Finding Related Words with NLEmbedding

Published on March 4, 2020

There may be cases in which you need to find related words to others. With the NSLEmbedding class, you can find related strings based on the proximity of their vectors.

Using NLEmbedding

Using NLEmbedding is very straight forward. A simple task is to get an array of related words, which come as an array of (String, NLDistance) back.

The distance between words tells you how “related” they are

let embedding = NLEmbedding.wordEmbedding(for: .english)
let foundWords = embedding!.neighbors(for: "family", maximumCount: 3)
print(foundWords)

In this example, it will print:

Analyzing Natural Language Text with NLTagger

Published on February 26, 2020

In the past few weeks, we have explored how we can tokenize natural language text and how to recognize the language a natural language text is written in. This week we will continue exploring more natural language APIs provided by the NaturalLanguage framework. We will learn about the NLTagger class, which allows us to to analyze natural language text to find parts of speech, lexical classes, lemma, scripts, and more. This API, introduced in iOS 12, implements machine learning to work, and just like the other NaturalLanguage classes, is very easy to use.

Recognizing the language in a Natural Language Text with NLanguageRecognizer

Published on February 19, 2020

Continuing my trend of writing about language processing, today I want to discuss about identifying the language of a body of text. This is an interesting task we can do thanks, once again, to Apple’s investment in APIs linked to machine learning.

Today we will explore the NLLanguageRecognizer object. Introduced in iOS 12, this class can do a lot of language recognizing, from detecting the “dominant language” of a string, to all the possible languages.

Tokenizing Natural Language into Semantic Units in iOS

Published on February 12, 2020

Working with Natural Language is possible thanks to machine learning. Starting on iOS 12, Apple has provided many APIs just for this task. In this article we will explore how to use NLTokenizer to separate natural language text into its proper units.

Introduction to Natural Language Tokenizing

If you are not familiar with the inner workings of Natural Language processing, tokenizing simply means that we separate a string and analyze it to find its semantic units. If you are writing a program that processes text, you may be tempted to split the string using a separator. For example, if you wanted to get all the words in a natural sentence string in an array, you would write something like this:

Matching Natural Language Text for Predefined Data Patterns on Apple's Devices

Published on February 5, 2020

iOS has a lot of APIs that deal with natural language detection. One such class is NSDataDetector. This class allows you to match different kinds of data in text, including dates, time, links, and more. This class, actually introduced a very long time ago (in the iOS 4.0 days!) makes it very easy to find this kind of data in strings. In this article we will explore how to use this very old class - whose documentation is Objective-C only at this time - in Swift, and how to do common tasks with it.