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679 lines
28 KiB
679 lines
28 KiB
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import spacy
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import nltk
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from nltk.stem.snowball import SnowballStemmer
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import hickle as hkl
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import FASTsearch
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stemmer = SnowballStemmer("german")
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class Passiv2Aktiv(object):
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def __init__(self, hklDatabaseDir_Aktiv, hklDatabaseDir_Vorgangspassiv, hklDatabaseDir_Zustandspassiv):
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if hklDatabaseDir_Aktiv is not None:
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self.AktivDB = hkl.load(hklDatabaseDir_Aktiv)
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if hklDatabaseDir_Vorgangspassiv is not None:
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self.VorgangspassivDB = hkl.load(hklDatabaseDir_Vorgangspassiv)
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if hklDatabaseDir_Zustandspassiv is not None:
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self.ZustandspassivDB = hkl.load(hklDatabaseDir_Zustandspassiv)
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#print('loading the german spacy model..')
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self.nlp = spacy.load('de_core_news_sm')
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#print('done')
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#print('loading the stemmer..')
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self.stemmer = SnowballStemmer("german")
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#print('done')
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return
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def create_hklDB_from_csv(self, csvDbDir, StemOrNot):
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with open(csvDbDir) as lines:
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self.DB_All = []
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for line in lines:
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#print(line)
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self.DB_All.append(list(eval(line)))
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self.hkldb1 = []
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self.hkldb2 = []
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counter = 0
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for n in range(len(self.DB_All)):
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counter += 1
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if counter % 1000 == 0:
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print(counter)
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self.hkldb1.append([self.DB_All[n][0][0]] )
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self.hkldb2.append([self.DB_All[n][1][0]] )
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print('creating the hkl dump of DBAll')
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hkl.dump(self.DB_All, 'hkldb_All' + csvDbDir[:-4] + '.hkl', mode='w', compression='lzf')
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#print('done..')
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print('Creating the hkl dump of DB 1')
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hkl.dump(self.hkldb1, 'hkldb1' + csvDbDir[:-4] + '.hkl', mode='w', compression='lzf')
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#print('done..')
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print('Creating the hkl dump of DB 2')
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hkl.dump(self.hkldb2, 'hkldb2' + csvDbDir[:-4] + '.hkl', mode='w', compression='lzf')
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#print('done..')
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return 'done'
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def load_DB_into_FASTsearch(self):
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#print('loading the hkldb_All databases..')
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self.hkldbAktiv_All = hkl.load('hkldb_AllAktiv.hkl')
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#print('first done')
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self.hkldbVorgangspassiv_All = hkl.load('hkldb_AllVorgangspassiv.hkl')
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#print('second done')
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self.hkldbZustandspassiv_All = hkl.load('hkldb_AllZustandspassiv.hkl')
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#print('third done')
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#print('loading hkldbIndi_Conju 1..')
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self.fsearchAktiv1 = FASTsearch.FASTsearch('hkldb1Aktiv.hkl')
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#print('done')
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#print('loading hkldbIndi_Conju 2..')
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self.fsearchAktiv2 = FASTsearch.FASTsearch('hkldb2Aktiv.hkl')
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#print('done')
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# generate bow model only necessary the first time
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#print('generating BoW Model 1..')
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#self.fsearchAktiv1.Gen_BoW_Model(20000, "word", punctuation = False)
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#print('done')
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#print('generating BoW Model 2..')
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#self.fsearchAktiv2.Gen_BoW_Model(20000, "word", punctuation = False)
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#print('done')
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#print('loading the bow model 1')
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self.fsearchAktiv1.Load_BoW_Model('bagofwordshkldb1Aktiv.pkl', 'DataBaseOneZeroshkldb1Aktiv.hkl')
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#print('done')
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#print('loading the bow model 2')
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self.fsearchAktiv2.Load_BoW_Model('bagofwordshkldb2Aktiv.pkl', 'DataBaseOneZeroshkldb2Aktiv.hkl')
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#print('done')
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#print('loading hkldbIndi_Conju 1..')
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self.fsearchVorgangspassiv1 = FASTsearch.FASTsearch('hkldb1Vorgangspassiv.hkl')
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#print('done')
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#print('loading hkldbIndi_Conju 2..')
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self.fsearchVorgangspassiv2 = FASTsearch.FASTsearch('hkldb2Vorgangspassiv.hkl')
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#print('done')
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# uncomment if models are not there
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#print('generating BoW Model 1..')
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#self.fsearchVorgangspassiv1.Gen_BoW_Model(20000, "word", punctuation = False)
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#print('done')
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#print('generating BoW Model 2..')
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#self.fsearchVorgangspassiv2.Gen_BoW_Model(20000, "word", punctuation = False)
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#print('done')
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#print('loading the bow model 1')
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self.fsearchVorgangspassiv1.Load_BoW_Model('bagofwordshkldb1Vorgangspassiv.pkl', 'DataBaseOneZeroshkldb1Vorgangspassiv.hkl')
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#print('done')
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#print('loading the bow model 2')
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self.fsearchVorgangspassiv2.Load_BoW_Model('bagofwordshkldb2Vorgangspassiv.pkl', 'DataBaseOneZeroshkldb2Vorgangspassiv.hkl')
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#print('done')
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#print('loading hkldbIndi_Conju 1..')
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self.fsearchZustandspassiv1 = FASTsearch.FASTsearch('hkldb1Zustandspassiv.hkl')
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#print('done')
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#print('loading hkldbIndi_Conju 2..')
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self.fsearchZustandspassiv2 = FASTsearch.FASTsearch('hkldb2Zustandspassiv.hkl')
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#print('done')
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#print('generating BoW Model 1..')
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#self.fsearchZustandspassiv1.Gen_BoW_Model(20000, "word", punctuation = False)
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#print('done')
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#print('generating BoW Model 2..')
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#self.fsearchZustandspassiv2.Gen_BoW_Model(20000, "word", punctuation = False)
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#print('done')
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#print('loading the bow model 1')
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self.fsearchZustandspassiv1.Load_BoW_Model('bagofwordshkldb1Zustandspassiv.pkl', 'DataBaseOneZeroshkldb1Zustandspassiv.hkl')
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#print('done')
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#print('loading the bow model 2')
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self.fsearchZustandspassiv2.Load_BoW_Model('bagofwordshkldb2Zustandspassiv.pkl', 'DataBaseOneZeroshkldb2Zustandspassiv.hkl')
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#print('done')
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import GS_Utils
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#print('initializing the gs utils..')
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self.gs = GS_Utils.GS_Utils('de_core_news_sm')
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#print('done')
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from SentGlue import SentGlueMach
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#print('loading the Stochastic Gradient models..')
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self.sgm = SentGlueMach('trainedSGD.pkl', 'bagofwords.pkl')
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#print('done')
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#print('initializing the SGM..')
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self.sgm.initialize()
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#print('done')
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#print('oi thats the get_feature_names', self.fsearch1.vectorizer.get_feature_names())
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#print('oi thats the get_feature_names', self.fsearch2.vectorizer.get_feature_names())
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def replacePassivForms(self,sentences):
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endsentences = []
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sentencecount = 0
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for sentence in sentences:
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try:
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sentencecount += 1
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#print('processing sentence', sentencecount)
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doc = self.nlp(' '.join(sentence))
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verbs_of_sentence = []
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wordindex_to_replace = []
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count = 0
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subjectofsentence = []
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subjectindex = []
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erindex = []
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Erindex = []
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undindex = []
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for word in doc:
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count += 1
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#print(word.text)
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#print(word.dep_)
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if word.dep_ == 'sb':
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#print('oi')
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subjectofsentence.append(word.text)
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subjectindex.append(count)
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if word.text == 'er':
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erindex.append(count)
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if word.text == 'Er':
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Erindex.append(count)
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if word.text == 'und':
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undindex.append(count)
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if word.tag_[0] == 'V':
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verbs_of_sentence.append(word.text)
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wordindex_to_replace.append(count)
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if len(verbs_of_sentence) == 1 and verbs_of_sentence[0] == ('wurde' or 'wird' or 'werden' or 'wirst' or 'werde' or 'war'):
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verbs_of_sentence[0] = 'bliblablubdudidu'
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verbs_of_sentence_string = ' '.join(verbs_of_sentence)
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length_verbs_of_sentence_string = len(verbs_of_sentence_string)
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verbs_of_sentence_string += ' ' + str(length_verbs_of_sentence_string)
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#print(verbs_of_sentence_string)
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bestmatchesZustandspassiv1, matchindexZustandspassiv1 = self.fsearchZustandspassiv1.search_with_highest_multiplikation_Output(verbs_of_sentence_string, 1)
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bestmatchesVorgangspassiv1, matchindexVorgangspassiv1 = self.fsearchVorgangspassiv1.search_with_highest_multiplikation_Output(verbs_of_sentence_string, 1)
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#print('verbs of sentence string', verbs_of_sentence_string)
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#print(len(verbs_of_sentence))
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#print(matchindexVorgangspassiv1)
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#print(matchindexZustandspassiv1)
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vorgangORnot = 0
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zustandORnot = 0
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if (len(verbs_of_sentence) + 1) == matchindexVorgangspassiv1[1]:
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workindex = matchindexVorgangspassiv1[0]
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vorgangORnot = 1
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if (len(verbs_of_sentence) + 1) == matchindexZustandspassiv1[1]:
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workindex = matchindexZustandspassiv1[0]
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zustandORnot = 1
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#print(workindex)
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#print(self.hkldbAktiv_All[matchindexVorgangspassiv1[0]])
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#print(self.hkldbVorgangspassiv_All[matchindexVorgangspassiv1[0]])
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#print(self.hkldbZustandspassiv_All[matchindexZustandspassiv1[0]])
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formToReplace = []
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if vorgangORnot == 1:
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completeform = self.hkldbVorgangspassiv_All[workindex]
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if len(verbs_of_sentence_string.split()) != len(completeform[0][0].split()):
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vorgangORnot = 0
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if vorgangORnot == 1:
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completeform = self.hkldbVorgangspassiv_All[workindex]
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formToReplace = self.hkldbVorgangspassiv_All[workindex][1][0].split()[-2:]
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#print('formtoreplace vorgang',formToReplace)
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#print('complete form', completeform)
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formToReplace = '3. Person Singular ' + ' '.join(formToReplace)
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#print(formToReplace)
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thrdPersonAktivindex = self.fsearchAktiv2.search_with_highest_multiplikation_Output(formToReplace, 1)[0]
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thrdPersonAktiv = self.hkldbAktiv_All[thrdPersonAktivindex[0]][0][0].split()[:-1]
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#print(thrdPersonAktiv)
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thrdPersonAktiv = ' '.join(thrdPersonAktiv)
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dalist = verbs_of_sentence_string.split()[:-1]
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for verb in dalist:
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#print(sentence)
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#print(index)
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sentence.remove(verb)
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thereisasubjectEr = 0
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for index in subjectindex:
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for ind in undindex:
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if index - 1 == ind:
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if index - 2 == ('er' or 'Er'):
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thereisasubjectEr = 1
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if index + 1 == ind:
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if index + 2 == 'er' or index + 2 == 'Er':
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thereisasubjectEr = 1
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#print('subjectofsentence', subjectofsentence)
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thereisasubjectich = 0
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thereisasubjectdu = 0
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thereisasubjectihr = 0
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thereisasubjectwir = 0
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for word in subjectofsentence:
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if word == 'er' or word == 'Er':
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thereisasubjectEr = 1
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if word == 'ich':
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thereisasubjectich = 1
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if word == 'du':
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thereisasubjectdu = 1
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if word == 'ihr':
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thereisasubjectihr = 1
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if word == 'wir':
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thereisasubjectwir = 1
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#print('there is a subjecter', thereisasubjectEr)
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if thereisasubjectEr == 1:
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try:
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sentence.remove('Er')
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except:
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sentence.remove('er')
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sentence.append('ihn')
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if thereisasubjectich == 1:
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sentence.remove('ich')
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sentence.append('mich')
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if thereisasubjectdu == 1:
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sentence.remove('du')
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sentence.append('dich')
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if thereisasubjectihr == 1:
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sentence.remove('ihr')
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sentence.append('euch')
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if thereisasubjectwir == 1:
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sentence.remove('wir')
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sentence.append('uns')
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sentence.append(thrdPersonAktiv)
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#print('sentence in the vorgangornot', sentence)
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jemandornot = 1
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wordstodelete = []
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for n in range(len(sentence) - 1):
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if sentence[n] == 'von':
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if sentence[n + 1] == 'ihr':
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sentence[n + 1] = 'sie'
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wordstodelete.append(n)
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jemandornot = 0
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if sentence[n + 1] == 'ihm':
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sentence[n + 1] = 'er'
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wordstodelete.append(n)
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jemandornot = 0
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import spacy
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nlp = spacy.load('de_core_news_sm')
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token1 = nlp(sentence[n - 1])
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token2 = nlp(sentence[n + 1])
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for word in token1:
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if word.tag_ != 'NN' and word.tag_ != 'NE':
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for word in token2:
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if word.tag_ == 'NN' or word.tag_ == 'NE':
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wordstodelete.append(n)
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jemandornot = 0
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if sentence[n + 1] == 'dem' or sentence[n + 1] == 'einem':
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token3 = nlp(sentence[n-1])
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for word in token3:
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if word.tag_ != 'NN' and word.tag_ != 'NE':
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sentence[n + 1] = 'ein'
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wordstodelete.append(n)
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jemandornot = 0
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if sentence[n + 1] == 'der' or sentence[n + 1] == 'einer':
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token4 = nlp(sentence[n-1])
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for word in token4:
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if word.tag_ != 'NN' and word.tag_ != 'NE':
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sentence[n + 1] = 'eine'
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wordstodelete.append(n)
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jemandornot = 0
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if sentence[n] == 'vom':
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sentence[n] = 'ein'
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jemandornot = 0
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for index in wordstodelete[::-1]:
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del sentence[index]
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if jemandornot == 1:
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sentence.append('jemand')
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#print('sentence checkpoint 2', sentence)
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#print('get the tuples and triples to check..')
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tuplesTocheck, triplesTocheck, quadruplesToCheck = self.gs.GetTuplesinSentence(sentence)
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#print('done')
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#print(tuplesTocheck, triplesTocheck)
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grammpiecessentence = self.gs.createTupleofGrammarpieces( sentence, tuplesTocheck, triplesTocheck, quadruplesToCheck)
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if len(grammpiecessentence) > 7:
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print('A sentence is too long, too many permutations. \n piping wrong grammar..')
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endsentences.append(' '.join(grammpiecessentence).split())
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else:
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#print('the grammpiecessentence', grammpiecessentence)
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#print('genrating the permutations')
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permutations = self.sgm.GeneratePermutationsOfSentence(grammpiecessentence)
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#print('done')
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#print(permutations)
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#if (len(tuplesTocheck) != 0) or (len(triplesTocheck) != 0):
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# print('filtering the permutations based on the tuples and triples..')
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# filteredpermutations = self.gs.filterpermutationsaccordingtotuples(permutations, tuplesTocheck, triplesTocheck)
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# print('done')
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#else:
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# print('there are no triples or tuples to check..')
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# filteredpermutations = permutations
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sentencesToCheck = []
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for sentence in permutations:
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sentencesToCheck.append(' '.join(sentence))
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#print('sentencesToCheck', sentencesToCheck)
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#print('classifying the probability for right grammar in the filtered permutations..')
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#print(' '.join(sentence))
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endsentence = self.sgm.GetBestSentenceFromSentencesAccordingToGrammar(sentencesToCheck, ' '.join(sentence))
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#print('done')
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#print('the endsentence', endsentence)
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endsentences.append(endsentence.split())
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#count1 = 0
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#print(subjectindex)
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#subjectindex = subjectindex[0]
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#if subjectindex != 0:
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#for word in sentence[subjectindex - 1:subjectindex + 1]:
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#count1 += 1
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#if word == 'und':
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#thereIsanUnd = count1
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#if subjectindex == 0:
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#for word in sentence[subjectindex:subjectindex + 1]:
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#count1 += 1
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#if word == 'und':
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#thereIsanUnd = count1
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#thereisanEr = 0
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#if sentence[subjectindex - 1 + thereIsanUnd] == 'er' or sentence[subjectindex - 1 + thereIsanUnd] == 'Er':
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#thereisanEr = 1
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#if thereisanEr == 1:
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#sentence.remove('Er')
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#sentence.remove('er')
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#sentence.append('ihn')
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#print('zustandornot',zustandORnot)
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#print('vorgang', vorgangORnot)
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if zustandORnot == 1:
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completeform = self.hkldbZustandspassiv_All[workindex]
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if len(verbs_of_sentence_string.split()) != len(completeform[0][0].split()):
|
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zustandORnot = 0
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|
|
|
|
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if zustandORnot == 1:
|
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#completeform = self.hkldbZustandspassiv_All[workindex]
|
|
formToReplace = self.hkldbZustandspassiv_All[workindex][1][0].split()[-2:]
|
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formToReplace = '3. Person Singular ' + ' '.join(formToReplace)
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#print('formtoreplace zustand',formToReplace)
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|
#print('complete form', completeform)
|
|
|
|
thrdPersonAktivindex = self.fsearchAktiv2.search_with_highest_multiplikation_Output(formToReplace, 1)[0]
|
|
|
|
thrdPersonAktiv = self.hkldbAktiv_All[thrdPersonAktivindex[0]][0][0].split()[:-1]
|
|
|
|
thrdPersonAktiv = ' '.join(thrdPersonAktiv)
|
|
|
|
for verb in verbs_of_sentence_string.split()[:-1]:
|
|
#print(sentence)
|
|
#print(index)
|
|
|
|
sentence.remove(verb)
|
|
|
|
thereisasubjectEr = 0
|
|
|
|
for index in subjectindex:
|
|
for ind in undindex:
|
|
if index - 1 == ind:
|
|
if index - 2 == ('er' or 'Er'):
|
|
thereisasubjectEr = 1
|
|
if index + 1 == ind:
|
|
if index + 2 == 'er' or index + 2 == 'Er':
|
|
thereisasubjectEr = 1
|
|
#print('subjectofsentence', subjectofsentence)
|
|
|
|
thereisasubjectich = 0
|
|
thereisasubjectdu = 0
|
|
thereisasubjectihr = 0
|
|
thereisasubjectwir = 0
|
|
for word in subjectofsentence:
|
|
if word == 'er' or word == 'Er':
|
|
thereisasubjectEr = 1
|
|
if word == 'ich':
|
|
thereisasubjectich = 1
|
|
if word == 'du':
|
|
thereisasubjectdu = 1
|
|
if word == 'ihr':
|
|
thereisasubjectihr = 1
|
|
if word == 'wir':
|
|
thereisasubjectwir = 1
|
|
if thereisasubjectEr == 1:
|
|
try:
|
|
sentence.remove('Er')
|
|
except:
|
|
sentence.remove('er')
|
|
sentence.append('ihn')
|
|
|
|
if thereisasubjectich == 1:
|
|
sentence.remove('ich')
|
|
sentence.append('mich')
|
|
if thereisasubjectdu == 1:
|
|
sentence.remove('du')
|
|
sentence.append('dich')
|
|
if thereisasubjectihr == 1:
|
|
sentence.remove('ihr')
|
|
sentence.append('euch')
|
|
if thereisasubjectwir == 1:
|
|
sentence.remove('wir')
|
|
sentence.append('uns')
|
|
|
|
sentence.append(thrdPersonAktiv)
|
|
|
|
jemandornot = 1
|
|
wordstodelete = []
|
|
for n in range(len(sentence) - 1):
|
|
if sentence[n] == 'von':
|
|
if sentence[n + 1] == 'ihr':
|
|
sentence[n + 1] = 'sie'
|
|
wordstodelete.append(n)
|
|
jemandornot = 0
|
|
if sentence[n + 1] == 'ihm':
|
|
sentence[n + 1] = 'er'
|
|
wordstodelete.append(n)
|
|
jemandornot = 0
|
|
|
|
import spacy
|
|
nlp = spacy.load('de_core_news_sm')
|
|
token1 = nlp(sentence[n - 1])
|
|
token2 = nlp(sentence[n + 1])
|
|
for word in token1:
|
|
if word.tag_ != 'NN' and word.tag_ != 'NE':
|
|
for word in token2:
|
|
if word.tag_ == 'NN' or word.tag_ == 'NE':
|
|
wordstodelete.append(n)
|
|
|
|
jemandornot = 0
|
|
if sentence[n + 1] == 'dem' or sentence[n + 1] == 'einem':
|
|
|
|
token3 = nlp(sentence[n-1])
|
|
for word in token3:
|
|
if word.tag_ != 'NN' and word.tag_ != 'NE':
|
|
sentence[n + 1] = 'ein'
|
|
wordstodelete.append(n)
|
|
jemandornot = 0
|
|
if sentence[n + 1] == 'der' or sentence[n + 1] == 'einer':
|
|
token4 = nlp(sentence[n-1])
|
|
for word in token4:
|
|
if word.tag_ != 'NN' and word.tag_ != 'NE':
|
|
sentence[n + 1] = 'eine'
|
|
wordstodelete.append(n)
|
|
jemandornot = 0
|
|
|
|
if sentence[n] == 'vom':
|
|
|
|
sentence[n] = 'ein'
|
|
jemandornot = 0
|
|
|
|
for index in wordstodelete[::-1]:
|
|
del sentence[index]
|
|
|
|
if jemandornot == 1:
|
|
sentence.append('jemand')
|
|
|
|
|
|
#print(sentence)
|
|
|
|
#print('get the tuples and triples to check..')
|
|
tuplesTocheck, triplesTocheck, quadruplesTocheck = self.gs.GetTuplesinSentence(sentence)
|
|
#print('done')
|
|
#print(tuplesTocheck, triplesTocheck)
|
|
|
|
grammpiecessentence = self.gs.createTupleofGrammarpieces( sentence, tuplesTocheck, triplesTocheck, quadruplesTocheck)
|
|
|
|
if len(grammpiecessentence) > 7:
|
|
print('A sentence is too long, too many permutations. \n piping wrong grammar..')
|
|
endsentences.append(' '.join(grammpiecessentence).split())
|
|
|
|
else:
|
|
|
|
#print('the grammpiecessentence', grammpiecessentence)
|
|
#print('genrating the permutations')
|
|
permutations = self.sgm.GeneratePermutationsOfSentence(grammpiecessentence)
|
|
#print('done')
|
|
#print(permutations)
|
|
#if (len(tuplesTocheck) != 0) or (len(triplesTocheck) != 0):
|
|
# print('filtering the permutations based on the tuples and triples..')
|
|
# filteredpermutations = self.gs.filterpermutationsaccordingtotuples(permutations, tuplesTocheck, triplesTocheck)
|
|
# print('done')
|
|
#else:
|
|
# print('there are no triples or tuples to check..')
|
|
# filteredpermutations = permutations
|
|
|
|
sentencesToCheck = []
|
|
for sentence in permutations:
|
|
sentencesToCheck.append(' '.join(sentence))
|
|
|
|
#print('sentencesToCheck', sentencesToCheck)
|
|
#print('classifying the probability for right grammar in the filtered permutations..')
|
|
#print(' '.join(sentence))
|
|
endsentence = self.sgm.GetBestSentenceFromSentencesAccordingToGrammar(sentencesToCheck, ' '.join(sentence))
|
|
#print('done')
|
|
|
|
#print('the endsentence', endsentence)
|
|
endsentences.append(endsentence.split())
|
|
|
|
|
|
|
|
if zustandORnot == 0 and vorgangORnot == 0:
|
|
#print('it is coming to the else')
|
|
endsentences.append(sentence)
|
|
|
|
except:
|
|
print('the sentence ' + str(sentence) + ' caused an error in the module passive2active')
|
|
if endsentences[-1] == sentence:
|
|
pass
|
|
else:
|
|
endsentences.append(sentence)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
return endsentences
|
|
|
|
|
|
|
|
# Vorgangspassiv wird auf selbe Zeit gemappt, 3. Person Singular.
|
|
# Zustandspassiv: Immer eine Zeit dahinter. D.h.
|
|
# Präsens => Präteritum, Präteritum => Perfekt
|
|
|