angular VS spaCy

AngularJS (UI framework built in JavaScript): open (last commit: Apr 12 2022), unsupported Angular 2 to 12 (UI framework built in TypeScript): open, unsupported Angular 13 to 15 (UI framework built in TypeScript): open, supported until May 4 2023, Nov 11 2023 and May 18 2024 AngularDart (UI framework build in Dart): open (last commit: Oct 7 2021?, Today?), supported by community?, they say no Flutter 1: open (last commit: Mar 30 2022), unsupported Flutter 2: open (last commit: Apr 18 2022?), couldn't find if still supported, I assume unsupported Flutter 3: open (last commit: Today), supported

eh.. kinda

Angular Dart. https://pub.dev/packages/angular It seems to be unmaintained, but for a personal project it can fit your needs.

"Angular DART" might be "AngularDart";

By the same mechanism as in the first approach, the custom or default content will be rendered by SlotRendererComponent. The reason why this solution is not working is due to @ContentChildren not being able to query nested ng-contents. Setting { descendants: true } also did not work for me. I found an issue describing the problem for the AngularDart repository so maybe it is related (or I am doing something wrong here ;) ).

Let's start with some words about the language. I honestly didn't like Dart at the beginning, when I first read about it some years ago. Dart started as a language that compiled to Javascript, and was advertised as a nice integration to the web ecosystem, especially to Angular, which is still there but not being actively recommended by the AngularDart team.

spaCy https://spacy.io/

I chose spacy. Although it's not state of the art, it's very well established and stable.

noun_chunks: The noun phrase (extracted by spaCy) that have associated bounding boxes (predicted by GLIP). The items in the children list respectively represent 'Start of the noun chunk in caption', 'End of the noun chunk in caption', 'normalized x_min', 'normalized y_min', 'normalized x_max', 'normalized y_max', 'confidence score'.

You could try spaCy. This is the brains of the operation - an open-source NLP library for advanced NLP in Python. Another is DocArray - It's built on top of NumPy and Dask, and good for preprocessing, modeling, and analysis of text data.

In this example given in the article, I can't just use SQL functions to extract the age and phone number. I guess the phone number could be regexed but ideally I should use something like spaCy and also record some kind of confidence score. This is where Spark/Dask/etc really shine. Does Airbyte support user defined functions in a language like Python?

(1) For large scale processing/tokenizing your data I would consider using something like NLTK or Spacy. That's if your books are already in text form. If they are scans, you'll need to use some OCR software first.

Tokenize with NLTK, SpaCy or CoreNLP

If you've got a specific area you're looking at, and already have street data, you could: 1. Follow the ArcGis blog's directions, creating intersection features. 2. Train a classifier (or a specific NER entity type; SpaCy would be a good package for that) on the types of cross-street references you're finding in your text. You can see some of the relevant tokens in the examples you provided - "Corner of", "along", and I'd imagine "intersection of" etc. Even simple string lookups could help you bootstrap the training data. 3. Use some sort of embedding similarity to compare the hit terms to potential cross-streets.