Learn Most, Natural and Redwoodjs

In this course we’ll work through Natural’s API for natural language processing in JavaScript. We’ll look at how to process text: learning how to break up language strings, find the word roots, work with inflectors, find sequences of words, and tag parts of speech. We’ll learn how to find important stats about a body of text: how to compare strings, how to classify text with machine learning, how to use the tf-idf tool to find relevant words. We’ll look at some of the extra tools Natural gives us, including the dictionary/thesaurus of WordNet, a phonetics comparer that lets us see if two words sound the same, and a spellcheck feature. We’ll also look at tries and digraphs, two data structures that help us better analyze bodies of text.

Natural is a general natural language facility for nodejs. Tokenizing, stemming, classification, phonetics, tf-idf, WordNet, string similarity, and some inflections are currently supported.

natural

Natural Language Processing in JavaScript with Natural

RedwoodJS takes the JAMstack philosophy to the next level. It brings together all the tools modern web development has to offer:

- React
- GraphQL
- Prisma

RedwoodJS abstracts database management away from you so that you can just build your schema and it will do all the plumbing. This collection will go over how to create a RedwoodJS project and the scripts that will help make your developer experience a joy.


Bringing full-stack to the JAMstack

Do you love the JAMstack philosophy but need a database-backed web app? Want great developer experience and easy scaling? Redwood is here! Built on React, GraphQL, and Prisma, Redwood works with the components and development workflow you love, but with simple conventions and helpers to make your experience even better.

redwoodjs

Introduction to RedwoodJS: full-stack framework for JAMstack

React is one of the web’s most popular frameworks for building JavaScript applications. 

If you know what you’re doing, React can drastically simplify how you build, use, and maintain code.

Whether you’re a React newbie or you’re ready for advanced techniques, you can level-up with egghead. Explore our in-depth courses, lessons, and community resources to build more powerful applications and crack open your career.

react

We use `yarn redwood generate page` (`yarn rw` for short) in this lesson to generate an `IndexPage` component. 

The first argument to `yarn rw generate page` is the name of the page you are generating. The second argument is the path you want the page to show up on. So `yarn rw generate page index /` will generate an `IndexPage` component and insert a route entry in our `web/src/Routes.js` file.

We learn that redwood import `React` for you so you don't have to import it manually when you are creating a component.

Generate a Page Using the Redwood CLI

In this lesson, we are going over how to generate a `RedwoodJS` project.

We can create a `RedwoodJS` project with `yarn create redwood-app ./my-redwood-project`.

`yarn` will generate the `RedwoodJS` app in the file folder you specified without installing any globals.

To run the `RedwoodJS` app, we use `yarn redwood dev`. This runs the Redwood client as well as the `GraphQL` backend.

Generate a Redwood Project

RedwoodJS provides a `Link` component that we can use to navigate around our app. We can provide a `to` prop with the path that we want to navigate to.

RedwoodJS takes this a step further and provides a `routes` object from `@redwoodjs/router`. This object is aware of all the routes defined in `web/Routes.js`. This allows us to use the methods on this object rather than referencing the our routes with a string literal. We can then change the route path without having to change all of the references to that route all over our app.

Use RedwoodJS Link and routes Object to Navigate

RedwoodJS gives us a cli command to generate layouts.

`yarn redwood generate layout main`

This will generate a `MainLayout` component in `src/layouts/MainLayout/`. We can use this layout component to define any headers or footers we want to share across multiple pages. 

Extract a RedwoodJS Layout Component

We will learn about “stemming,” the process of finding the root of words, often in order to group words by a common base root. We will look at the Porter and Lancaster Stemmers, briefly touch on Natural’s support for Russian and Spanish stemmers, and introduce the function to stem and tokenize at the same time.

Find the roots of words using stemming in Natural

Inflectors are the modifiers of a word that indicate grammatical categories. While Natural’s coverage of inflectors is not comprehensive, we will show how Natural can pluralize/singularize nouns and count numbers. 

Pluralizing nouns and counting numbers with inflectors in Natural

N-grams are sequences of words, where the 'n' stands for the number of words in the sequence. In this lesson, we will see how to find bigrams (2-grams), trigrams (3-grams), and any other length n-gram in a body of text. 

Find sequences of words (n-grams) using Natural

We will learn how to compare how similar two strings are to each other, examining three algorithms: Jaro-Winkler, Levenshtein, and Dice’s Coefficient.  

You should note that none of these algorithms are inherently better than the others. Instead, it's important to choose the one that best fits your text data. 

Compare similarity of strings through string distance in Natural

In this lesson, we will learn how to train a Naive Bayes classifier or a Logistic Regression classifier - basic machine learning algorithms - in order to classify text into categories.

Classify text into categories with machine learning in Natural

In this lesson, we will learn how to train a Naive Bayes classifier and a Logistic Regression classifier - basic machine learning algorithms - on JSON text data, and classify it into categories.

While this dataset is still considered a small dataset -- only a couple hundred points of data -- we'll start to get better results.

The general rule is that Logistic Regression will work better than Naive Bayes, but only if there is enough data. Since this is still a pretty small dataset, Naive Bayes works better here. Generally, Logistic Regression takes longer to train as well. 

This uses data from Ana Cachopo: http://ana.cachopo.org/datasets-for-single-label-text-categorization

Classify JSON text data with machine learning in Natural

Tries are a data structure that provide an efficient way to search for the existence of a word or phrase in a body of text, or to search by prefix. 

Search more efficiently with tries using Natural

Most.js is a toolkit for reactive programming. It helps you compose asynchronous operations on streams of values and events, e.g. WebSocket messages, DOM events, etc, and on time-varying values, e.g. the "current value" of an <input>, without many of the hazards of side effects and mutable shared state.

most

Most provides many means for creating streams, the simplest of which is the `of` function. In this lesson, we demonstrate the use of `of` to lift a single value into a stream. We also show off the `just` alias of `of`, as well as, a common method for currying a function.


Create Streams From Single Values With Most.js

In this lesson, we’ll see how to use Natural’s probabilistic spell-checker, which uses the trie data structure. 

Include spell-check in text projects using Natural

Tf-idf, or term frequency-inverse document frequency, is a statistic that indicates how important a word is to the entire document. This lesson will explain term frequency and inverse document frequency, and show how we can use tf-idf to identify the most relevant words in a body of text. 

Identify the most important words in a document using tf-idf in Natural

One of the more powerful functions for creating streams in MostJS is the `from` function.

In this lesson, we use `from` to create streams from various Javascript Iterables, take a look at how to throw generator functions into the mix and, for good measure, take a sneak peek at how we can limit our streams using a function called `take`.

As a bonus, we see what it looks like to use `Function.prototype.bind` for a quick and dirty way to partially apply arguments to a function.

Create Streams From Iterables With Most.js

In this lesson, we’ll take a look at Natural’s phonetics feature. We’ll learn how to check whether two words sound alike, looking at both the SoundEx and Metaphone algorithms.

Check if words sound alike using Natural

This lesson introduces WordNet, which is an important resource in natural language processing. With WordNet, we can look up a word’s definition, or find its synonyms.

Find a word’s definition using WordNet in Natural

An important component of many natural language processing projects is being able to identify the grammar of a piece of text. We’ll learn how to do that with Natural’s parts of speech (POS) tagger.  

There are many tags, and it's worth looking them up online (search "POS tag symbols") to become familiar with them all. 

The setup of the tagger may seem a little strange, but it allows you to replace the lexicon or the rules with a different lexicon or rule set of your choice. 

Tag parts of speech using Natural

By this point we've seen that classification can take a long time, and with more data, it would take even longer. Luckily, Natural provides support to save your classifiers. In this lesson, we will learn how to save a classifier and load it into a new project in order to classify new data.

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