textcleaning verfeinert

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jannis.grundmann 2017-08-31 14:54:01 +02:00
parent bb9edcff25
commit 86ee5d7fba
3 changed files with 411 additions and 167 deletions

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@ -1,3 +1,5 @@
TH;Technische_Universität (Hauptform);Technische Hochschule;TU
Passwort (Hauptform);Kodewort;Schlüsselwort;Zugangscode;Kennwort (Hauptform);Geheimcode;Losung;Codewort;Zugangswort;Losungswort;Parole
Fission;Kernfission;Kernspaltung;Atomspaltung Fission;Kernfission;Kernspaltung;Atomspaltung
Wiederaufnahme;Fortführung Wiederaufnahme;Fortführung
davonfahren;abdüsen (ugs.);aufbrechen;abfliegen;abfahren;(von etwas) fortfahren;abreisen;wegfahren;wegfliegen davonfahren;abdüsen (ugs.);aufbrechen;abfliegen;abfahren;(von etwas) fortfahren;abreisen;wegfahren;wegfliegen
@ -2182,7 +2184,6 @@ Spitzenklöppel (Handarbeit);Glockenklöppel;Klöppel
gutartig;benigne (fachspr.) gutartig;benigne (fachspr.)
Beutelratte;Taschenratte Beutelratte;Taschenratte
rollen;kollern (ugs.);kullern;kugeln rollen;kollern (ugs.);kullern;kugeln
Kodewort;Schlüsselwort;Zugangscode;Kennwort (Hauptform);Geheimcode;Losung;Codewort;Zugangswort;Passwort (Hauptform);Losungswort;Parole
packen;einpacken packen;einpacken
Ratschluss;Urteil;Wille;Entscheidung;Entschlossenheit;Beschluss;das letzte Wort (ugs.);Entschluss;Entscheid (schweiz.) Ratschluss;Urteil;Wille;Entscheidung;Entschlossenheit;Beschluss;das letzte Wort (ugs.);Entschluss;Entscheid (schweiz.)
dreckig machen;versiffen;beschmutzen;verschmutzen dreckig machen;versiffen;beschmutzen;verschmutzen
@ -4207,7 +4208,6 @@ Akzise;Oktroi;Verbrauchsabgabe
Aufrührer;Tumultant Aufrührer;Tumultant
genügsam;bedürfnislos genügsam;bedürfnislos
zeigen;offenbaren;bekunden;kundtun zeigen;offenbaren;bekunden;kundtun
TH;Technische Universität;Technische Hochschule;TU
Versprechen;Absichtserklärung (Nachrichtensprache);Zusicherung;Versicherung;Beteuerung Versprechen;Absichtserklärung (Nachrichtensprache);Zusicherung;Versicherung;Beteuerung
Beschaulichkeit;Stille Beschaulichkeit;Stille
Auswärtiges Amt;Außenamt (ugs.);Außenministerium (ugs.);AA;Ministerium für Auswärtige Angelegenheiten Auswärtiges Amt;Außenamt (ugs.);Außenministerium (ugs.);AA;Ministerium für Auswärtige Angelegenheiten

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@ -17,15 +17,7 @@ import keras.backend as K
""" """
csv.field_size_limit(sys.maxsize) csv.field_size_limit(sys.maxsize)
"""
def printRandomDoc(textacyCorpus):
print()
print("len(textacyCorpus) = %i" % len(textacyCorpus))
randIndex = int((len(textacyCorpus) - 1) * random.random())
print("Index: {0} ; Text: {1} ; Metadata: {2}".format(randIndex, textacyCorpus[randIndex].text, textacyCorpus[randIndex].metadata))
print()
def getFirstSynonym(word, thesaurus_gen): def getFirstSynonym(word, thesaurus_gen):
word = word.lower() word = word.lower()
@ -58,8 +50,9 @@ def getFirstSynonym(word, thesaurus_gen):
return word # zur Not die eingabe ausgeben return word # zur Not die eingabe ausgeben
def cleanText(string,custom_stopwords=None, custom_symbols=None, custom_words=None, customPreprocessing=None, lemmatize=False): """
import re """
def cleanText(string,custom_stopwords=None, custom_symbols=None, custom_words=None, customPreprocessing=None, lemmatize=False, normalize_synonyms=False):
# use preprocessing # use preprocessing
if customPreprocessing is not None: if customPreprocessing is not None:
@ -119,7 +112,7 @@ def cleanText(string,custom_stopwords=None, custom_symbols=None, custom_words=No
tokens = [] tokens = []
added_entities = ["WORK_OF_ART","ORG","PRODUCT", "LOC"]#,"PERSON"] added_entities = ["WORK_OF_ART","ORG","PRODUCT", "LOC"]#,"PERSON"]
added_POS = ["NOUN", "NUM" ]#,"VERB","ADJ"] #IDEE NUM mit in den Corpus aufnehmen, aber fürs TopicModeling nur Nomen http://aclweb.org/anthology/U15-1013 added_POS = ["NOUN"]#, "NUM" ]#,"VERB","ADJ"] #IDEE NUM mit in den Corpus aufnehmen, aber fürs TopicModeling nur Nomen http://aclweb.org/anthology/U15-1013
# append Tokens to a list # append Tokens to a list
for tok in spacy_doc: for tok in spacy_doc:
@ -148,55 +141,33 @@ def cleanText(string,custom_stopwords=None, custom_symbols=None, custom_words=No
tokens = [tok for tok in tokens if len(tok)>1] tokens = [tok for tok in tokens if len(tok)>1]
# remove large strings of whitespace # remove large strings of whitespace
while "" in tokens: remove_large_strings_of_whitespace(" ".join(tokens))
tokens.remove("")
while " " in tokens:
tokens.remove(" ") #idee abkürzungen auflösen (v.a. TU -> Technische Universität)
while "\n" in tokens:
tokens.remove("\n") if normalize_synonyms:
while "\n\n" in tokens: tokens = [str(getFirstSynonym(tok,THESAURUS_list)) for tok in tokens]
tokens.remove("\n\n")
"""
tokenz = []
for tok in tokens:
tokenz.append(str(getFirstSynonym(tok,THESAURUS_gen)))
tokens = tokenz
"""
tokens = [str(getFirstSynonym(tok,THESAURUS_gen)) for tok in tokens]
return " ".join(tokens) return " ".join(tokens)
def generateTextfromXML(path2xml, clean=True, textfield='Beschreibung'): def remove_large_strings_of_whitespace(sentence):
import xml.etree.ElementTree as ET
tree = ET.parse(path2xml, ET.XMLParser(encoding="utf-8")) whitespaceFinder = re.compile(r'(\r\n|\r|\n)', re.IGNORECASE)
sentence = whitespaceFinder.sub(" ", sentence)
root = tree.getroot() tokenlist = sentence.split(" ")
for subject in root.iter(textfield): while "" in tokenlist:
if clean: tokenlist.remove("")
yield cleanText(subject.text) while " " in tokenlist:
else: tokenlist.remove(" ")
yield subject.text
def generateMetadatafromXML(path2xml, keys=["Loesung","Kategorie","Zusammenfassung"]): return " ".join(tokenlist)
import xml.etree.ElementTree as ET """
"""
tree = ET.parse(path2xml, ET.XMLParser(encoding="utf-8")) def generateFromXML(path2xml, textfield='Beschreibung', clean=False, normalize_Synonyms=False,lemmatize=False):
root = tree.getroot()
metadata = dict.fromkeys(keys)
for ticket in root.findall('ticket'):
for key in metadata:
metadata[key] = ticket.find(key).text
yield metadata
def generateFromXML(path2xml, clean=True, textfield='Beschreibung'):
import xml.etree.ElementTree as ET import xml.etree.ElementTree as ET
tree = ET.parse(path2xml, ET.XMLParser(encoding="utf-8")) tree = ET.parse(path2xml, ET.XMLParser(encoding="utf-8"))
@ -208,58 +179,291 @@ def generateFromXML(path2xml, clean=True, textfield='Beschreibung'):
for field in ticket: for field in ticket:
if field.tag == textfield: if field.tag == textfield:
if clean: if clean:
text = cleanText(field.text) text = cleanText_words(field.text,PARSER,normalize_synonyms=normalize_Synonyms,lemmatize=lemmatize)
else: else:
text = field.text text = field.text
else: else:
#idee hier auch cleanen?
metadata[field.tag] = field.text metadata[field.tag] = field.text
yield text, metadata yield text, metadata
"""
LANGUAGE = 'de'
PARSER = spacy.load(LANGUAGE)
def generateTextfromXML(path2xml, textfield='Beschreibung', clean=False, normalize_Synonyms=False,lemmatize=False):
import xml.etree.ElementTree as ET
tree = ET.parse(path2xml, ET.XMLParser(encoding="utf-8"))
root = tree.getroot()
"""
for subject in root.iter(textfield):
if clean:
yield cleanText(subject.text)
else:
yield subject.text
"""
for ticket in root:
text = "ERROR"
for field in ticket:
if field.tag == textfield:
if clean:
text = cleanText_words(field.text,normalize_synonyms=normalize_Synonyms,lemmatize=lemmatize)
else:
text = field.text
yield text
def generateMetadatafromXML(path2xml, textfield='Beschreibung'):#,keys_to_clean=["Loesung","Zusammenfassung"]):
import xml.etree.ElementTree as ET
tree = ET.parse(path2xml, ET.XMLParser(encoding="utf-8"))
root = tree.getroot()
"""
metadata = dict.fromkeys(keys)
for ticket in root.findall('ticket'):
for key in metadata:
metadata[key] = ticket.find(key).text
yield metadata
"""
for ticket in root:
metadata = {}
for field in ticket:
if field.tag != textfield:
if field.tag == "Zusammenfassung":
# idee lösung nur whitespace entfernen, zusammenfassung auch von symbolen befreien
metadata[field.tag] = cleanText_symbols(field.text)
elif field.tag == "Loesung":
metadata[field.tag] = remove_whitespace(field.text)
else:
metadata[field.tag] = field.text
yield metadata
def cleanText_symbols(string, parser=PARSER, custom_symbols=None, keep=None):
"""
https://spacy.io/docs/usage/pos-tagging
cleans text from PUNCT, NUM, whitespaces, newlines, and the following list of symbols:
["-----","---","...","","",".","-","<",">",",","?","!","..","nt","n't","|","||",";",":","","s","'s",".","(",")","[","]","#"]
"""
if custom_symbols is not None:
custom_symbols = custom_symbols
else:
custom_symbols = []
if keep is not None:
keep = keep
else:
keep = []
# List of symbols we don't care about
symbols = ["-----","---","...","","",".","-","<",">",",","?","!","..","nt","n't","|","||",";",":","","s","'s",".","(",")","[","]","#"] + custom_symbols
# parse with spaCy
spacy_doc = parser(string)
tokens = []
pos = ["NUM", "SPACE", "PUNCT"]
for p in keep:
pos.remove(p)
# append Tokens to a list
for tok in spacy_doc:
if tok.pos_ not in pos and tok.text not in symbols:
tokens.append(tok.text)
return " ".join(tokens)
def cleanText_words(string,parser=PARSER, custom_stopwords=None, custom_words=None, customPreprocessing=cleanText_symbols, lemmatize=False, normalize_synonyms=False):
# use preprocessing
if customPreprocessing is not None:
string = customPreprocessing(string)
if custom_stopwords is not None:
custom_stopwords = custom_stopwords
else:
custom_stopwords = []
if custom_words is not None:
custom_words = custom_words
else:
custom_words = []
# custom stoplist
# https://stackoverflow.com/questions/9806963/how-to-use-pythons-import-function-properly-import
stop_words = __import__("spacy." + parser.lang, globals(), locals(), ['object']).STOP_WORDS
stoplist =list(stop_words) + custom_stopwords
# replace twitter
mentionFinder = re.compile(r"@[a-z0-9_]{1,15}", re.IGNORECASE)
string = mentionFinder.sub("MENTION", string)
# replace emails
emailFinder = re.compile(r"\b[A-Z0-9._%+-]+@[A-Z0-9.-]+\.[A-Z]{2,}\b", re.IGNORECASE)
string = emailFinder.sub("EMAIL", string)
# replace urls
urlFinder = re.compile(r"^(?:https?:\/\/)?(?:www\.)?[a-zA-Z0-9./]+$", re.IGNORECASE)
string = urlFinder.sub("URL", string)
# replace HTML symbols
string = string.replace("&amp;", "and").replace("&gt;", ">").replace("&lt;", "<")
# parse with spaCy
spacy_doc = parser(string)
tokens = []
added_entities = ["WORK_OF_ART","ORG","PRODUCT", "LOC"]#,"PERSON"]
added_POS = ["NOUN"]#, "NUM" ]#,"VERB","ADJ"] #fürs TopicModeling nur Nomen http://aclweb.org/anthology/U15-1013
# append Tokens to a list
for tok in spacy_doc:
if tok.pos_ in added_POS:
if lemmatize:
tokens.append(tok.lemma_.lower().strip())
else:
tokens.append(tok.text.lower().strip())
# add entities
if tok.ent_type_ in added_entities:
tokens.append(tok.text.lower())
# remove stopwords
tokens = [tok for tok in tokens if tok not in stoplist]
# remove custom_words
tokens = [tok for tok in tokens if tok not in custom_words]
# remove single characters
tokens = [tok for tok in tokens if len(tok)>1]
# remove large strings of whitespace
#remove_whitespace(" ".join(tokens))
#idee abkürzungen auflösen (v.a. TU -> Technische Universität)
if normalize_synonyms:
tokens = [str(getFirstSynonym(tok,THESAURUS_list)) for tok in tokens]
return " ".join(set(tokens))
def remove_whitespace(sentence):
whitespaceFinder = re.compile(r'(\r\n|\r|\n|(\s)+)', re.IGNORECASE)
sentence = whitespaceFinder.sub(" ", sentence)
return sentence
def getFirstSynonym(word, thesaurus_gen):
word = word.lower()
# durch den thesaurrus iterieren
for syn_block in thesaurus_gen: # syn_block ist eine liste mit Synonymen
for syn in syn_block:
syn = syn.lower()
if re.match(r'\A[\w-]+\Z', syn): # falls syn einzelwort ist
if word == syn:
return getHauptform(syn_block, word)
else: # falls es ein satz ist
if word in syn:
return getHauptform(syn_block, word)
return word # zur Not, das ursrpüngliche Wort zurückgeben
def getHauptform(syn_block, word, default_return_first_Syn=False):
for syn in syn_block:
syn = syn.lower()
if "hauptform" in syn and len(syn.split(" ")) <= 2:
# nicht ausgeben, falls es in Klammern steht
for w in syn.split(" "):
if not re.match(r'\([^)]+\)', w):
return w
if default_return_first_Syn:
# falls keine hauptform enthalten ist, das erste Synonym zurückgeben, was kein satz ist und nicht in klammern steht
for w in syn_block:
if not re.match(r'\([^)]+\)', w):
return w
return word # zur Not, das ursrpüngliche Wort zurückgeben
def printRandomDoc(textacyCorpus):
print()
print("len(textacyCorpus) = %i" % len(textacyCorpus))
randIndex = int((len(textacyCorpus) - 1) * random.random())
print("Index: {0} ; Text: {1} ; Metadata: {2}".format(randIndex, textacyCorpus[randIndex].text, textacyCorpus[randIndex].metadata))
print()
####################'####################'####################'####################'####################'############## ####################'####################'####################'####################'####################'##############
import de_core_news_md
DATAPATH = "ticketSamples.xml" DATAPATH = "ticketSamples.xml"
DATAPATH_thesaurus = "openthesaurus.csv" DATAPATH_thesaurus = "openthesaurus.csv"
LANGUAGE = 'de'
normalize_Synonyms = True
clean = True
lemmatize = True
custom_words = ["grüßen", "fragen"]
####################'####################'####################'####################'####################'############## ####################'####################'####################'####################'####################'##############
PARSER = spacy.load(LANGUAGE) #PARSER = de_core_news_md.load()
THESAURUS_gen = textacy.fileio.read_csv(DATAPATH_thesaurus, delimiter=";") # generator [[a,b,c,..],[a,b,c,..],...]
THESAURUS_list=list(textacy.fileio.read_csv(DATAPATH_thesaurus, delimiter=";")) ## !!!!!! list wichtig, da sonst nicht die gleichen Synonyme zurückgegeben werden, weil der generator während der laufzeit pickt
## files to textacy-corpus ## files to textacy-corpus
textacyCorpus = textacy.Corpus(PARSER) textacyCorpus = textacy.Corpus(PARSER)
print("add texts to textacy-corpus...") print("add texts to textacy-corpus...")
#textacyCorpus.add_texts(texts=generateTextfromXML(DATAPATH), metadatas=generateMetadatafromXML(DATAPATH)) textacyCorpus.add_texts(texts=generateTextfromXML(DATAPATH,normalize_Synonyms=normalize_Synonyms, clean=clean, lemmatize=lemmatize), metadatas=generateMetadatafromXML(DATAPATH))
for txt, dic in generateFromXML(DATAPATH):
textacyCorpus.add_text(txt,dic)
#for txt, dic in generateFromXML(DATAPATH, normalize_Synonyms=normalize_Synonyms, clean=clean, lemmatize=lemmatize):
# textacyCorpus.add_text(txt,dic)
for doc in textacyCorpus:
print(doc.metadata)
print(doc.text)
#print(textacyCorpus[2].text)
print(textacyCorpus[2].text)
#printRandomDoc(textacyCorpus) #printRandomDoc(textacyCorpus)
#print(textacyCorpus[len(textacyCorpus)-1].text) #print(textacyCorpus[len(textacyCorpus)-1].text)
print()
print()

220
test.py
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@ -8,90 +8,146 @@ import xml.etree.ElementTree as ET
DATAPATH_thesaurus = "openthesaurus.csv" DATAPATH_thesaurus = "openthesaurus.csv"
def generateFromXML(path2xml, clean=True, textfield='Beschreibung'):
import xml.etree.ElementTree as ET
tree = ET.parse(path2xml, ET.XMLParser(encoding="utf-8")) PARSER = spacy.load('de')
root = tree.getroot()
for ticket in root:
metadata = {}
text = "ERROR" def cleanText_symbols(string, parser=PARSER, custom_symbols=None, keep=None):
for field in ticket: """
if field.tag == textfield: https://spacy.io/docs/usage/pos-tagging
if clean:
text = (field.text) cleans text from PUNCT, NUM, whitespaces, newlines, and the following list of symbols:
["-----","---","...","","",".","-","<",">",",","?","!","..","nt","n't","|","||",";",":","","s","'s",".","(",")","[","]","#"]
"""
if custom_symbols is not None:
custom_symbols = custom_symbols
else:
custom_symbols = []
if keep is not None:
keep = keep
else:
keep = []
# List of symbols we don't care about
symbols = ["-----","---","...","","",".","-","<",">",",","?","!","..","nt","n't","|","||",";",":","","s","'s",".","(",")","[","]","#"] + custom_symbols
# parse with spaCy
spacy_doc = parser(string)
tokens = []
pos = ["NUM", "SPACE", "PUNCT"]
for p in keep:
pos.remove(p)
# append Tokens to a list
for tok in spacy_doc:
if tok.pos_ not in pos:
tokens.append(tok.text.lower().strip())
# remove symbols
tokens = [tok for tok in tokens if tok not in symbols]
# remove whitespace
remove_whitespace(" ".join(tokens))
return " ".join(tokens)
def cleanText_words(string, parser=PARSER, custom_stopwords=None, custom_words=None, customPreprocessing=cleanText_symbols, lemmatize=False, normalize_synonyms=False):
# use preprocessing
if customPreprocessing is not None:
string = customPreprocessing(string)
if custom_stopwords is not None:
custom_stopwords = custom_stopwords
else:
custom_stopwords = []
if custom_words is not None:
custom_words = custom_words
else:
custom_words = []
# custom stoplist
# https://stackoverflow.com/questions/9806963/how-to-use-pythons-import-function-properly-import
stop_words = __import__("spacy." + parser.lang, globals(), locals(), ['object']).STOP_WORDS
stoplist =list(stop_words) + custom_stopwords
# replace twitter
mentionFinder = re.compile(r"@[a-z0-9_]{1,15}", re.IGNORECASE)
string = mentionFinder.sub("MENTION", string)
# replace emails
emailFinder = re.compile(r"\b[A-Z0-9._%+-]+@[A-Z0-9.-]+\.[A-Z]{2,}\b", re.IGNORECASE)
string = emailFinder.sub("EMAIL", string)
# replace urls
urlFinder = re.compile(r"^(?:https?:\/\/)?(?:www\.)?[a-zA-Z0-9./]+$", re.IGNORECASE)
string = urlFinder.sub("URL", string)
# replace HTML symbols
string = string.replace("&amp;", "and").replace("&gt;", ">").replace("&lt;", "<")
# parse with spaCy
spacy_doc = parser(string)
tokens = []
added_entities = ["WORK_OF_ART","ORG","PRODUCT", "LOC"]#,"PERSON"]
added_POS = ["NOUN"]#, "NUM" ]#,"VERB","ADJ"] #fürs TopicModeling nur Nomen http://aclweb.org/anthology/U15-1013
# append Tokens to a list
for tok in spacy_doc:
if tok.pos_ in added_POS:
if lemmatize:
tokens.append(tok.lemma_.lower().strip())
else: else:
text = field.text tokens.append(tok.text.lower().strip())
else:
metadata[field.tag] = field.text
yield text, metadata
# add entities
def getFirstSynonym(word, thesaurus_gen): if tok.ent_type_ in added_entities:
tokens.append(tok.text.lower())
word = word.lower()
# TODO word cleaning https://stackoverflow.com/questions/3939361/remove-specific-characters-from-a-string-in-python
# durch den thesaurrus iterieren
for syn_block in thesaurus_gen: # syn_block ist eine liste mit Synonymen
# durch den synonymblock iterieren
for syn in syn_block:
syn = syn.lower().split(" ") if not re.match(r'\A[\w-]+\Z', syn) else syn # aus synonym mach liste (um evtl. sätze zu identifieziren)
# falls das wort in dem synonym enthalten ist (also == einem Wort in der liste ist)
if word in syn:
# Hauptform suchen
if "Hauptform" in syn:
# nicht ausgeben, falls es in Klammern steht
for w in syn:
if not re.match(r'\([^)]+\)', w) and w is not None:
return w
# falls keine hauptform enthalten ist, das erste Synonym zurückgeben, was kein satz ist und nicht in klammern steht
if len(syn) == 1:
w = syn[0]
if not re.match(r'\([^)]+\)', w) and w is not None:
return w
return word # zur Not die eingabe ausgeben
def getFirstSynonym(word, thesaurus_gen): # remove stopwords
tokens = [tok for tok in tokens if tok not in stoplist]
word = word.lower() # remove custom_words
# TODO word cleaning https://stackoverflow.com/questions/3939361/remove-specific-characters-from-a-string-in-python tokens = [tok for tok in tokens if tok not in custom_words]
# remove single characters
tokens = [tok for tok in tokens if len(tok)>1]
# remove large strings of whitespace
#remove_whitespace(" ".join(tokens))
# durch den thesaurrus iterieren #idee abkürzungen auflösen (v.a. TU -> Technische Universität)
for syn_block in thesaurus_gen: # syn_block ist eine liste mit Synonymen
for syn in syn_block: #if normalize_synonyms:
# tokens = [str(getFirstSynonym(tok,THESAURUS_list)) for tok in tokens]
if re.match(r'\A[\w-]+\Z', syn): #falls syn einzelwort ist return " ".join(tokens)
if word == syn:
getHauptform(syn_block)
def remove_whitespace(sentence):
whitespaceFinder = re.compile(r'(\r\n|\r|\n|\s)', re.IGNORECASE)
sentence = whitespaceFinder.sub(" ", sentence)
return sentence
def cleanText_normalize(string, parser=PARSER, customPreprocessing=cleanText_words, lemmatize=True):
# use preprocessing
def getHauptform(syn_block): if customPreprocessing is not None:
for s in syn_block: string = customPreprocessing(string)
if "Hauptform" in s:
# nicht ausgeben, falls es in Klammern steht
for w in s:
if not re.match(r'\([^)]+\)', w) and w is not None:
return w
# falls keine hauptform enthalten ist, das erste Synonym zurückgeben, was kein satz ist und nicht in klammern steht
if len(s) == 1:
w = s[0]
if not re.match(r'\([^)]+\)', w) and w is not None:
return w
@ -101,25 +157,9 @@ def getHauptform(syn_block):
string = "Frau Hinrichs überdenkt die Situation und 545453 macht dann neue Anträge. \n Dieses Ticket wird geschlossen \n \n test"
print(cleanText_symbols(string=string, parser=PARSER, keep=["NUM"]))
string = "Frau Hinrichs überdenkt die Situation und 545453 macht dann neue Anträge. \n Dieses Ticket wird geschlossen \n \n test"
print(cleanText_symbols(string=string, parser=PARSER, keep=None))
strings = ["passwort",""]
THESAURUS_gen = textacy.fileio.read_csv(DATAPATH_thesaurus, delimiter=";") # generator [[a,b,c,..],[a,b,c,..],...]
for s in strings:
print(getFirstSynonym(s,THESAURUS_gen))