2017-10-16 14:01:38 +02:00
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[thesaurus]
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2017-10-17 10:13:49 +02:00
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input = deWordNet.xml
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pickle_file = thesaurus_dict.pkl
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2017-09-15 14:32:44 +02:00
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2017-10-16 14:01:38 +02:00
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[spellchecking]
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2017-10-17 10:13:49 +02:00
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input = deu_news_2015_1M-sentences.txt
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pickle_file = words_dict.pkl
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2017-09-15 14:32:44 +02:00
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2017-10-16 14:01:38 +02:00
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[lemmatization]
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2017-10-17 10:13:49 +02:00
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input = lemmas.txt
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pickle_file = lemma_dict.pkl
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2017-09-15 14:32:44 +02:00
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2017-10-16 14:01:38 +02:00
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[nouns]
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2017-10-17 10:13:49 +02:00
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input1 = nomen.txt
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input2 = nomen2.txt
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pickle_file = nouns_list.pkl
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2017-09-15 14:32:44 +02:00
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2017-10-16 14:01:38 +02:00
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[firstnames]
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2017-10-17 10:13:49 +02:00
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input = firstnames.txt
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pickle_file = firstnames_list.pkl
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2017-10-16 14:01:38 +02:00
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[de_stopwords]
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2017-10-17 10:13:49 +02:00
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input1 = de_stopwords_1.txt
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input2 = de_stopwords_2.txt
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input3 = de_stopwords_3.txt
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pickle_file = stopwords_list.pkl
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2017-10-16 14:01:38 +02:00
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[logging]
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level = INFO
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2017-10-17 10:13:49 +02:00
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filename = topicModelTickets.log
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2017-10-16 14:01:38 +02:00
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[de_corpus]
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2017-10-17 10:13:49 +02:00
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#input = M42-Export/Tickets_med.csv
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#input = M42-Export/Tickets_small.csv
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#input = M42-Export/Tickets_mini.csv
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input = M42-Export/de_tickets.csv
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2017-10-16 14:01:38 +02:00
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2017-10-17 10:13:49 +02:00
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path = corpi/
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2017-10-16 14:01:38 +02:00
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[en_corpus]
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2017-10-17 10:13:49 +02:00
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input = M42-Export/en_tickets.csv
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2017-10-16 14:01:38 +02:00
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2017-10-17 10:13:49 +02:00
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path = corpi/
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2017-10-16 14:01:38 +02:00
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[tickets]
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content_collumn_name = Description
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metaliste = TicketNumber,Subject,CreatedDate,categoryName,Impact,Urgency,BenutzerID,VerantwortlicherID,EigentuemerID,Solution
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2017-09-15 14:32:44 +02:00
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2017-09-11 12:12:28 +02:00
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[preprocessing]
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2017-09-11 13:24:20 +02:00
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ents2keep = WORK_OF_ART,ORG,PRODUCT,LOC
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2017-09-11 12:12:28 +02:00
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2017-09-15 14:32:44 +02:00
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custom_words = grüßen,fragen,damen,probleme,herren,dank
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2017-09-11 12:12:28 +02:00
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#lemmatize = True
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2017-09-11 13:24:20 +02:00
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[topic modeling]
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ngrams = (1,2)
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min_df = 0
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max_df = 1.0
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no_below = 20
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no_above = 0.5
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topicModel = lda
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top_topic_words = 5
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top_document_labels_per_topic = 2
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2017-09-11 12:12:28 +02:00
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