This method is very useful for providing suggestions based on bad spelling. In the example above, we have inserted an i thus the Levenshtein distance will compute the string based on an insertion. As you can see, there are many ways of spelling “Daine” – Levenshtein distances can get incredibly complex as it uses a number of parameters referred to as: The above will log out 1 meaning that the two strings match in context of the algorithm. Luckily, we can use a tokenizer to do so, have a look below: TokensĬonsole.log(natural.LevenshteinDistance("Daine","Dane")) I found this article title on Web Designer Depot:Īnalyzing the entire string is one thing, but we want to be able to perform methods on individual words in order to extract more data from them. For this project, we’ll be using Natural.įor the sake of brevity I’m just going to cover the most useful methods that would be quick to implement into your own projects and iterate on. NLP is usually performed on a string of words.Īs a developer using an NLP library I can extract a ton of information that could help perform almost any task I like. The search form on Google’s home page is incredibly complex when it comes to analyzing what, we as humans have typed into the form input. Ever searched on Google and spelled a word incorrectly? Ever noticed how Google then tells you, “Displaying results for x as well” – well that’s some NLP at work. Natural Language Processing by definition as stated on Wikipedia, refers to: “the application of computational techniques to the analysis and synthesis of natural language and speech.” – Lets break that down.Īn NLP library will help you perform relatively complicated data extraction on string. Content editing features for content producers like spell checks, syntax and more.Text to Speech integrations, similar to services like Amazon Polly. Chatbot integration for a better understanding of user input / conversation.More intelligent search suggestions and search results.To be clear, what we’re going to explore today is only the tip of the iceberg when it comes to Machine Learning and my examples are going to be quite rudimentary, however Natural Language Processing is quite an exciting prospect and has some amazing, yet creative use cases for your projects, examples of those would be: I’m by no means a Machine Learning expert, but I have brushed the surface of it in a couple of development projects which required a “smarter” way of doing things. var proc = require('child_process') var commands = ::In.For a couple of years now, AI and Machine Learning have been taking over web forums as well as adding to the excitement of eager developers who are keen to give it a go in their projects. In order to do that, we will use child_process module, which shipped with standard node. We have to invoke PowerShell process and pass arguments there. Now, when we understand how to execute TTS engine on Windows, let’s move on to NodeJS. Rule the world! ' $tts.SpeakSsml($Phrase) Like that: Add-Type -AssemblyName System.speech $tts = New-Object $Phrase = ' Normal pitch. You can even use SSML to generate everything you want. You can play with speed, pitch, or type of voice, by adding following commands: $tts.Rate = -5 # -10 to 10 -10 is slowest, 10 is fastest
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