[thesaurus]
input =  deWordNet.xml
pickle_file =  thesaurus_dict.pkl


[spellchecking]
input = deu_news_2015_1M-sentences.txt
pickle_file =  words_dict.pkl


[lemmatization]
input =  lemmas.txt
pickle_file =  lemma_dict.pkl


[nouns]
input1 =  nomen.txt
input2 =  nomen2.txt
pickle_file =  nouns_list.pkl


[firstnames]
input =  firstnames.txt
pickle_file =  firstnames_list.pkl


[de_stopwords]
input1 =  de_stopwords_1.txt
input2 =  de_stopwords_2.txt
input3 =  de_stopwords_3.txt
pickle_file = stopwords_list.pkl


[logging]
level = INFO
filename = topicModelTickets.log


[de_corpus]
#input = M42-Export/Tickets_med.csv
#input =  M42-Export/Tickets_small.csv
#input =  M42-Export/Tickets_mini.csv
input =  M42-Export/de_tickets.csv

path =  corpi/

[en_corpus]
input =  M42-Export/en_tickets.csv

path =  corpi/



[tickets]
content_collumn_name = Description
metaliste =  TicketNumber,Subject,CreatedDate,categoryName,Impact,Urgency,BenutzerID,VerantwortlicherID,EigentuemerID,Solution




[preprocessing]

ents2keep = WORK_OF_ART,ORG,PRODUCT,LOC

custom_words = grüßen,fragen,damen,probleme,herren,dank

#lemmatize = True


[topic modeling]

ngrams = (1,2)

min_df = 0
max_df = 1.0
no_below = 20
no_above = 0.5

topicModel = lda

top_topic_words = 5

top_document_labels_per_topic = 2