TextChain([TextTokenizer(word_tokenize), TextStemmer(EnglishStemmer()), TextTokenNormalizer()]);
for topic_id, topic_str in topics.items():
print topic_id;
sys.stdout.flush();
topic = TextPiece(topic_str);
topic_chain.work(topic);
if not judge_file.has_key(topic_id):
continue;
docnos = judge_file[topic_id].keys();
for docno in docnos:
if not is_cluewebB(docno):
continue;
doc_text = text_db[docno];
window_candidates = match_window(topic, doc_text, sentence_chain);
sentences = map(lambda text_piece: text_piece.text, window_candidates);
text = '\n'.join(sentences);
window_db[docno] = text.encode('utf8');
window_db.close();
def exe_extract_words(article_path, topic_path, out_word_path):
term_set = set()
topics = StandardFormat().read(topic_path)
for topic_id, topic_string in topics.items():
topic = complete_text_work(topic_string)
for token in topic.tokens:
term_set.add(token)
reader = TRECReader()
reader.open(article_path)
doc = reader.next()
while doc:
print doc.ID, len(term_set)
text = complete_text_work(doc.text)
for token in text.tokens:
term_set.add(token)
doc = reader.next()
print 'writing.....'
word_list_file = open(out_word_path, 'w');
words = list(term_set)
words.sort();
map(lambda word:word_list_file.write('%s\n' % word), words);
word_list_file.close();
def exe_extract_feature(argv):
window_path, doc_path, topic_path, judge_path, word_stat_path, out_path = argv;
judge_file = QRelFile(judge_path);
topics = StandardFormat().read(topic_path);
global window_db, doc_db, word_stat, model_factory;
window_db = bsddb.hashopen(window_path);
doc_db = bsddb.hashopen(doc_path);
word_stat = load_word_stat(word_stat_path);
model_factory = DocumentModelFactory(word_stat);
writer = open(out_path, 'w');
global topic_chain, window_chain, doc_chain;
topic_chain = TextChain([TextTokenizer(word_tokenize), TextStopRemover(stop_path), TextStemmer(stemmer), TextModeler(model_factory)]);
window_chain = topic_chain;
doc_chain = TextChain([TextTokenizer(word_tokenize),TextStopRemover(stop_path), TextStemmer(stemmer), TextModeler(model_factory), WindowWorker(window_chain), DocumentTitleWorker(topic_chain)])
global topic_id;
topic_ids = judge_file.keys();
for topic_id in topic_ids:
if not topics.has_key(topic_id):
continue;
topic_str = topics[topic_id];
print topic_id;
global topic;
topic = TextPiece(topic_str);
topic_chain.work(topic);
p = Pool(task_num);
lines_group = p.map(multithread_extract_feature, judge_file[topic_id].items());
for lines in lines_group:
for line in lines:
writer.write(line);
writer.write('\n');
writer.close();
def exe_extract_topic_words(argv):
from nltk.tokenize import word_tokenize;
topic_path, word_list_path = argv;
trec_format = StandardFormat();
word_list_file = open(word_list_path, 'w');
topics = trec_format.read(topic_path);
word_set = set();
for topic_id, topic_text in topics.items():
words = map(lambda word: word.lower(), word_tokenize(topic_text));
word_set.update(words);
word_list_file.write('\n'.join(word_set));
word_list_file.close();
import sys
from JudgeFile import QRelFile
from TRECTopics import StandardFormat
from TrainGenerator import *;
topics = StandardFormat().read(sys.argv[1]);
judge_file = QRelFile(sys.argv[2])
lemmas = set()
topic_ids = judge_file.keys()
for topic_id in topic_ids:
if not topics.has_key(topic_id):
continue
topic_str = topics[topic_id]
topic = TextPiece(topic_str)
lemmas.update(topic.lemmas)
for lemma in lemmas:
print lemma
