textcleaning verfeinert
This commit is contained in:
jannis.grundmann
parent
bb9edcff25
commit
86ee5d7fba
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@ -1,3 +1,5 @@
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TH;Technische_Universität (Hauptform);Technische Hochschule;TU
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Passwort (Hauptform);Kodewort;Schlüsselwort;Zugangscode;Kennwort (Hauptform);Geheimcode;Losung;Codewort;Zugangswort;Losungswort;Parole
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Fission;Kernfission;Kernspaltung;Atomspaltung
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Wiederaufnahme;Fortführung
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davonfahren;abdüsen (ugs.);aufbrechen;abfliegen;abfahren;(von etwas) fortfahren;abreisen;wegfahren;wegfliegen
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@ -2182,7 +2184,6 @@ Spitzenklöppel (Handarbeit);Glockenklöppel;Klöppel
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gutartig;benigne (fachspr.)
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Beutelratte;Taschenratte
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rollen;kollern (ugs.);kullern;kugeln
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Kodewort;Schlüsselwort;Zugangscode;Kennwort (Hauptform);Geheimcode;Losung;Codewort;Zugangswort;Passwort (Hauptform);Losungswort;Parole
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packen;einpacken
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Ratschluss;Urteil;Wille;Entscheidung;Entschlossenheit;Beschluss;das letzte Wort (ugs.);Entschluss;Entscheid (schweiz.)
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dreckig machen;versiffen;beschmutzen;verschmutzen
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@ -4207,7 +4208,6 @@ Akzise;Oktroi;Verbrauchsabgabe
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Aufrührer;Tumultant
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genügsam;bedürfnislos
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zeigen;offenbaren;bekunden;kundtun
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TH;Technische Universität;Technische Hochschule;TU
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Versprechen;Absichtserklärung (Nachrichtensprache);Zusicherung;Versicherung;Beteuerung
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Beschaulichkeit;Stille
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Auswärtiges Amt;Außenamt (ugs.);Außenministerium (ugs.);AA;Ministerium für Auswärtige Angelegenheiten
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Can't render this file because it is too large.
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354
preprocessing.py
354
preprocessing.py
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@ -17,15 +17,7 @@ import keras.backend as K
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"""
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csv.field_size_limit(sys.maxsize)
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def printRandomDoc(textacyCorpus):
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print()
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print("len(textacyCorpus) = %i" % len(textacyCorpus))
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randIndex = int((len(textacyCorpus) - 1) * random.random())
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print("Index: {0} ; Text: {1} ; Metadata: {2}".format(randIndex, textacyCorpus[randIndex].text, textacyCorpus[randIndex].metadata))
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print()
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"""
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def getFirstSynonym(word, thesaurus_gen):
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word = word.lower()
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@ -58,8 +50,9 @@ def getFirstSynonym(word, thesaurus_gen):
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return word # zur Not die eingabe ausgeben
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def cleanText(string,custom_stopwords=None, custom_symbols=None, custom_words=None, customPreprocessing=None, lemmatize=False):
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import re
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"""
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"""
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def cleanText(string,custom_stopwords=None, custom_symbols=None, custom_words=None, customPreprocessing=None, lemmatize=False, normalize_synonyms=False):
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# use preprocessing
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if customPreprocessing is not None:
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@ -119,7 +112,7 @@ def cleanText(string,custom_stopwords=None, custom_symbols=None, custom_words=No
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tokens = []
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added_entities = ["WORK_OF_ART","ORG","PRODUCT", "LOC"]#,"PERSON"]
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added_POS = ["NOUN", "NUM" ]#,"VERB","ADJ"] #IDEE NUM mit in den Corpus aufnehmen, aber fürs TopicModeling nur Nomen http://aclweb.org/anthology/U15-1013
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added_POS = ["NOUN"]#, "NUM" ]#,"VERB","ADJ"] #IDEE NUM mit in den Corpus aufnehmen, aber fürs TopicModeling nur Nomen http://aclweb.org/anthology/U15-1013
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# append Tokens to a list
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for tok in spacy_doc:
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@ -148,55 +141,33 @@ def cleanText(string,custom_stopwords=None, custom_symbols=None, custom_words=No
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tokens = [tok for tok in tokens if len(tok)>1]
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# remove large strings of whitespace
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while "" in tokens:
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tokens.remove("")
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while " " in tokens:
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tokens.remove(" ")
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while "\n" in tokens:
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tokens.remove("\n")
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while "\n\n" in tokens:
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tokens.remove("\n\n")
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"""
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tokenz = []
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for tok in tokens:
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tokenz.append(str(getFirstSynonym(tok,THESAURUS_gen)))
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tokens = tokenz
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"""
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tokens = [str(getFirstSynonym(tok,THESAURUS_gen)) for tok in tokens]
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remove_large_strings_of_whitespace(" ".join(tokens))
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#idee abkürzungen auflösen (v.a. TU -> Technische Universität)
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if normalize_synonyms:
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tokens = [str(getFirstSynonym(tok,THESAURUS_list)) for tok in tokens]
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return " ".join(tokens)
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def generateTextfromXML(path2xml, clean=True, textfield='Beschreibung'):
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import xml.etree.ElementTree as ET
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def remove_large_strings_of_whitespace(sentence):
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tree = ET.parse(path2xml, ET.XMLParser(encoding="utf-8"))
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whitespaceFinder = re.compile(r'(\r\n|\r|\n)', re.IGNORECASE)
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sentence = whitespaceFinder.sub(" ", sentence)
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root = tree.getroot()
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tokenlist = sentence.split(" ")
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for subject in root.iter(textfield):
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if clean:
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yield cleanText(subject.text)
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else:
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yield subject.text
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while "" in tokenlist:
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tokenlist.remove("")
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while " " in tokenlist:
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tokenlist.remove(" ")
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def generateMetadatafromXML(path2xml, keys=["Loesung","Kategorie","Zusammenfassung"]):
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import xml.etree.ElementTree as ET
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tree = ET.parse(path2xml, ET.XMLParser(encoding="utf-8"))
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root = tree.getroot()
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metadata = dict.fromkeys(keys)
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for ticket in root.findall('ticket'):
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for key in metadata:
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metadata[key] = ticket.find(key).text
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yield metadata
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def generateFromXML(path2xml, clean=True, textfield='Beschreibung'):
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return " ".join(tokenlist)
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"""
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"""
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def generateFromXML(path2xml, textfield='Beschreibung', clean=False, normalize_Synonyms=False,lemmatize=False):
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import xml.etree.ElementTree as ET
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tree = ET.parse(path2xml, ET.XMLParser(encoding="utf-8"))
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@ -208,58 +179,291 @@ def generateFromXML(path2xml, clean=True, textfield='Beschreibung'):
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for field in ticket:
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if field.tag == textfield:
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if clean:
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text = cleanText(field.text)
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text = cleanText_words(field.text,PARSER,normalize_synonyms=normalize_Synonyms,lemmatize=lemmatize)
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else:
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text = field.text
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else:
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#idee hier auch cleanen?
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metadata[field.tag] = field.text
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yield text, metadata
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"""
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LANGUAGE = 'de'
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PARSER = spacy.load(LANGUAGE)
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def generateTextfromXML(path2xml, textfield='Beschreibung', clean=False, normalize_Synonyms=False,lemmatize=False):
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import xml.etree.ElementTree as ET
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tree = ET.parse(path2xml, ET.XMLParser(encoding="utf-8"))
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root = tree.getroot()
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"""
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for subject in root.iter(textfield):
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if clean:
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yield cleanText(subject.text)
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else:
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yield subject.text
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"""
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for ticket in root:
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text = "ERROR"
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for field in ticket:
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if field.tag == textfield:
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if clean:
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text = cleanText_words(field.text,normalize_synonyms=normalize_Synonyms,lemmatize=lemmatize)
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else:
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text = field.text
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yield text
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def generateMetadatafromXML(path2xml, textfield='Beschreibung'):#,keys_to_clean=["Loesung","Zusammenfassung"]):
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import xml.etree.ElementTree as ET
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tree = ET.parse(path2xml, ET.XMLParser(encoding="utf-8"))
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root = tree.getroot()
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"""
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metadata = dict.fromkeys(keys)
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for ticket in root.findall('ticket'):
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for key in metadata:
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metadata[key] = ticket.find(key).text
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yield metadata
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"""
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for ticket in root:
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metadata = {}
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for field in ticket:
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if field.tag != textfield:
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if field.tag == "Zusammenfassung":
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# idee lösung nur whitespace entfernen, zusammenfassung auch von symbolen befreien
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metadata[field.tag] = cleanText_symbols(field.text)
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elif field.tag == "Loesung":
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metadata[field.tag] = remove_whitespace(field.text)
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else:
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metadata[field.tag] = field.text
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yield metadata
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def cleanText_symbols(string, parser=PARSER, custom_symbols=None, keep=None):
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"""
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https://spacy.io/docs/usage/pos-tagging
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cleans text from PUNCT, NUM, whitespaces, newlines, and the following list of symbols:
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["-----","---","...","“","”",".","-","<",">",",","?","!","..","n’t","n't","|","||",";",":","…","’s","'s",".","(",")","[","]","#"]
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"""
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if custom_symbols is not None:
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custom_symbols = custom_symbols
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else:
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custom_symbols = []
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if keep is not None:
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keep = keep
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else:
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keep = []
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# List of symbols we don't care about
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symbols = ["-----","---","...","“","”",".","-","<",">",",","?","!","..","n’t","n't","|","||",";",":","…","’s","'s",".","(",")","[","]","#"] + custom_symbols
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# parse with spaCy
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spacy_doc = parser(string)
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tokens = []
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pos = ["NUM", "SPACE", "PUNCT"]
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for p in keep:
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pos.remove(p)
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# append Tokens to a list
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for tok in spacy_doc:
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if tok.pos_ not in pos and tok.text not in symbols:
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tokens.append(tok.text)
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return " ".join(tokens)
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def cleanText_words(string,parser=PARSER, custom_stopwords=None, custom_words=None, customPreprocessing=cleanText_symbols, lemmatize=False, normalize_synonyms=False):
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# use preprocessing
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if customPreprocessing is not None:
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string = customPreprocessing(string)
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if custom_stopwords is not None:
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custom_stopwords = custom_stopwords
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else:
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custom_stopwords = []
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if custom_words is not None:
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custom_words = custom_words
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else:
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custom_words = []
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# custom stoplist
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# https://stackoverflow.com/questions/9806963/how-to-use-pythons-import-function-properly-import
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stop_words = __import__("spacy." + parser.lang, globals(), locals(), ['object']).STOP_WORDS
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stoplist =list(stop_words) + custom_stopwords
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# replace twitter
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mentionFinder = re.compile(r"@[a-z0-9_]{1,15}", re.IGNORECASE)
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string = mentionFinder.sub("MENTION", string)
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# replace emails
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emailFinder = re.compile(r"\b[A-Z0-9._%+-]+@[A-Z0-9.-]+\.[A-Z]{2,}\b", re.IGNORECASE)
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string = emailFinder.sub("EMAIL", string)
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# replace urls
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urlFinder = re.compile(r"^(?:https?:\/\/)?(?:www\.)?[a-zA-Z0-9./]+$", re.IGNORECASE)
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string = urlFinder.sub("URL", string)
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# replace HTML symbols
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string = string.replace("&", "and").replace(">", ">").replace("<", "<")
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# parse with spaCy
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spacy_doc = parser(string)
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tokens = []
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added_entities = ["WORK_OF_ART","ORG","PRODUCT", "LOC"]#,"PERSON"]
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added_POS = ["NOUN"]#, "NUM" ]#,"VERB","ADJ"] #fürs TopicModeling nur Nomen http://aclweb.org/anthology/U15-1013
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# append Tokens to a list
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for tok in spacy_doc:
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if tok.pos_ in added_POS:
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if lemmatize:
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tokens.append(tok.lemma_.lower().strip())
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else:
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tokens.append(tok.text.lower().strip())
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# add entities
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if tok.ent_type_ in added_entities:
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tokens.append(tok.text.lower())
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# remove stopwords
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tokens = [tok for tok in tokens if tok not in stoplist]
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# remove custom_words
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tokens = [tok for tok in tokens if tok not in custom_words]
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# remove single characters
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tokens = [tok for tok in tokens if len(tok)>1]
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# remove large strings of whitespace
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#remove_whitespace(" ".join(tokens))
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#idee abkürzungen auflösen (v.a. TU -> Technische Universität)
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if normalize_synonyms:
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tokens = [str(getFirstSynonym(tok,THESAURUS_list)) for tok in tokens]
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return " ".join(set(tokens))
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def remove_whitespace(sentence):
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whitespaceFinder = re.compile(r'(\r\n|\r|\n|(\s)+)', re.IGNORECASE)
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sentence = whitespaceFinder.sub(" ", sentence)
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return sentence
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def getFirstSynonym(word, thesaurus_gen):
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word = word.lower()
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# durch den thesaurrus iterieren
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for syn_block in thesaurus_gen: # syn_block ist eine liste mit Synonymen
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for syn in syn_block:
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syn = syn.lower()
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if re.match(r'\A[\w-]+\Z', syn): # falls syn einzelwort ist
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if word == syn:
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return getHauptform(syn_block, word)
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else: # falls es ein satz ist
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if word in syn:
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return getHauptform(syn_block, word)
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return word # zur Not, das ursrpüngliche Wort zurückgeben
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def getHauptform(syn_block, word, default_return_first_Syn=False):
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for syn in syn_block:
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syn = syn.lower()
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if "hauptform" in syn and len(syn.split(" ")) <= 2:
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# nicht ausgeben, falls es in Klammern steht
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for w in syn.split(" "):
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if not re.match(r'\([^)]+\)', w):
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return w
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if default_return_first_Syn:
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# falls keine hauptform enthalten ist, das erste Synonym zurückgeben, was kein satz ist und nicht in klammern steht
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for w in syn_block:
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if not re.match(r'\([^)]+\)', w):
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return w
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return word # zur Not, das ursrpüngliche Wort zurückgeben
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def printRandomDoc(textacyCorpus):
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print()
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print("len(textacyCorpus) = %i" % len(textacyCorpus))
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randIndex = int((len(textacyCorpus) - 1) * random.random())
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print("Index: {0} ; Text: {1} ; Metadata: {2}".format(randIndex, textacyCorpus[randIndex].text, textacyCorpus[randIndex].metadata))
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print()
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####################'####################'####################'####################'####################'##############
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import de_core_news_md
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DATAPATH = "ticketSamples.xml"
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DATAPATH_thesaurus = "openthesaurus.csv"
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LANGUAGE = 'de'
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normalize_Synonyms = True
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clean = True
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lemmatize = True
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custom_words = ["grüßen", "fragen"]
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####################'####################'####################'####################'####################'##############
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PARSER = spacy.load(LANGUAGE)
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THESAURUS_gen = textacy.fileio.read_csv(DATAPATH_thesaurus, delimiter=";") # generator [[a,b,c,..],[a,b,c,..],...]
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#PARSER = de_core_news_md.load()
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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
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## files to textacy-corpus
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textacyCorpus = textacy.Corpus(PARSER)
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print("add texts to textacy-corpus...")
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#textacyCorpus.add_texts(texts=generateTextfromXML(DATAPATH), metadatas=generateMetadatafromXML(DATAPATH))
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for txt, dic in generateFromXML(DATAPATH):
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textacyCorpus.add_text(txt,dic)
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textacyCorpus.add_texts(texts=generateTextfromXML(DATAPATH,normalize_Synonyms=normalize_Synonyms, clean=clean, lemmatize=lemmatize), metadatas=generateMetadatafromXML(DATAPATH))
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#for txt, dic in generateFromXML(DATAPATH, normalize_Synonyms=normalize_Synonyms, clean=clean, lemmatize=lemmatize):
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# textacyCorpus.add_text(txt,dic)
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for doc in textacyCorpus:
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print(doc.metadata)
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print(doc.text)
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print(textacyCorpus[2].text)
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#print(textacyCorpus[2].text)
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#printRandomDoc(textacyCorpus)
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#print(textacyCorpus[len(textacyCorpus)-1].text)
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print()
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print()
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|
||||
|
|
|
|||
200
test.py
200
test.py
|
|
@ -8,90 +8,146 @@ import xml.etree.ElementTree as ET
|
|||
|
||||
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"))
|
||||
root = tree.getroot()
|
||||
PARSER = spacy.load('de')
|
||||
|
||||
for ticket in root:
|
||||
metadata = {}
|
||||
text = "ERROR"
|
||||
for field in ticket:
|
||||
if field.tag == textfield:
|
||||
if clean:
|
||||
text = (field.text)
|
||||
|
||||
|
||||
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:
|
||||
|
||||
["-----","---","...","“","”",".","-","<",">",",","?","!","..","n’t","n't","|","||",";",":","…","’s","'s",".","(",")","[","]","#"]
|
||||
|
||||
"""
|
||||
if custom_symbols is not None:
|
||||
custom_symbols = custom_symbols
|
||||
else:
|
||||
text = field.text
|
||||
custom_symbols = []
|
||||
|
||||
if keep is not None:
|
||||
keep = keep
|
||||
else:
|
||||
metadata[field.tag] = field.text
|
||||
yield text, metadata
|
||||
keep = []
|
||||
|
||||
# List of symbols we don't care about
|
||||
symbols = ["-----","---","...","“","”",".","-","<",">",",","?","!","..","n’t","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)
|
||||
|
||||
|
||||
def getFirstSynonym(word, thesaurus_gen):
|
||||
|
||||
word = word.lower()
|
||||
# TODO word cleaning https://stackoverflow.com/questions/3939361/remove-specific-characters-from-a-string-in-python
|
||||
# append Tokens to a list
|
||||
for tok in spacy_doc:
|
||||
if tok.pos_ not in pos:
|
||||
tokens.append(tok.text.lower().strip())
|
||||
|
||||
|
||||
# durch den thesaurrus iterieren
|
||||
for syn_block in thesaurus_gen: # syn_block ist eine liste mit Synonymen
|
||||
# remove symbols
|
||||
tokens = [tok for tok in tokens if tok not in symbols]
|
||||
|
||||
# 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)
|
||||
# remove whitespace
|
||||
remove_whitespace(" ".join(tokens))
|
||||
|
||||
# falls das wort in dem synonym enthalten ist (also == einem Wort in der liste ist)
|
||||
if word in syn:
|
||||
return " ".join(tokens)
|
||||
|
||||
# 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
|
||||
def cleanText_words(string, parser=PARSER, custom_stopwords=None, custom_words=None, customPreprocessing=cleanText_symbols, lemmatize=False, normalize_synonyms=False):
|
||||
|
||||
# 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
|
||||
# use preprocessing
|
||||
if customPreprocessing is not None:
|
||||
string = customPreprocessing(string)
|
||||
|
||||
return word # zur Not die eingabe ausgeben
|
||||
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("&", "and").replace(">", ">").replace("<", "<")
|
||||
|
||||
|
||||
|
||||
def getFirstSynonym(word, thesaurus_gen):
|
||||
# parse with spaCy
|
||||
spacy_doc = parser(string)
|
||||
tokens = []
|
||||
|
||||
word = word.lower()
|
||||
# TODO word cleaning https://stackoverflow.com/questions/3939361/remove-specific-characters-from-a-string-in-python
|
||||
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())
|
||||
|
||||
# durch den thesaurrus iterieren
|
||||
for syn_block in thesaurus_gen: # syn_block ist eine liste mit Synonymen
|
||||
|
||||
for syn in syn_block:
|
||||
|
||||
if re.match(r'\A[\w-]+\Z', syn): #falls syn einzelwort ist
|
||||
if word == syn:
|
||||
getHauptform(syn_block)
|
||||
# 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]
|
||||
|
||||
def getHauptform(syn_block):
|
||||
for s in syn_block:
|
||||
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
|
||||
# remove custom_words
|
||||
tokens = [tok for tok in tokens if tok not in custom_words]
|
||||
|
||||
# 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
|
||||
# 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(tokens)
|
||||
|
||||
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
|
||||
if customPreprocessing is not None:
|
||||
string = customPreprocessing(string)
|
||||
|
||||
|
||||
|
||||
|
|
@ -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"]))
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
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))
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
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))
|
||||
|
|
|
|||
Loading…
Reference in New Issue