How to scrape poetry sites


The topic of scraping poetry or other types of websites involves technical discussions. In the podcast , the experts discuss using AI to scrape data from websites. They mention the common approach of using a basic scraper to collect data, which is then processed by AI for various tasks such as structuring information in a specified output format like JSON. This kind of method can be adapted to scrape poetry sites systematically and efficiently 1.

For a deeper understanding or specific techniques, consulting technical resources or tutorials in web scraping and programming would be beneficial.

AI Automation Conversations

Nathan and Div discuss using AI for information gathering and taking actions online, emphasizing the potential for AI to automate tasks like structuring information and executing actions on websites, offering a glimpse into the future of AI-driven automations.

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Learn about the fundamentals of web scraping.


The fundamentals of web scraping involve the automated extraction of data from websites. This process relies on tools like bots or spiders to navigate the web, gather information by parsing HTML and then storing the data. Here are some key points discussed in relevant podcast episodes:

  • Ethics and Regulations: A significant aspect of web scraping discussed by and includes the ethical and regulatory dilemmas surrounding data ownership and compensation. They emphasized the need for a balanced approach to ensure that web data owners are fairly compensated, while also making the data available for broader use, such as in search engines and AI applications like chat GPT 1.

  • Mechanics of Parsing: explains that HTML elements on web pages can be parsed for structured information using technologies like microdata. This simplifies the extraction process, as elements like product pricing are often explicitly delineated in the HTML for marketing benefits, which in turn aids in straightforward parsing and data extraction 2.

Understanding these principles and the ethical landscape forms a foundational knowledge for those looking to engage in web scraping.

Web Crawling Ethics

Auren and Oren discuss the ethics of web crawling, emphasizing the need for a balance between data access and ownership rights. They delve into the complexities of compensation models and the implications for businesses like Quora and Getty Images.

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Understanding AI Research with Oren Etzioni. Signs of AGI

Explain more about parsing techniques in web scraping.


Parsing techniques in web scraping are central to efficiently and effectively extracting relevant information from web pages. Here are some sophisticated parsing strategies that trusted experts have discussed:

  1. Probabilistic Parsing: This technique involves using statistical models to predict the structure of sentences. It assumes that language can be parsed based on probability distributions rather than strict grammatical rules. This approach was vital for early NLP applications and led to the development of models that could handle the complexity and variability of human language 1.

  2. Universal Abstract Syntax Trees (UAST): UASTs provide a unified interface for parsing code across different programming languages by using annotations. This concept allows for a more structured extraction of specific elements, such as function names, by abstracting the syntax details of each language. It simplifies the process by using a common parsing framework, regardless of the programming language involved 2.

  3. Structured Data Extraction:

    • HTML and DOM Parsing: Web pages structured in HTML can be broken into a Document Object Model (DOM), where each node represents part of the page (like text or images). This hierarchical model allows parsers to navigate and extract specific data systematically.
    • Microdata: When websites use microdata, they incorporate additional tags in their HTML to define specific types of data like prices or brands. This structured approach is advantageous because it makes the data extraction more straightforward and accurate 3.

      The Shift in NLP

      Christopher and Daniel discuss the shift towards corpus-based learning in natural language processing (NLP) and the excitement of applying machine learning to large collections of text. They explore the limitations of formal grammatical rules and the need for better models to capture the nuances of colloquial language. The debate between rationalist and empiricist schools of thought in NLP is also touched upon, highlighting the influence of linguistic theories on NLP implementation.

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      Christopher Manning: Linguistics and the Development of NLP

Each of these techniques focuses on reducing the complexity of parsing and enhancing the accuracy and efficiency of data extraction from various sources.