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The Power of Deep Learning in Content Analysis

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Verschiedene Beispiel was heutzutage schon im Bereich Content-Kreation und Content-Analyse möglich ist.

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The Power of Deep Learning in Content Analysis

  1. 1. Programmatic Content Analysis Content
  2. 2. WTF!?
  3. 3. The Power of Deep Learning in Content Analysis
  4. 4. AI: Die Vision von Computern die wie Menschen denken. Machine Learning: Algorithmen die nach bestimmten Mustern lernen Deep Learning: Maschinelles Lernen mit Hilfe neuronaler Netze
  5. 5. + RankBrain
  6. 6. Es gibt unterschiedlichste Arten von ML ● Supervised ● Unsupervised ● Semi-Supervised ● Reinforcement Learning ● ...
  7. 7. Supervised ML ● Es gibt Trainingsdaten mit (korrekten) Antworten ● Damit wird das System gefüttert und so der Algorithmus angelernt/geschaffen ● Dieser Algorithmus entscheidet bei neuen Daten welches die richtige Antwort ist ● Dabei wird ein großer Teil der Daten (meist 90%) zum anlernen des Algorithmus genutzt, der andere Teil um das Ergebnis zu prüfen
  8. 8. Supervised ML Beispiel
  9. 9. Unsupervised ML ● Es gibt keine Trainingsdaten ● Der Algorithmus wird mit Daten gefüttert in der Hoffnung, dass sinnvolle Ergebnisse rauskommen Beispiele: ● Clustering ● Assoziation
  10. 10. Beispiel Assoziation https://rare-technologies.com/word2vec-tutorial/
  11. 11. Beispiel Assoziation https://rare-technologies.com/word2vec-tutorial/
  12. 12. Weitere Beispiele Unsupervised Learning ● Geopolitics: Iraq - Violence = Jordan ● Distinction: Human - Animal = Ethics ● President - Power = Prime Minister ● Library - Books = Hall ● Analogy: Stock Market ≈ Thermometer https://deeplearning4j.org/word2vec
  13. 13. Weitere Arten von ML ● semi-supervised ○ Eine Mischung aus Supervised und unsupervised ○ u.a. um die Anzahl der Daten zu reduzieren die man konkret vorgeben muss ● reinforcement learning ○ Lernen anhand von vorangegangenen Ergebnissen ○ Z.B. beim Schach an gewonnenen oder verlorenen Partien ● ...
  14. 14. Am Ende ist ML Mathe / Statistik / ...
  15. 15. Was leistet ML heute?
  16. 16. Image colorization
  17. 17. Kunst? https://github.com/alexjc/neural-doodle
  18. 18. Beschreibung von Bildern http://cs.stanford.edu/people/karpathy/neuraltalk2/demo.html
  19. 19. Bären scheinen Probleme zu machen ;) http://cs.stanford.edu/people/karpathy/neuraltalk2/demo.html
  20. 20. Google Cloud Video Intelligence https://cloud.google.com/video-intelligence/#demo
  21. 21. Emotion recognition https://www.microsoft.com/cognitive-services/en-us/emotion-api
  22. 22. Roboter... https://www.youtube.com/watch?v=rVlhMGQgDkY
  23. 23. Chatbots Amazon Echo Google Home
  24. 24. https://de.slideshare.net/bretmc/machine-learning-with-google-machine-learning-apis-puppy-or-muffin?qid=c9d5ded8-a31d-4aea -a7ad-ad6069406d70&v=&b=&from_search=1
  25. 25. ML & Content
  26. 26. Mensch oder Maschine? U.S. And China Top Oil Companies To Hold Major Gas Crisis For North Korea U.S., China Said To Discuss Choking Off North Korea Energy
  27. 27. Mensch oder Maschine? U.S. And China Top Oil Companies To Hold Major Gas Crisis For North Korea U.S., China Said To Discuss Choking Off North Korea Energy http://clickotron.com/article/5602/us-and-c hina-top-oil-companies-to-hold-major-gas -crisis-for https://www.bloomberg.com/news/articles /2016-10-04/u-s-china-said-to-discuss-ch oking-off-north-korea-energy-trade Maschine Mensch
  28. 28. Mensch oder Maschine? John McCain Warns Supreme Court To Stand Up For Birth Control Reform John McCain Just Did What His Best Friend In The Senate Warned Republicans Not To Do
  29. 29. Mensch oder Maschine? John McCain Warns Supreme Court To Stand Up For Birth Control Reform John McCain Just Did What His Best Friend In The Senate Warned Republicans Not To Do Maschine Mensch http://clickotron.com/article/5535/john-mc cain-warns-supreme-court-to-stand-up-for -birth-contro http://www.vox.com/policy-and-politics/20 16/10/18/13315926/lindsey-graham-clinto n-supreme-court-john-mccain
  30. 30. Mensch oder Maschine? Teen Charged Of Killing Youth Ranch Worker Appears In Court New Hampshire Teen In Court After Killing Of Police Officer
  31. 31. Mensch oder Maschine? Teen Charged Of Killing Youth Ranch Worker Appears In Court New Hampshire Teen In Court After Killing Of Police Officer http://www.thespectrum.com/story/news/2 016/12/30/teen-charged-killing-youth-ranc h-worker-appears-court/96003154/ http://clickotron.com/article/30232/new-ha mpshire-teen-in-court-after-killing-of-polic e-officer MaschineMensch
  32. 32. Generating Text ● “News Portal” mit automatisch generierten Nachrichten: clickotron.com ● Umsetzung von Ende 2015 als Experiment ● Auch wurden schon Shakespeare Dialoge, Wikipedia Artikel oder Source Code auf die Art und Weise durch Maschinen erstellt ● Further reading: http://karpathy.github.io/2015/05/21/rnn-effectiveness/ https://larseidnes.com/2015/10/13/auto-generating-clickbait-with-recurrent-neural-networks/
  33. 33. Auch hier - die Maschine ist “erfinderisch”... Miley Cyrus Turns 13 New President Is 'Hours Away' From Royal Pregnancy This Guy Thinks His Cat Was Drunk For His Five Years, He Gets A Sex Assault At A Home Mary J. Williams On Coming Out As A Woman
  34. 34. - Text Classification - Sentiment Analysis - Translation - Automatic summarization - Coreference resolution - Discourse analysis - Machine translation - Morphological segmentation - Named entity recognition (NER) - Natural language generation - Natural language understanding - Optical character recognition (OCR) - Parsing - Question answering - Relationship extraction - Speech recognition - Speech segmentation - Word segmentation - Word sense disambiguation - Information retrieval (IR) - Information extraction (IE) - Speech processing ML wird in vielen/den meisten NLP Bereichn eingesetzt Publicationen: http://nlp.stanford.edu/pubs/
  35. 35. Readability von Texten ● Automatische erkennung von Textqualität ● Einsatzgebiete: ○ Content Audit / Optimierung ○ Prüfung von Lesbarkeit vor livegang ○ ...
  36. 36. Easier readability = more interaction 4926 Moz Blog Articles & Onsite Likes / Comments
  37. 37. Clustering / Topic Extraction ● Clustern von Texten ● Zuweisen von Texten zu einem Bestimmten Cluster / Topic ● Einsatzgebiete: ○ Strukturieren von vielen Texten, z.B. ein nicht oder nur wenig strukturierter Ratgeberbereich / Blog / Newsbereich… ○ Tagging von Texten ○ Aufsetzen von Linkstrukturen ○ Schneller Überblick über Struktur von Textinhalte ○ Analyse von Wettbewerber Inhalten ■ In welchen Bereichen hat der Wettbewerb Inhalte in denen ich noch keine habe? ○ ...
  38. 38. Sentiment Analyse ● Positive oder Negative Aussagen ● Einsatzgebiete ○ Zu welchen Artikeln erhalte ich positive / negative Kommentare ○ Aus Bewertungen - (eigene oder fremde) - welche (Podukt-)Eigenschaften werden positiv oder Negativ Bewertet? ○ ...
  39. 39. Entity Extraction ● Extrahieren und klassifizieren von Personen, Unternehmen, Produkten usw. aus Texten ● Einsatzgebiete: ○ Klassifizierung von Content / Seiten (Commercial, Informational, …) ○ Extrahierung von Entitäten, z.B. Wettbeweber Crawlen und alle Unternehmen / Marken extrahieren ○ Keywordanalyse/Clustering ○ ...
  40. 40. Entity Extraction Beispiel ⟨Google⟩1 , headquartered in ⟨Mountain View⟩6 , unveiled the new ⟨Android⟩3 ⟨phone⟩2 at the ⟨Consumer Electronic Show⟩7 . ⟨Sundar Pichai⟩5 said in his ⟨keynote⟩9 that ⟨users⟩4 love their new ⟨Android⟩3 ⟨phones⟩8 . https://cloud.google.com/natural-language/
  41. 41. Keyword Extraction ● Automatisches extrahieren von Keywords (Phrasen) aus Texten ● Einsatzgebiete: ○ Keywordanalyse ○ Content/Themen-Research ○ Optimierung WDF / IDF Analysen auf Phrasen ○ ...
  42. 42. Keyword Extraction Beispiel https://moz.com/blog/local-seo-ranking- your-local-business-in-2017 Extracted via IBM Whatsan AlchemyAPI
  43. 43. Word Clustering ● Building Cluster from Words ● Einsatzgebiete: ○ 10.000+ Keywords in GSC, GA, Adwords, Extraction from Webpages…? Cluster it! ○ Combine with entity meaning and build verticals like places for local, buying intent vs informational… ○ ...
  44. 44. Text Summarization ● Automatisches Zusammenfassen von Texten ● Einsatzgebiete ○ (Automatisches) Erstellen von Abstracts ○ Erstellen von Meta Descriptions ○ Kürzen von Produktbeschreibung ○ Schaffung von neuen Inhalten ○ ...
  45. 45. Beispiel Text Summarization article: gulf newspapers voiced skepticism thursday over whether newly re - elected us president bill clinton could help revive the troubled middle east peace process but saw a glimmer of hope . human: gulf skeptical about whether clinton will revive peace process machine: gulf press skeptical over clinton 's prospects for peace process https://github.com/tensorflow/models/tree/master/textsum
  46. 46. Headline CTR Prediction ● Anhand einer bestehenden Datenbasis können CTRs für neue Headlines vorhergesagt werden ● Einsatzgebiete: ○ Prüfung verschiedener Headlines / Auswahl der optimalen Headline für Social, Adwords, OnPage Content… ○ Zusammen mit Extraction / Headline generierung eine “optimale” Headline erstellen
  47. 47. Beispiel Headlines CTR Prediction (Train set) Highest predicted CTRs 1) les anges 8 : andréane en dit plus sur son couple avec aurélie (groundtruth: 1st) 2) secret story, les 8 plus grosses prises de poids : jessica, nadège, aurélie… (groundtruth: 2nd) 3) nabilla et thomas vergara : révélations sur leur vie sexuelle (groundtruth: 13th) 4) la folle virée d’elodie frégé et joeystarr… kim kardashian, poupée pour sa fille… (groundtruth: 72th) 5) pamela anderson : entièrement nue, à 46 ans, pour une série photo érotique (groundtruth: 29th) 6) mort d’isabelle (secret story 2) : une femme généreuse qui avait peur de mourir (groundtruth: 3th) 7) patrick poivre d’arvor : claire chazal, la mort de ses trois filles et son fils françois (groundtruth: 4th) 8) 10 alcooliques qui ont marqué le monde par leur intelligence (groundtruth: 48th) 9) laurence chirac : sa vie hors de l’élysée et ses derniers jours dans l’ombre (groundtruth: 35th) 10) sylvie vartan présente sa fille, darina : “elle m’a apporté un coup de jeune” (groundtruth: 13th) https://blog.deepomatic.com/text-regression-for-click-through-rate-pre diction-using-convnet-9f43971e12c#.h5ijqba88
  48. 48. Beispiel Headlines CTR Prediction (Train set) Lowest predicted CTRs: 250) mauvaise haleine ? ces 7 astuces simples vont y mettre un terme (groundtruth: 155th) 251) entre sculpture et photographie, huit artistes modernes au musée rodin (groundtruth: 230th) 252) les recettes de tartes aux artichauts (groundtruth: 223th) 253) pintadeau de la drôme sur canapé par alain ducasse (groundtruth: 242th) 254) recette de cuisses de grenouilles par alain ducasse: (groundtruth: 208th) https://blog.deepomatic.com/text-regression-for-click-through-rate-pre diction-using-convnet-9f43971e12c#.h5ijqba88
  49. 49. Lasst es heute beginnen...
  50. 50. Fragen? Du willst wissen was searchVIU ist / macht? www.searchviu.com
  51. 51. Vielen Dank!

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