textcleaning verfeinert
This commit is contained in:
parent
bb9edcff25
commit
86ee5d7fba
|
@ -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
|
||||||
|
|
Can't render this file because it is too large.
|
354
preprocessing.py
354
preprocessing.py
|
@ -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:
|
||||||
|
|
||||||
|
["-----","---","...","“","”",".","-","<",">",",","?","!","..","n’t","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 = ["-----","---","...","“","”",".","-","<",">",",","?","!","..","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)
|
||||||
|
|
||||||
|
|
||||||
|
# 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("&", "and").replace(">", ">").replace("<", "<")
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
# 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
220
test.py
|
@ -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:
|
||||||
|
|
||||||
|
["-----","---","...","“","”",".","-","<",">",",","?","!","..","n’t","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 = ["-----","---","...","“","”",".","-","<",">",",","?","!","..","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)
|
||||||
|
|
||||||
|
|
||||||
|
# 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("&", "and").replace(">", ">").replace("<", "<")
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
# 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))
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
Loading…
Reference in New Issue