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Programmatic Content
Analysis
Content
WTF!?
The Power of Deep Learning
in Content Analysis
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
+
RankBrain
Es gibt unterschiedlichste Arten von ML
● Supervised
● Unsupervised
● Semi-Supervised
● Reinforcement Learning
● ...
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
Supervised ML Beispiel
Unsupervised ML
● Es gibt keine Trainingsdaten
● Der Algorithmus wird mit Daten gefüttert in der Hoffnung, dass sinnvolle
Ergebnisse rauskommen
Beispiele:
● Clustering
● Assoziation
Beispiel Assoziation
https://rare-technologies.com/word2vec-tutorial/
Beispiel Assoziation
https://rare-technologies.com/word2vec-tutorial/
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
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
● ...
Am Ende ist ML Mathe / Statistik / ...
Was leistet ML heute?
Image colorization
Kunst?
https://github.com/alexjc/neural-doodle
Beschreibung von Bildern
http://cs.stanford.edu/people/karpathy/neuraltalk2/demo.html
Bären scheinen Probleme zu machen ;)
http://cs.stanford.edu/people/karpathy/neuraltalk2/demo.html
Google Cloud Video Intelligence
https://cloud.google.com/video-intelligence/#demo
Emotion recognition
https://www.microsoft.com/cognitive-services/en-us/emotion-api
Roboter...
https://www.youtube.com/watch?v=rVlhMGQgDkY
Chatbots
Amazon Echo Google Home
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
ML & Content
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
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
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
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
Mensch oder Maschine?
Teen Charged Of Killing Youth
Ranch Worker Appears In Court
New Hampshire Teen In Court
After Killing Of Police Officer
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
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/
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
- 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/
Readability von Texten
● Automatische erkennung von Textqualität
● Einsatzgebiete:
○ Content Audit / Optimierung
○ Prüfung von Lesbarkeit vor livegang
○ ...
Easier readability = more interaction
4926 Moz Blog Articles & Onsite Likes / Comments
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?
○ ...
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?
○ ...
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
○ ...
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/
Keyword Extraction
● Automatisches extrahieren von Keywords (Phrasen) aus Texten
● Einsatzgebiete:
○ Keywordanalyse
○ Content/Themen-Research
○ Optimierung WDF / IDF Analysen auf Phrasen
○ ...
Keyword Extraction Beispiel
https://moz.com/blog/local-seo-ranking-
your-local-business-in-2017
Extracted via IBM Whatsan AlchemyAPI
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…
○ ...
Text Summarization
● Automatisches Zusammenfassen von Texten
● Einsatzgebiete
○ (Automatisches) Erstellen von Abstracts
○ Erstellen von Meta Descriptions
○ Kürzen von Produktbeschreibung
○ Schaffung von neuen Inhalten
○ ...
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
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
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
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
Lasst es heute beginnen...
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The Power of Deep Learning in Content Analysis

  • 3. The Power of Deep Learning in Content Analysis
  • 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.
  • 6.
  • 7.
  • 9.
  • 10.
  • 11. Es gibt unterschiedlichste Arten von ML ● Supervised ● Unsupervised ● Semi-Supervised ● Reinforcement Learning ● ...
  • 12. 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
  • 14. Unsupervised ML ● Es gibt keine Trainingsdaten ● Der Algorithmus wird mit Daten gefüttert in der Hoffnung, dass sinnvolle Ergebnisse rauskommen Beispiele: ● Clustering ● Assoziation
  • 17. 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
  • 18. 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 ● ...
  • 19. Am Ende ist ML Mathe / Statistik / ...
  • 20. Was leistet ML heute?
  • 24. Bären scheinen Probleme zu machen ;) http://cs.stanford.edu/people/karpathy/neuraltalk2/demo.html
  • 25. Google Cloud Video Intelligence https://cloud.google.com/video-intelligence/#demo
  • 31. 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
  • 32. 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
  • 33. 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
  • 34. 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
  • 35. Mensch oder Maschine? Teen Charged Of Killing Youth Ranch Worker Appears In Court New Hampshire Teen In Court After Killing Of Police Officer
  • 36. 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
  • 37. 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/
  • 38. 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
  • 39. - 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/
  • 40. Readability von Texten ● Automatische erkennung von Textqualität ● Einsatzgebiete: ○ Content Audit / Optimierung ○ Prüfung von Lesbarkeit vor livegang ○ ...
  • 41. Easier readability = more interaction 4926 Moz Blog Articles & Onsite Likes / Comments
  • 42. 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? ○ ...
  • 43. 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? ○ ...
  • 44.
  • 45. 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 ○ ...
  • 46. 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/
  • 47. Keyword Extraction ● Automatisches extrahieren von Keywords (Phrasen) aus Texten ● Einsatzgebiete: ○ Keywordanalyse ○ Content/Themen-Research ○ Optimierung WDF / IDF Analysen auf Phrasen ○ ...
  • 49. 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… ○ ...
  • 50. Text Summarization ● Automatisches Zusammenfassen von Texten ● Einsatzgebiete ○ (Automatisches) Erstellen von Abstracts ○ Erstellen von Meta Descriptions ○ Kürzen von Produktbeschreibung ○ Schaffung von neuen Inhalten ○ ...
  • 51. 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
  • 52. 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
  • 53. 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
  • 54. 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
  • 55. Lasst es heute beginnen...
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