thesaurus auf basis von deWornNet angefangen
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jannis.grundmann
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51
testo.py
51
testo.py
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@ -519,7 +519,7 @@ def processContentstream(textstream, token_filterlist=None, parser=DE_PARSER):
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for doc in pipe:
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tokens = [tok for tok in doc]
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print(" ".join([tok.lower_ for tok in tokens]))
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#print(" ".join([tok.lower_ for tok in tokens]))
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# in_parse
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if token_filterlist is not None:
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@ -577,11 +577,19 @@ def filterTokens(tokens, funclist):
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custom_words=["geehrt","dame","herr","hilfe","problem","lauten","bedanken","voraus",
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"hallo","gerne","freundlich","fragen","fehler","bitten","ehre", "lieb",
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"versuchen","unbestimmt","woche","tadelos", "klappen" ,"mittlerweile", "bekommen","erreichbar"
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"hallo","gerne","freundlich","fragen","fehler","bitten","ehre", "lieb","helfen"
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"versuchen","unbestimmt","woche","tadelos", "klappen" ,"mittlerweile", "bekommen","erreichbar","gruss",
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"auffahren","vorgang","hinweis","institut","universitaet","name","gruss","id","erfolg","mail","folge",
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"nummer","team","fakultaet","email","absender","tu","versenden","vorname","message",
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"service","strasse","prozess","portal","raum","personal","moeglichkeit","fremd","wende","rueckfrage", "stehen", "verfuegung"
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"funktionieren","kollege", "pruefen"
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]
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filter_tokens=[
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#removeENT(["PERSON"]),
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#idee addressen enfernen #bisher mit cut_after("gruss") --> postal.parser
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@ -669,7 +677,7 @@ pipe=[
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#path2csv = "/home/jannis.grundmann/PycharmProjects/topicModelingTickets/M42-Export/Tickets_med.csv"
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path2csv = "/home/jannis.grundmann/PycharmProjects/topicModelingTickets/M42-Export/Tickets_small.csv"
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#path2csv = "/home/jannis.grundmann/PycharmProjects/topicModelingTickets/M42-Export/de_tickets.csv"
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path2csv = "/home/jannis.grundmann/PycharmProjects/topicModelingTickets/M42-Export/de_tickets.csv"
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de_corpus = textacy.Corpus(DE_PARSER)
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#en_corpus = textacy.Corpus(EN_PARSER)
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@ -689,28 +697,6 @@ for i in range(10):
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"""
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spracherkennung
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alles nach grüße ist irrelevant außer PS:
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vllt kategorien in unterkategorien aufteilen
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allg:
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utf-korregieren,
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emails, urls, nummern raus
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vllt sogar alles, was ebend jenes enthält (oder auf .toplvldomain bzw. sonderzeichen enthält oder alles was ein @ enthält
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sinnvoller wörter von müll trennen: 8203;verfügung -> bei sonderzeichen wörter trennen
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abkürzungen raus: m.a, o.ä.
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wörter korrigieren
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sinnlose bsp: nr54065467 455a33c5 tvt?= ------problem--------
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"""
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end = time.time()
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printlog("Time Elapsed Preprocessing:{0} min".format((end - start)/60))
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@ -727,18 +713,18 @@ start = time.time()
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ngrams = 1
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min_df = 0.1
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max_df = 0.9
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no_below = 20
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no_above = 0.5
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max_df = 0.6
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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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# http://textacy.readthedocs.io/en/latest/api_reference.html#textacy.tm.topic_model.TopicModel.get_doc_topic_matrix
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weighting = ('tf' if topicModel == 'lda' else 'tfidf')
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top_topic_words = 10
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top_document_labels_per_topic = 5
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top_topic_words = 15
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top_document_labels_per_topic = 7
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n_topics = 15 #len(LABELDICT)#len(set(ticketcorpus[0].metadata.keys()))+1 #+1 wegen einem default-topic
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n_topics = 20 #len(LABELDICT)#len(set(ticketcorpus[0].metadata.keys()))+1 #+1 wegen einem default-topic
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@ -789,6 +775,7 @@ for topic_idx, top_docs in model.top_topic_docs(doc_topic_matrix, top_n=top_docu
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printlog(topic_idx)
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for j in top_docs:
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printlog(de_corpus[j].metadata['categoryName'])
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print()
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#####################################################################################################################
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print()
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131
testra.py
131
testra.py
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@ -26,66 +26,6 @@ nomen=[]
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### extract from derewo
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#http://www1.ids-mannheim.de/kl/projekte/methoden/derewo.html
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raw = textacy.fileio.read_file_lines("DeReKo-2014-II-MainArchive-STT.100000.freq")
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for line in raw:
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line_list=line.split()
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if line_list[2] == "NN":
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string = line_list[1].lower()
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# replaceRockDots
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string = re.sub(r'[ß]', "ss", string)
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string = re.sub(r'[ö]', "oe", string)
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string = re.sub(r'[ü]', "ue", string)
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string = re.sub(r'[ä]', "ae", string)
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nomen.append(string.lower().strip())
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textacy.fileio.write_file_lines(nomen,"nomen2.txt")
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"""
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### extract from deWordNet.xml
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@ -112,7 +52,78 @@ for r in root:
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textacy.fileio.write_file_lines(nomen,"nomen.txt")
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"""
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### extract from derewo
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#http://www1.ids-mannheim.de/kl/projekte/methoden/derewo.html
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raw = textacy.fileio.read_file_lines("DeReKo-2014-II-MainArchive-STT.100000.freq")
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for line in raw:
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line_list=line.split()
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if line_list[2] == "NN":
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string = line_list[1].lower()
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# replaceRockDots
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string = re.sub(r'[ß]', "ss", string)
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string = re.sub(r'[ö]', "oe", string)
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string = re.sub(r'[ü]', "ue", string)
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string = re.sub(r'[ä]', "ae", string)
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nomen.append(string.lower().strip())
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textacy.fileio.write_file_lines(nomen,"nomen2.txt")
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"""
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