We’ve all been in a situation where we’re driving through traffic and have to make an emergency call. “Hey, Siri! Call ‘Mom’” is a common response.
The truth is, we have become increasingly reliant on voice assistants such as Alexa, Siri, Google Assistant, and several others.
Bonus Read: Implementation of NLP in Industries
These machines are powered by NLP (Natural Language Processing), the mechanism that builds devices capable of responding with their speech to text or voice data. They are quite similar to humans, despite not having a physical form.
Let’s take a look at the basics: What natural processing is, its applications, and where you can get started in NLP.
What is Natural Language Processing?
Before we dive into how you can learn about NLP, let’s first dive into what NLP is.
Natural Language Processing is a branch of computer science that deals with Artificial Intelligence. It strives to construct machines similar to humans that possess the ability to comprehend text and speech.
NLP has combined two technologies: computational statistics and machine learning models. The rule-based modeling of human language equips machines with the ability to understand human language (in text or voice data form).
This mechanism doesn’t just enable AI to comprehend the text of a speaker, but also their sentiment.
Programs including translators (translating from one language to another), responding to spoken instructions, and summarizing large volumes of text, are all driven by NLP.
NLP-operated programs have become a dominant part of our daily lives. Most of us have operated text-to-speech programs, digital assistants, chatbots, and even GPS systems at some point in our lives.
But how does NLP benefit enterprises?
NLP boosts productivity by not just assisting business employees, but also streamlining business processes. It also plays a role in simplifying complicated business processes.
How to Learn Natural Language Processing and Machine Intelligence
Humans communicate with idioms, metaphors, sarcasm, grammar, homophones, and several other variations in sentence structure. This makes it even harder to develop software that interprets what a speaker is saying.
The intended meaning of voice data needs to be taught to AI to make sense of and respond to data in the form of text. Programmers need to teach this to natural language-driven apps to maximize their efficiency and ensure they fulfill their intended purpose.
NLP tasks help computers understand what a person is saying by breaking down the text into comprehensible pieces of data. Here are some of the NLP tasks:
- Speech Recognition:
Speech recognition is also referred to as “speech-to-text” and is the process of converting voice data into the form of text.
This task is necessary for programs that either answer spoken questions or fulfill commands inputted in the form of text. All voice assistants are based on this process.
The development of speech recognition is not simple, considering the varying accents and the way people talk.
Most of the time, incorrect grammar is used, words are slurred, and emphasis and intonation are different for every individual. This makes it even harder for developers to come up with a hard-and-fast rule for speech-to-text programs.
- Grammatical Tagging:
The use of a part of speech can be different, depending on how it is used in a sentence. For example, in the sentence, “He makes paper planes”, the word ‘make’ is used as a verb, while the same word is used as a noun in, “The make of this car is particularly unique.”
Recognizing what the same word means in a different context such as the example given above is referred to as grammatical tagging or part of speech tagging.
- Co-reference resolution:
Often pronouns are used interchangeably with names in sentences.
For instance, one might say, “Abigail is sleepy” followed by the sentence, “She stayed up late last night.” In the second sentence, the word ‘she’ refers to Abigail.
Co-reference resolution is the process that determines when two different words refer to the same entity.
However, it may also be used to identify and comprehend metaphorical sentences. For instance, “night owl” doesn’t always just refer to the nocturnal bird.
- Analyzing Sentiments:
Text is a form of data that also involves emotion apart from meaning.
Explicit meanings are often extracted from text through sentiment analysis which helps the program identify emotion, confusion, suspicion, and sarcasm.
How to Learn Natural Language Processing and Where to Get Started?
One important part of machine learning in NLP.
Basic programming skills are useful for natural language processing. For example, Python is recommended, but not necessary, when you begin with NLP.
Here are the three unsurpassed resources to get started with NLP:
- Online Courses:
One of the best resources to get started is online courses. Dan Jurafsky and Chris Manning have an excellent introductory video series on this part of machine learning.
Coursera also offers an introductory course on NLP through the University of Michigan.
For a more advanced course, Stanford CS224d: Deep Learning for Natural Language Processing is an excellent resource that covers advanced NN architectures for NLP, as well as cutting-edge ML algorithms.
For the basics, one of the best books to refer to is Speech and Language Processing by Jurafsky and Martin. The NLP book covers all the basics of natural language processing, how it works, and other information.
For a more profound introduction to NLP, Neural Network Methods in Natural Language Processing by Goldberg is a good place to start.
And for more advanced learning of NLP, Manning and Schütze’s book, Foundations of Statistical Natural Language Processing is an exceptional resource.
- Open-source Libraries:
fastText, spaCY, NLTK (Natural Language Toolkit), AllenNLP, Stanford CoreNLP are all outstanding open-source libraries that offer comprehensive information on NLP
Did you know that the first NLP apps were coded by hand and could not interpret large volumes of text and speech data? These rule-based systems couldn’t accomplish much.
NLP has come a long way since and has become a dominating part of our lives.
As an essential component of machine learning, learning about NLP can be quite beneficial in understanding how voice assistants, GPS systems, etc. are programmed to perform complex tasks.