def preprocess(self):
tokenizedTweets_writer = open(
'./daten/tokenized_tweets_normalized.txt', 'w')
preprocTweets_writer = open(
'./daten/preprocessed_tweets_normalized.txt', 'w')
pp = Pipeline(self.this_file, "./autosarkasmus/rsrc/de-tiger.map")
tweets_tkn, tweets_proc, labels = pp.process()
assert (len(tweets_tkn) == len(tweets_proc) == len(labels))
# filter stopwords + normalize tokens
lemmatizer = IWNLPWrapper(
lemmatizer_path='daten/IWNLP.Lemmatizer_20170501.json')
lemmatized_tokens = []
for x in range(len(tweets_tkn)):
tweet = []
for token in tweets_tkn[x]:
if token.lower() in stopwords.words('german'):
continue
try:
lemma = lemmatizer.lemmatize_plain(token, ignore_case=True)
if (lemma):
tweet.append(lemma[0])
else:
tweet.append(token)
except Exception as e:
print(e)
lemmatized_tokens.append(tweet)
assert (len(lemmatized_tokens) == len(tweets_proc) == len(labels))
# write preprocessing results to file
for x in range(len(lemmatized_tokens)):
t_tweet = (" ").join(lemmatized_tokens[x])
p_tweet = (" ").join(
[str(x) + "/" + str(y) for x, y in tweets_proc[x]])
label = labels[x]
tokenizedTweets_writer.write(t_tweet + "\t" + label + "\n")
preprocTweets_writer.write(p_tweet + "\t" + label + "\n")
class spaCyIWNLP(object):
def __init__(self,
lemmatizer_path,
use_plain_lemmatization=False,
ignore_case=False):
self.lemmatizer = IWNLPWrapper(lemmatizer_path=lemmatizer_path)
self.use_plain_lemmatization = use_plain_lemmatization
self.ignore_case = ignore_case
Token.set_extension('iwnlp_lemmas', getter=self.get_lemmas, force=True)
def __call__(self, doc):
for token in doc:
token._.iwnlp_lemmas = self.get_lemmas(token)
return doc
def get_lemmas(self, token):
if self.use_plain_lemmatization:
return self.lemmatizer.lemmatize_plain(
token.text, ignore_case=self.ignore_case)
else:
return self.lemmatizer.lemmatize(token.text,
pos_universal_google=token.pos_)
class LemmatizerPlus(object):
def __init__(self, lemmatizer_path, nlp):
self.lemmatizer = IWNLPWrapper(lemmatizer_path=lemmatizer_path)
self.stringstore = nlp.vocab.strings
# self.matcher = PhraseMatcher(nlp.vocab)
Token.set_extension('iwnlp_lemmas', getter=self.lemmatize, force=True)
self.lookup = {
('fast', ADV): 'fast',
}
def __call__(self, doc):
for token in doc:
token._.iwnlp_lemmas = self.lemmatize(token)
return doc
def lemmatize(self, token):
"""
TODO: This doc is slightly outdated
This function uses the IWNLP lemmatizer with a few enhancements for compund nouns and nouns
with uncommon capitalization. Can also be used to lemmatize tokens with different POS-tags.
Do not use this function to lemmatize phrases.
:param token: white space stripped single token (str)
:return: str # TODO: tuple of type (str, bool)
value[0]: The lemma of the token if a lemma can be derived, else None.
# TODO: value[1]: True if the token can be retrieved from the Wiktionary database as is,
# else False.
"""
text = token.text.strip()
pos = token.pos_
# nothing to lemmatize here
if pos in {PHRASE, NPHRASE, PUNCT, SPACE, SYM}:
return text
# lemmatiaztions are odd on DET and NUM, so better leave it alone
if pos in {DET, NUM}:
return None
# Wiktionary has no POS PROPN
if pos == PROPN:
pos = NOUN
# first lookup token for given POS in dictionary
if (text, pos) in self.lookup:
return self.lookup[(text, pos)]
value = None
# default IWNLP lemmatization
lemm = self.lemmatizer.lemmatize(text, pos)
# default lemmatization hit?
if lemm:
value = lemm[0]
# default lemmatization miss?
# apply some rules to derive a lemma from the original token (nouns only)
elif pos == NOUN:
# first try default noun capitalization
lemm = self.lemmatizer.lemmatize(text.title(), pos)
if lemm:
value = lemm[0]
else:
# still no results: try all noun suffixes
# TODO: search for a more efficient implementation
text_low = text.lower()
tolerance = 3
for i in range(1, len(text) - tolerance):
# looks ugly, but avoids full captitalization
text_edit = text_low[i].upper() + text_low[i + 1:]
lemm = self.lemmatizer.lemmatize(text_edit, pos)
if lemm:
value = (text[:i] + lemm[0]).title()
break
# last try: plain lemmatization for all remaining POS tags
else:
lemm = self.lemmatizer.lemmatize_plain(text, ignore_case=True)
if lemm:
value = lemm[0]
if value and pos in {
ADJ, ADP, ADV, AUX, CCONJ, CONJ, INTJ, PART, PRON, SCONJ, VERB
}:
value = value.lower()
if value:
self.stringstore.add(value)
self.lookup[(text, pos)] = value
return value
class IWNLPWrapperTest(unittest.TestCase):
@classmethod
def setUpClass(self):
self.iwnlp = IWNLPWrapper(lemmatizer_path='data/IWNLP.Lemmatizer_20170501.json')
def test_lemmatize_plain_example1(self):
predicted = self.iwnlp.lemmatize_plain('Hallo')
self.assertEqual(predicted, ['Hallo'])
def test_lemmatize_plain_example2(self):
predicted = self.iwnlp.lemmatize_plain('Hallo', ignore_case=False)
self.assertEqual(predicted, ['Hallo'])
def test_lemmatize_plain_example3(self):
predicted = self.iwnlp.lemmatize_plain('birne', ignore_case=False)
self.assertEqual(predicted, None)
def test_lemmatize_plain_example4(self):
predicted = self.iwnlp.lemmatize_plain('birne', ignore_case=True)
self.assertEqual(predicted, ['Birne'])
def test_lemmatize_plain_example5(self):
predicted = self.iwnlp.lemmatize_plain('gespielt')
self.assertEqual(predicted, ['spielen'])
def test_lemmatize_plain_example6(self):
predicted = self.iwnlp.lemmatize_plain('schnell')
self.assertCountEqual(predicted, ['schnellen', 'schnell'])
def test_lemmatize_plain_example7(self):
predicted = self.iwnlp.lemmatize_plain('Gartenhäuser')
self.assertEqual(predicted, ['Gartenhaus'])
def test_contains_entry_example1(self):
self.assertEqual(self.iwnlp.contains_entry('Birne'), True)
def test_contains_entry_example2(self):
self.assertEqual(self.iwnlp.contains_entry('birne', ignore_case=False), False)
def test_contains_entry_example3(self):
self.assertEqual(self.iwnlp.contains_entry('birne', ignore_case=True), True)
def test_contains_entry_example4(self):
self.assertEqual(self.iwnlp.contains_entry('groko'), False)
def test_contains_entry_example5(self):
self.assertEqual(self.iwnlp.contains_entry('GroKo'), True)
def test_contains_entry_example6(self):
self.assertEqual(self.iwnlp.contains_entry('groko', ignore_case=True), True)
def test_contains_entry_example7(self):
self.assertEqual(self.iwnlp.contains_entry('groko', pos='Noun'), False)
def test_contains_entry_example8(self):
self.assertEqual(self.iwnlp.contains_entry('groko', pos='X'), False)
def test_contains_entry_example9(self):
self.assertEqual(self.iwnlp.contains_entry('groko', pos='AdjectivalDeclension'), False)
def test_contains_entry_example10(self):
self.assertEqual(self.iwnlp.contains_entry('groko', pos=["Noun", "X"], ignore_case=True), True)
def test_lemmatize_example1(self):
predicted = self.iwnlp.lemmatize('Lkws', pos_universal_google='NOUN')
self.assertEqual(predicted, ['Lkw'])
def test_lemmatize_example2(self):
predicted = self.iwnlp.lemmatize('gespielt', pos_universal_google='VERB')
self.assertEqual(predicted, ['spielen'])
def test_get_lemmas_example1(self):
predicted = self.iwnlp.get_lemmas('groko', pos=["Noun", "X"], ignore_case=True)
self.assertEqual(predicted, ['GroKo'])
