autoscraper VS duckling

See if something like autoscraper or mlscraper suits your needs.

GitHub - alirezamika/autoscraper: A Smart, Automatic, Fast and Lightweight Web Scraper for Python

Not a library but a decent program to not reinvent the wheel I'm currently adding regular selector lookups back into it https://github.com/alirezamika/autoscraper

It depends on the website, but autoscraper was used to calculate similar nodes given the text to search. Not sure how it works now but it's open source.

Should check out this for method 1.

In this article, we will learn how to create a simple e-commerce search API with multiple platform support: eBay and Amazon. AutoScraper and FastAPi provide the ability to create a powerful JSON API for the date. With Playwright's help, we'll extend our scraper and avoid blocking by using ScrapingAnt's web scraping API.

Maybe you could try out this tool never used it myself https://github.com/alirezamika/autoscraper Or you would need to train a neural network which does the parsing for you but the accuracy won't be 100% with this method https://github.com/scrapy/scrapely To answer your question there is no way to write one program which scrapes different pages correctly because their structure is not the same and some of them probably will have protection which will block your scraper so that would need extra attention or additional code

For the reasons others have said I don't see it replacing 'traditional' scraping soon. But I am looking forward to it replacing current methods of extracting data from the scraped content.

I've been using Duckling [0] for extracting fuzzy dates and times from text. It does a good job but I needed a custom build with extra rules to make that into a great job. And that's just for dates, 1 of 13 dimensions supported. Being able to use an AI that handles them with better accuracy will be fantastic.

Does a specialised model trained to extract times and dates already exist? It's entity tagging but a specialised form (especially when dealing with historical documents where you may need Gregorian and Julian calendars).

[0] https://github.com/facebook/duckling

Ah, sorry: https://github.com/facebook/duckling

To understand what relative distance and direction is indicated from the reference point, I'd look into something like Facebook & Wit.AI's Duckling, and a custom classifier to identify if it's on the reference point ("corner of"), or some distance from ("200 meters southwest"). If you can parse out a distance and direction, then it's all logic to plot the point.

It also powers the backend of Wit.ai which FB owns. Wit's open-source entity parser, duckling, is written entirely in Haskell. https://github.com/facebook/duckling

Our RandomPicForLater intent will have one slot called reminderTime and will be of type @duckling.time. Duckling is a library that extracts entities from text, and it is one of the tools used in JAICP for this purpose. Entity types in Duckling are called dimensions and there's a number of them built in, among them is Time which suits us perfectly since we need to ask users when they want us to schedule a post for and then parse a text input into a datetime object.

I'm working on a pretty interesting project right now and I'm having different results depending on the build tool used: with cabal, the test suite fails but it passes with stack.

Try downloading the v0.2.0.0 release, extracting it somewhere, opening that location in powershell, and running these commands:

Duckling (https://github.com/facebook/duckling) is a library for parsing text into structured data.

If you really want high precision, Duckling is a good project to check out https://github.com/facebook/duckling