Doing More with Less: Automated, High-Quality Content Generation
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Hamlet Batista discusses the integration of AI in SEO, emphasizing the importance of intent-based searches and automated question-answer generation. He outlines various methodologies for sourcing popular questions and leveraging existing knowledge bases alongside open-source AI to produce quality content. The document highlights tools, technical approaches, and resources for improving SEO strategies through automation and machine learning applications.
Doing More with Less: Automated, High-Quality Content Generation
- 2. HAMLET BATISTA CEO AND FOUNDER, RANKSENSE INC. @hamletbatista
- 4. You are Leveraging AI in Your SEO Work Already Using Google Docs or Gmail Smart Compose? @hamletbatista#SEJLive
- 5. Facing Writers’ block? Start typing and hit the tab key for full sentence ideas! https://transformer.huggin gface.co/doc/distil-gpt2 @hamletbatista#SEJLive
- 6. How to Go Deeper with Keyword Research “We are in the era where intent-based searches are more important to us than pure volume.” “You should take the extra step to learn the questions customers are asking and how they describe their problems.” “Go from keywords to questions” -Mindy Weinstein SEJ / May 9, 2020 https://www.searchenginejournal.com/deep-keyword-research-process/214096/ @hamletbatista#SEJLive
- 7. What is the Opportunity? 1. Search engines are answering engines these days. 2. One effective way to write original, and popular content is to answer your target audience’s most important questions. @hamletbatista#SEJLive
- 8. 3. FAQ search snippets take more real estate in the SERPs. 4. Doing this manually is going to be expensive and time consuming. @hamletbatista#SEJLive What is the Opportunity?
- 9. 5. Let’s automate it by leveraging AI and existing content assets @hamletbatista#SEJLive What is the Opportunity?
- 10. Leveraging Existing Knowledge 1. Most established businesses have valuable knowledge bases 2. Many times not yet publicly available (support emails, chats, internal wikis). @hamletbatista#SEJLive
- 11. Open Source AI + Proprietary Knowledge Using a technique called Transfer Learning, we can produce original, quality content by combining proprietary knowledge bases and public deep learning models and datasets. @hamletbatista#SEJLive
- 12. AGENDA We are going to review automated question and answer generation approaches: 1. We will source popular questions using online tools 2. We will answer them using two NLG approaches: a. A span search approach b. A “closed book” approach 3. We will add FAQ schema and validate using the SDTT 4. Resources to learn more @hamletbatista#SEJLive
- 14. ● Answer the Public ● Question Analyzer by BuzzSumo ● AlsoAsked.com SOURCING POPULAR QUESTIONS @hamletbatista#SEJLive
- 16. Question Analyzer by BuzzSumo
- 17. AlsoAsked.com
- 19. Papers with Code Question Answering is an area of very active research https://paperswithcode.com/task/question-answering @hamletbatista#SEJLive
- 21. Stanford Question Answering Dataset @hamletbatista#SEJLive
- 23. In Just 3 Lines of Python Code !pip install transformers from transformers import pipeline # Allocate a pipeline for question-answering nlp = pipeline('question-answering') nlp({ 'question': 'What is the name of the repository ?', 'context': 'Pipeline have been included in the huggingface/transformers repository' }) {'answer': 'huggingface/transformers', 'end': 59, 'score': 0.5135626548884602, 'start': 35} @hamletbatista#SEJLive
- 24. A SPAN SEARCH APPROACH ● Load the Transformers NLP library https://github.com/huggingface/transformers ● Allocate a Question Answering pipeline https://huggingface.com/transformers/usage.html#extractive- question-answering ● Provide the question and context (content/text most likely to include the answer) @hamletbatista#SEJLive
- 25. How to Get the Context !pip install requests-html from requests_html import HTMLSession session = HTMLSession() url = "https://www.searchenginejournal.com/uncover-powerful-data-stories-phyton/328471/" selector = "#post-328471 > div:nth-child(2) > div > div > div.sej-article-content.gototop-pos" with session.get(url) as r: post = r.html.find(selector, first=True) text = post.text @hamletbatista#SEJLive
- 27. Exploring the Limits of NLG with T5 and Turing-NLG Google’s T5 (11-billion parameter model) and Microsoft’s TuringNG (17-billion parameter model) are able answer questions without providing any context! 🤯🤯🤯 @hamletbatista#SEJLive
- 28. Open Book vs Closed Book Question Answering @hamletbatista#SEJLive
- 29. Closed Book Trivia Challenge with T5 The Google’s T5 team went head-to-head with the 11-billion parameter model in a pub trivia challenge and lost! 😅 https://t5- trivia.glitch.me/ @hamletbatista#SEJLive
- 30. LET’S TRAIN, FINE-TUNE and LEVERAGE T5 @hamletbatista#SEJLive
- 31. Here is T5AnsweringArbitrary Questions We are going to train the 3-billion parameter model using a free Google Colab TPU. These are some example predictions.
- 32. ● Copy the example Colab notebook to your Google Drive ● Change the runtime environment to Cloud TPU ● Create a Google Cloud Storage bucket (use the free $300 in credits) ● Provide the bucket path to the notebook ● Select the 3-billion parameters model ● Run the remaining cells up to the prediction step We won’t need to write any Python code 😞 HERE IS THE TECHNICAL PLAN @hamletbatista#SEJLive
- 33. Copy the Colab Notebook to Your Google Drive @hamletbatista#SEJLive
- 34. Change the Runtime Environment to Cloud TPU @hamletbatista#SEJLive
- 35. Change the Runtime Environment to Cloud TPU @hamletbatista#SEJLive
- 36. Create a Google Cloud Storage Bucket @hamletbatista#SEJLive
- 37. Provide the Bucket Path to the Notebook @hamletbatista#SEJLive
- 38. Select the 3-billion Parameters Model @hamletbatista#SEJLive
- 39. Run the Remaining Cells up to the Prediction Step @hamletbatista#SEJLive
- 40. FINE TUNING TO ADD PROPRIETARY KNOWLEDGE @hamletbatista#SEJLive
- 41. Add New Proprietary Training Datasets 1. Preprocess your proprietary knowledge base into a format that can work with T5 2. Adapt the existing code for this purpose (Natural Questions, TriviaQA)
- 42. 1. Extract 2. Transform 3. Load https://www.searchenginejournal.com/machine-learning-practical-introduction-seo-professionals/366304/ Add New Proprietary Training Datasets
- 46. https://www.searchenginejournal.com/introduction-modern-javascript-for-seo/347836/
- 48. 1. Introduction to Python for SEOs 2. Introduction to Machine Learning for SEOs 3. Leverage SOTA models with one line of code 4. Exploring Transfer Learning with T5 5. Deep Learning on Steroids with the Power of Knowledge Transfer! 6. MarketMuse First Draft
- 50. About RankSense Automate tedious SEO tasks in Google Sheets. Import the sheets and deploy them as experiments to Cloudflare. Learn which changes are effective. https://www.ranksense.com @RankSense#SEJLive