def extract_encoded_text(encoded, sent_tokeniser, word_tokeniser,
morph_analyser):
if not sent_tokeniser:
return encoded
proc_sent = ExternalTextProcessor(sent_tokeniser.split())
content = base64.b64decode(encoded).decode("utf-8").replace("\t", " ")
tokenized_segs = proc_sent.process(content).strip()
tokenized_filtered = ""
for sent in tokenized_segs.split("\n"):
if sum([1 for m in sent if m in string.punctuation + string.digits
]) < len(sent) // 2:
tokenized_filtered += sent + "\n"
if not word_tokeniser:
b64text = base64.b64encode(tokenized_filtered.lower().encode("utf-8"))
return b64text.decode()
proc_word = ExternalTextProcessor(word_tokeniser.split())
tokenized_text = proc_word.process(tokenized_filtered)
if morph_analyser:
proc_morph = ExternalTextProcessor(morph_analyser.split())
tokenized_text = proc_morph.process(tokenized_text)
b64text = base64.b64encode(tokenized_text.lower().encode("utf-8"))
return b64text.decode()
def extract_encoded_text(encodedtext, tmp_file, tmp_file_origtext, morphanal,
sent_tokeniser, word_tokeniser):
proc_sent = ExternalTextProcessor(sent_tokeniser.split(' '))
proc_word = ExternalTextProcessor(word_tokeniser.split(' '))
content = base64.b64decode(encodedtext).decode("utf-8").replace("\t", " ")
tokenized_segs = proc_sent.process(content).strip()
tmp_file_origtext.write(tokenized_segs.encode())
tokenized_text = proc_word.process(tokenized_segs)
if morphanal is not None:
morphanalyser = ["/bin/bash", morphanal]
tokenized_text = run_analyse(morphanalyser, tokenized_text)
tmp_file.write(tokenized_text.lower().encode())
def sent_tokenize(text, senttok):
proc_sent = ExternalTextProcessor(senttok.split(' '))
# content = base64.b64decode(text).decode("utf-8").replace("\t", " ")
content = text.replace("\t", " ")
sents = proc_sent.process(content).strip()
sents = [s.strip() for s in sents.split("\n") if s.strip()]
return sents
def word_tokenize(sents, wordtok):
proc_word = ExternalTextProcessor(wordtok.split(' '))
ret = []
for sent in sents:
words = proc_word.process(sent)
ret.append(words.strip())
return ret
def extract_encoded_text(encoded, sent_tokeniser, word_tokeniser,
morph_analyser):
if not sent_tokeniser:
return encoded
content = base64.b64decode(encoded).decode("utf-8").replace("\t", " ")
tokenized_segs = []
seg = ""
sent_tokeniser.writeline(html.escape(content.strip()) + "\n")
while seg != "<P>":
seg = sent_tokeniser.readline().strip()
if seg != "" and seg != "<P>":
tokenized_segs.append(html.unescape(seg))
tokenized_filtered = []
for sent in tokenized_segs:
if sum([1 for m in sent if m in string.punctuation + string.digits
]) < len(sent) // 2:
tokenized_filtered.append(sent)
if not word_tokeniser:
b64text = base64.b64encode(tokenized_filtered.lower().encode("utf-8"))
return b64text.decode()
tokenized_text = ""
for sent in tokenized_filtered:
word_tokeniser.writeline(sent)
tokenized_text = tokenized_text + word_tokeniser.readline().strip(
) + "\n"
if morph_analyser:
proc_morph = ExternalTextProcessor(
morph_analyser.split()) # Apertium does line buffering
tokenized_text = proc_morph.process(tokenized_text)
b64text = base64.b64encode(tokenized_text.lower().encode("utf-8"))
return b64text.decode()
def split_sentences(original_text, sentence_splitter_cmd):
if sentence_splitter_cmd:
proc = ExternalTextProcessor(sentence_splitter_cmd.split())
text_split = proc.process(original_text.replace("\n\n", "\n"))
else:
text_split = original_text.replace("\n\n", "\n")
output = html.unescape(text_split)
return [n for n in output.split("\n") if filter_digits_and_punctuation(n)]
def ngrams_from_text(n, hash_values, ignore_set, word_tokeniser_cmd, page):
proc = ExternalTextProcessor(word_tokeniser_cmd.split(' '))
segments = proc.process(page).split("\n")
words = []
for s in segments:
words.extend(s.split(' '))
ngrams = _ngram_helper(words, n, hash_values)
if ignore_set:
return [ng for ng in ngrams if ng not in ignore_set]
return ngrams
def extract_encoded_text(encodedtext, encodedtokenized, tmp_file,
tmp_file_origtext, sent_tokeniser):
proc_sent = ExternalTextProcessor(sent_tokeniser.split(' '))
content = base64.b64decode(encodedtext).decode("utf-8").replace("\t", " ")
tokenized_segs = proc_sent.process(content).strip()
tokenized_filtered = ""
for sent in tokenized_segs.split("\n"):
if sum([1 for m in sent if m in string.punctuation + string.digits
]) < len(sent) // 2:
tokenized_filtered += sent + "\n"
tmp_file_origtext.write(tokenized_filtered.encode())
content_tokenized = base64.b64decode(encodedtokenized)
tmp_file.write(content_tokenized)
def langsplit(uri, langsplit_exec, text):
cmd = [langsplit_exec, "--printchunks"]
proc = ExternalTextProcessor(cmd)
tld = uri.split("/")[0].split(".")[-1]
header = u"%s tld:%s uri:%s\n" % (magic_numer, tld, uri)
output = proc.process(u"\n".join([header, text]))
if not output.strip():
res = langid.classify(text)
lang = res[0]
header = "%s\tlanguage:%s\tbytes:%d\n" % (header.rstrip(), lang,
len(text.encode("utf-8")))
return header + text
return output
def split_sentences(text, sentence_splitter_cmd, lang):
if not sentence_splitter_cmd:
return text.split('\n')
sentences = []
proc = ExternalTextProcessor([sentence_splitter_cmd, "-l", lang])
output = proc.process(text.replace("\n", "\n\n"))
for line in output.split("\n"):
line = line.strip()
if not line or line == "<P>":
continue
sentences.append(line)
return sentences
def split_sentences(text, sentence_splitter_cmd, lang):
if not sentence_splitter_cmd:
return text.split('\n')
sentences = []
proc = ExternalTextProcessor([sentence_splitter_cmd, "-l", lang])
output = proc.process(text.replace("\n", "\n\n"))
for line in output.split("\n"):
line = line.strip()
if not line or line == "<P>":
continue
sentences.append(line)
return sentences
def langsplit(uri, langsplit_exec, text):
cmd = [langsplit_exec, "--printchunks"]
proc = ExternalTextProcessor(cmd)
tld = uri.split("/")[0].split(".")[-1]
header = u"%s tld:%s uri:%s\n" % (magic_numer, tld, uri)
output = proc.process(u"\n".join([header, text]))
if not output.strip():
res = langid.classify(text)
lang = res[0]
header = "%s\tlanguage:%s\tbytes:%d\n" % (header.rstrip(),
lang,
len(text.encode("utf-8")))
return header + text
return output
def langsplit(uri, text,
langsplit_exec="/home/buck/net/build/mtma_bitext/html_convert/langsplit"):
cmd = [langsplit_exec, "--printchunks"]
proc = ExternalTextProcessor(cmd)
tld = uri.split("/")[0].split(".")[-1]
header = u"%s tld:%s uri:%s\n" % (magic_number, tld, uri)
output = proc.process(u"\n".join([header, text]))
if not output.strip():
import langid
res = langid.classify(text)
lang = res[0]
header = "%s\tlanguage:%s\tbytes:%d\n" % (header.rstrip(),
lang,
len(text.encode("utf-8")))
return header + text
return output
def split_sentences(original_text, sentence_splitter_cmd, prune_type,
prune_threshold):
# print("original_text", len(original_text))
proc = ExternalTextProcessor(sentence_splitter_cmd.split())
tmp1 = original_text.replace("\n\n", "\n")
# print("tmp1", len(tmp1))
tmp2 = proc.process(tmp1)
# print("tmp2", len(tmp2))
tmp3 = html.unescape(tmp2)
# print("tmp3", len(tmp3))
tmp4 = [n for n in tmp3.split("\n") if filter_digits_and_punctuation(n)]
# print("tmp4", len(tmp4))
tmp5 = []
count = 0
for extracted_line in tmp4:
extracted_line = extracted_line.strip()
if not extracted_line:
# print("empty line")
continue
if prune_type == "chars":
if len(extracted_line) > prune_threshold:
continue
elif prune_type == "words":
if len(extracted_line.split()) > prune_threshold:
continue
tmp5.append(extracted_line)
count += 1
# print("tmp5", len(tmp5))
return tmp5
def write_sentences(html, lang, sent_tokenizer, outfile, lid=None):
html = base64.b64decode(html).decode("utf8")
tagchunks = parsers.parse(html, lang).split("\n")
chunks = [
re_space.sub(" ", tc).strip() for tc in tagchunks
if not re_tag.match(tc.strip())
]
proc_sent = ExternalTextProcessor(sent_tokenizer.split(' '))
dedup = set()
n_sents = 0
for chunk in chunks:
if chunk.strip():
if lid:
pred = lid.predict([chunk])[0]
if pred[0][0][9:] != lang:
continue
tokenized_segs = proc_sent.process(chunk).strip()
for sent in tokenized_segs.split("\n"):
if sent not in dedup:
print(sent, file=outfile)
dedup.add(sent)
n_sents += 1
return n_sents
def normalize(text, normalizer_cmd, lang):
proc = ExternalTextProcessor([normalizer_cmd, lang])
output = proc.process(text.strip())
return output
def tokenize(text, tokenizer_cmd, lang):
proc = ExternalTextProcessor([tokenizer_cmd, "-a", "-l", lang])
output = proc.process(text.strip())
return output
def split_sentences(text, sentence_splitter_cmd, lang):
proc = ExternalTextProcessor([sentence_splitter_cmd, "-l", lang])
output = proc.process(text.replace("\n\n", "\n"))
return [n for n in output.split("\n") if filter_digits_and_punctuation(n)]
def tokenize(text, tokenizer_cmd, lang):
proc = ExternalTextProcessor([tokenizer_cmd, "-a", "-l", lang])
output = proc.process(text.strip())
return output
def normalize(text, normalizer_cmd, lang):
proc = ExternalTextProcessor([normalizer_cmd, lang])
output = proc.process(text.strip())
return output
stderr=subprocess.PIPE,
stdin=subprocess.PIPE)
morph_stdout, error = tpmorph.communicate(input=text)
if len(error.strip()) == 0:
text = re.sub(
r"\^\*?", r"",
re.sub(r"[/<][^$]*\$", r"",
morph_stdout.decode("utf-8")))
# Getting the bag of words in the document
if lang == options.lang1:
proc = ExternalTextProcessor(options.wordtokeniser1.split(' '))
elif lang == options.lang2:
proc = ExternalTextProcessor(options.wordtokeniser2.split(' '))
sorted_uniq_wordlist = set(proc.process(text).lower().split())
# Trimming non-aplphanumerics:
clean_sorted_uniq_wordlist = [
_f
for _f in [w.strip(punctuation) for w in sorted_uniq_wordlist]
if _f
]
sorted_uniq_wordlist = clean_sorted_uniq_wordlist
for word in sorted_uniq_wordlist:
if lang in word_map:
if word in word_map[lang]:
word_map[lang][word].append(docnumber)
else:
word_map[lang][word] = []
word_map[lang][word].append(docnumber)