NLP, or natural language processing, is a field of artificial intelligence that focuses on computer-based methods to identify and extract information from natural human language (spoken and written).

In other words, NLP is the study of how computers can understand human language.

What is Natural Language Processing?

If you’ve ever used Siri or Google Search to:

  • Takedown a note
  • Find the time
  • Set the alarm
  • Search for an address in your contact list
  • Or perform similar tasks

You’ve already run into natural language processing.

In plain English, it is the ability for machines to understand and interact with language as it would be spoken naturally by humans.

It’s one of the hottest new technologies available today and can help computers better understand speech and react accordingly.

In this post, we’ll look at the most basic terms you’ll need to understand to get started in NLP. We’ll cover the terms, their meanings and give examples for each one.

And I’ll teach you how to approach NLP, so you can use it for yourself or for your career.

Why is NLP Important?

In the past, companies couldn’t process large volumes of unstructured data fast enough to stay competitive. This is why many companies began investing in Natural Language Processing technologies.

With Natural Language Processing, organizations can now more efficiently process and analyze large amounts of data quickly and consistently.

Allowing them to gain valuable insights to make better decisions.

How Does NLP Work?

NLP is what gives computers the ability to understand human language.

But languages are complex.

It’s not just about spelling and grammar. Before a proper understanding can be achieved, there are nuances, subtleties, and various elements that need to be considered, such as intent, tone, and context. That’s why multiple algorithms and techniques are used in NLP to interpret linguistic rules.

Natural language processing is most often a combination of computational linguistics rule-based and deep learning machine algorithms.

This paves the way for unstructured data to be processed and analyzed so that a computer can understand. 

The two main techniques used in NLP to achieve this are:

  1. Syntactic analysis
  2. Semantic analysis

 

Syntactic Analysis

Syntactic analysis refers to the process of analyzing a piece of text for syntactic correctness. In other words, it is a way of seeing if the provided text conforms to grammar rules.

Here are some of the main syntactic techniques used:

Morphological Segmentation

Morphological segmentation is the process of breaking a word down into its constituent parts, morphemes.

Tokenization

Tokenization is the process of breaking down text into smaller units or tokens. Tokens are a semantically independent combination of morphemes.

Stop Word Removal

A stop word is a word that is not meaningful to the text being analyzed. For example, the removal of words like “the,” “a,” “an,” “the,” and “of” from a text. To ensure words that provide the most value or meaning remain.

Lemmatization and Stemming

Lemmatization is the process of analyzing a sentence and dividing it into its lemmas, the smallest meaningful units that can stand on their own in a sentence.

Stemming cuts off the prefixes or suffixes found in inflected words.

The goal is to reduce inflectional words into their root forms.

Part of Speech Tagging

Part-of-speech tagging is a process of assigning a part-of-speech tag to each word, such as nouns, verbs, and adjectives. This is usually done by examining the surrounding words and context.

Semantic Analysis

Semantic analysis is the process of identifying the meaning of words, phrases, and sentences.

It involves extracting information about the semantic context of a word in a text. And can be done on individual words and larger chunks of text.

 Here are some of the common semantic analysis techniques:

Named Entity Recognition (NER)

Named Entity Recognition (NER) is the task of identifying words that represent people, organizations, locations, dates, times, monetary values, quantities, percentages, and other named units of information in text.

Named Entity Recognition can be used for many purposes, including information extraction and question answering.

Word Sense Disambiguation (WSD)

Word sense disambiguation is the process of determining the correct meaning of a word in a given context.

Natural Language Generation (NLG)

Natural language generation is a subset of Natural language processing that automatically produces natural-sounding sentences that mimic real human speech.

This includes generating text that sounds natural (in other words, it is not robotic).

Natural Language Understanding (NLU)

Natural language understanding (NLU) refers to the process of extracting information about the context, structure, and intent of natural language input, including written and spoken language.

It is part of the broader field of natural language processing (NLP).

The primary task of NLU is to identify a meaning of a specific set of words (lexical analysis) and determine the meaning of a phrase or sentence (semantic analysis).

Benefits of Natural Language Processing

Natural language processing has many benefits. Too many to list in an article, so I’ll just mention a few.

Automation

One of the most prominent use cases for NLP is to automate tedious manual tasks. In healthcare, for example, NLP can be used to assist in clinical documentation.

Speed

One of the most important uses for NLP is the speed it can provide. You can have a computer look at a document and generate reports based on its findings in mere seconds. This allows for faster turnaround times for business needs.

Natural language processing can be much faster when performing tasks such as information extraction, semantic analysis, and question answering than traditional data collection and analysis methods.

Efficiency

Traditional methods of collecting and analyzing data usually require many resources, including money, time, and people.

Natural language processing can be done by machines with minimal human involvement. This is especially useful in industries with large amounts of data to analyze but not enough time or money to analyze it all.

Challenges of Natural Language Processing

Although natural language processing technology is advancing rapidly, it’s still very much a work in progress and far from perfect.

The main challenge of natural language processing is dealing with the ambiguity and variability of natural language.

Although humans are incredibly adept at using language, they are often unable to provide a clear, unambiguous definition of the concept or item that is being described. 

There is also an issue of polysemy. That is, words can have multiple meanings. For example, the term “bank” can refer to a building that serves as a place to keep money or the bank of a river.

Another issue is homonymy. This is when words sound the same but mean different things. For example, the word “bank” can be used to refer to the process of collecting depositing money, as well as the place where you put your money.

Furthermore, training a computer to understand context is complicated. It requires an understanding of various domains and types of data and the ability to discern between similar or synonymous concepts.

Examples of NLP Applications

Let’s go over some examples of NLP in action to show you how it’s being used in the real world.

 

Speech Recognition

Speech recognition is the process of converting spoken words into text.

This process is often used to convert speech into machine-readable text to be stored, transmitted, or analyzed. It is a vital component in many applications, including mobile phones, dictation, voice search, and automated customer service.

AI Writing Software

One of the biggest areas for growth in NLP is artificial intelligence writing software. This includes programs that can create, edit, format, and structure text. These can be used for things like creating social media posts, emails, or news articles.

Email Spam Filters

Spam filters use NLP to recognize patterns and frequent words spammers use, to identify spam messages.

Machine Translation

Machine translation is the automatic translation of one language to another.

While there is much room for improvement, machine translation is still a great help in translating long texts and documents.

Speech Synthesis

Speech synthesis is the process of converting natural language into synthetic speech.

For example, a text-to-speech program can transform a piece of text into a voice recording.

Information Extraction

Information Extraction (IE) is the process of extracting structured data from unstructured text.

It involves looking for key phrases, words, or sentences containing interesting information. This information can be stored in a database or used to build a knowledge base.

Sentiment Analysis

Sentiment analysis is a process of determining the degree of positivity or negativity a text conveys.

It is commonly used in social media and customer feedback.

Chatbots

Chatbots make use of NLP by learning from human responses and conversational patterns.

These bots then provide a more personalized and intelligent response than a simple yes or no answer.

They are used in customer service, information retrieval, question answering, and sales.

Virtual Assistants

Siri, Alexa, and Google Assistant are examples of virtual assistants.

These programs have been created to help users perform tasks such as making calls, sending messages, ordering items, and setting reminders.

Search Engines

Google search, Bing, Yahoo, etc., are all search engines.

These engines use NLP to analyze search queries to identify and return relevant content to the user.

Tools and Courses to Get You Started with NLP

If you’re looking to get started with NLP, there are a few open-source tools and recommended courses you can check out.

 

Natural Language Toolkit (NLTK)

Natural Language Toolkit (NLTK) is a Python module that provides tools for working with natural language data.

NLTK includes a parser for many commonly used formal languages (e.g., XML, HTML, RDF, and more), a set of useful statistical and linguistic functions, a library of corpora (texts and parsed text), and a collection of machine learning algorithms.

NLTK is free and open-source software released under the GNU General Public License.

Apache OpenNLP

Apache OpenNLP is a suite of Natural Language Processing (NLP) tools written in Java.

It includes a parser, tokenizer, part-of-speech tagger, named entity recognition, and sentence splitter. These tools are often used to perform NLP tasks such as

  • sentiment analysis
  • text categorization
  • document summarization
  • and information extraction.

Stanford NLP

Stanford NLP tools are open-source software tools used to analyze text for key phrases, sentiment, topics, and more.

These tools are available for free and are widely used by researchers in Natural Language Processing and related fields.

They include the following: Stanford CoreNLP, Stanford POS Tagger, Stanford Parser, Stanford Tokenizer, Stanford Named Entity Recognizer (NER), and more.

IBM Applied AI Certificate

The IBM Applied Artificial Intelligence Certificate Program is a course designed to provide students with a practical understanding of artificial intelligence (AI) and machine learning technologies and foundational skills for building and deploying applications in an AI-enabled world.

Udemy NLP Courses

Udemy offers a variety of natural language processing (NLP) courses that cover topics like machine learning, text mining, data science, NLP, and artificial intelligence (AI).

The course also includes modules on Python, R, and other NLP software that can process large amounts of data and modules on data visualization.

Final Thoughts

 

I hope this article has given you a simpler explanation of what is natural language processing.

The benefits of NLP technology are numerous. There is no doubt that it will continue to become more and more important as the world becomes more and more dependent on technology. As a result, companies must take steps now to prepare for these changes.

The future of natural language processing is bright.

Still, we must learn how to leverage it properly to reap all of the benefits it provides us.

 

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