def preprocess_corpora(corpora, stopwords, allowed_pos, max_doc=float('inf'), no_above=0.5, no_below=1, keep_n=None):
"""
:rtype : gensim.corpora.dictionary.Dictionary
:param corpora:
:param stopwords:
:param allowed_pos:
:param max_doc:
:return:
"""
logging.info('Lemmatizing the corpora...')
count = 0
corpus_num = len(corpora)
processed_corpora = []
corpus_id2orig_id = []
for index, corpus in corpora.items():
count += 1
if count > max_doc:
break
if corpus is None: # skip if corpus is None
continue
print '\r', count, '/', corpus_num,
cleaned_corpus = clean_text(corpus) # delete irrelevant characters
corpus = []
tokens = lemmatize(content=cleaned_corpus, allowed_tags=allowed_pos)
for token in tokens:
word, pos = token.split('/')
corpus.append(word)
# convert compound word into one token
corpus = convert_compound(corpus)
# filter stop words, long words, and non-english words
corpus = [w for w in corpus if not w in stopwords and 2 <= len(w) <= 15 and w.islower()]
processed_corpora.append(corpus)
corpus_id2orig_id.append(index)
print '\n'
logging.info('Creating dictionary and corpus...')
dictionary = Dictionary(processed_corpora)
dictionary.corpus_id2orig_id = corpus_id2orig_id
logging.info('Filtering unimportant terms...')
dictionary.filter_extremes(no_below=no_below, no_above=no_above, keep_n=keep_n)
dictionary.compactify()
logging.info('Generating corpus...')
dictionary.corpus = [dictionary.doc2bow(corpus) for corpus in processed_corpora]
dictionary.id2token = revdict(dictionary.token2id)
return dictionary
