What is natural language processing? A beginner’s guide to AI.

May 24, 2022

Artificial intelligence is often shrouded in mystery, and natural language processing (NLP) is one of the most complex aspects of AI. NLP allows machines to understand human speech and naturally interact with humans.

In this blog post, we will explain NLP and how it works. We will also discuss the benefits of using NLP for consumer data and intelligence.

So let’s get into it!

What is Artificial Intelligence?

Before getting stuck into natural language processing, we need to lay the groundwork and explain artificial intelligence.

In its simplest form, Artificial intelligence (AI) is the science of using a machine to automate tasks that a human would otherwise perform. AI machines use algorithms to undertake problem-solving operations, often processing high volumes of real-time data.

A machine that can naturally interact with humans is said to have “human-like intelligence”. But in reality, AI simply automates basic human tasks at volumes and speeds that humans cannot. 

One of the primary issues around the use of AI is the amount of disinformation and myths about its capabilities.

We’ve all seen the movies where robots become self-aware and overthrow their human masters (think The Terminator or Ex Machina). But in reality, AI is nowhere near this level of sophistication.

It’s also not going to put everyone out of a job. Yes, AI will automate some tasks and put people out of work. But it will also create new job opportunities in its wake.

For example, as more businesses adopt AI, the demand for people with data science and machine learning skills will increase. So, if you’re looking for a career change, now might be the time to upskill in these areas.

AI is already being used in several industries, including healthcare, finance, retail, and manufacturing.

AI is used in healthcare to diagnose diseases and predict patient outcomes. In finance, AI is being used to detect fraud and financial risks. In retail, AI is being used for customer service and recommendations. And in manufacturing, AI is used for quality control and predictive maintenance.

And now, we are entering the world where we can use machines to understand and predict likely human behaviour. The irony doesn’t escape us!

How does natural language processing fit into all of this?

Natural language processing (NLP) is a subfield of AI that deals with the interaction between computers and humans using natural language. We’ll define natural language as any language spoken or written by humans.

NLP deals with how computers can understand human language and respond in a way that is natural for humans.

It involves understanding the meaning of words and sentences and the context in which they are used.

Essentially, what this means is that NLP can be used to make computers understand human language and respond accordingly.

For a computer to understand human language, it must first be able to process natural language. This is where NLP comes in.

How does NLP work?

NLP is used for various tasks, including automatic summarization, question answering, machine translation, and text classification.

To carry out these tasks, NLP algorithms must be able to perform two main functions: text preprocessing and feature extraction.

Text preprocessing

Text preprocessing includes tasks such as tokenization, lemmatization, and stopword removal.

Tokenization is splitting a string of text into smaller pieces or tokens.

Lemmatization is the process of reducing a word to its base form. For example, the words “am”, “are”, and “is” are all lemmatized to the word “be”.

Stopword removal is the process of removing words that are not relevant to the task at hand.

For example, in the sentence “I am going to the store”, the word “I” is a stopword and can be removed.

What texts preprocessing does is help the machine learning algorithm focus on the text’s important words.

Feature extraction

Feature extraction is taking a set of data and reducing it to features most relevant to the task. For example, if we were trying to build a machine learning model to classify emails as spam or not spam, some features might be the presence of certain words in the email or the length of the email.

The goal of feature extraction is to take a set of data and reduce it to the features that are most relevant to the task at hand.

This process can be done in several ways, but some common methods are:

Bag of words: This method takes a text and creates a vector of word counts. This is a simple but effective method of feature extraction.

TF-IDF: This method takes a text and creates a vector of word counts, but each word is given a weight based on how common it is in the text. This can be helpful if you want to give more weight to rarer words.

Word Embeddings: This is a more advanced method that considers the context of words in a text. For example, the word “bank” could refer to a financial institution or the side of a river. Word embeddings try to capture this context and can be very effective features for machine learning models.

Machine learning algorithms

After the text has been preprocessed and the features extracted, it can be fed into a machine learning algorithm.

There are many different machine learning algorithms, but they can be broadly classified into two categories: supervised and unsupervised.

Supervised learning algorithms require a training dataset labelled in some way.

For example, if we were trying to build a machine learning model to classify articles as either “sports” or “non-sports,” the training data would need to be labelled accordingly.

The machine learning algorithm would then learn from this training data and be able to make predictions on new, unlabeled data.

On the other hand, unsupervised learning algorithms do not require labelled data. These algorithms try to find patterns in the data without knowing what they might be.

The point of using either a supervised or unsupervised machine learning algorithm is to make predictions on new data ultimately.

So, for example, if we were using a supervised learning algorithm to build a model that predicts whether or not a customer will churn (i.e. stop being a customer), we would need labelled data to train the model.

Once the model is trained, it can be used to make predictions on new, unlabeled data.

How does NLP work with non-English languages?

With most NLP examples, English is used because it is the most popular language and has the most data available for training models.

However, NLP can be used with any language.

D/A is one of the few companies that have successfully applied NLP to Arabic, which is a notoriously difficult language to work with due to its complex grammar rules.

Through D/A’s proprietary consumer insight engine, Sila, they can analyze Arabic social media and digital data and extract otherwise unavailable insights.

This is a game-changer for brands who want to understand their Arabic-speaking customers better but have been limited by traditional data collection and analysis methods.

You can listen to our latest podcast for more details on how D/A uses AI and NLP specifically to understand Arabic consumers. 

How AI and NLP can help your business:

Data-driven marketing relies on audience data sets to make informed decisions about allocating marketing budgets. Traditionally, this data has been collected through surveys and focus groups, which are time-consuming and expensive.

With the advent of social media, brands now have access to a wealth of customer data that can be used to understand consumer behaviour. However, this data is often unstructured and hard to analyze. This is where NLP comes in.

NLP can help brands extract insights from social media data by understanding the context of the conversation and identifying key themes and sentiments.

This allows brands to make more informed decisions about allocating their marketing budgets and how to appeal to their target audience. D/A is at the forefront of NLP for Arabic dialects, helping brands understand what their customers are saying across social media.

Our team of expert linguists and data scientists has developed a unique set of algorithms that can accurately identify sentiment, emotions, and intent in Arabic text. This allows us to provide our clients with actionable insights to help them improve their marketing strategies and better understand their customers.

If you’re looking to get ahead of the competition and unlock the power of Arabic NLP, contact D/A today. We’ll be happy to show you what we can do.

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