Natural language processing is one of the hottest areas of artificial intelligence. NLP spending has gone up to 30% in some markets, and the market for NLP products and services is bound to increase to more than $25 billion by 2024.
A closely related but different term is natural language generation. Applications of NLP and NLG are already a part of our lives.
This article will give you a birds-eye view of NLP and insights into its application in machine learning marketing and content creation.
Introduction to Natural Language Processing (NLP)
“Alexa, I like this song.”
The volume of the music decreases, and Alexa responds:
“Thank you John, I have noted your preference”.
At the back-end, Alexa adds the song to John’s playlist and changes its algorithm to increase the frequency of playback. Welcome to the world of NLP and NLG.
Natural language processing is a subset of AI that gives machines the ability to understand and derive meaning from human languages. In short, NLP is the ability of computers to comprehend what we’re saying. NLG is their ability to communicate with us in our language.
Every sentence we speak or write has three types of cues:
- Structural: Syntax, linguistics, and the rules of each language.
- Contextual: the message we are trying to convey.
- Emotional: tone and mood.
As humans, we have an instinctive understanding of these cues, and we respond accordingly. For machines, each written and spoken sentence is unstructured data that needs to be converted to structured data to enable the computer to comprehend what we are saying. This process is NLP for one language.
In our Alexa example, NLP converted John’s spoken sentence into structured data that Alexa understands. Based on that data, NLG triggered the responses, adding the song to the playlist, changing its algorithm for playback frequency, and converted the structured data back to language with the spoken response.
How NLP works
Natural language processing performs three core tasks:
1. Recognition
Computers need to convert written and spoken sentences into structured data (binary code) according to machine language rules to recognize them.
Some of these rules include:
- Tokenization and parsing;
- Lemmatization and stemming;
- Part-of-speech tagging;
- Language detection;
- Identification of semantic relationships.
These rules help computers to break down each sentence of speech and text into individual words and recognize things like the language, relationship between the words, syntax, and semantic rules.
The rules help convert unstructured data (speech and written text) into structured data that is a binary code (series of zeros and ones). We can look at NLP-based speech recognition as a process defined by these rules.
2. Understanding
A binary code is the output of the recognition stage. The understanding stage uses algorithms to run statistical analysis on the binary code to establish relationships and meanings.
Some of the processes used to achieve this include:
- Content categorization: Create a document summary based on linguistics.
- Topic discovery and modeling: Capture meaning and themes in text collections.
- Contextual extraction: Pull structured information from text-based sources.
- Sentiment analysis: Identify mood and opinion of the text or speech.
- Speech-to-text and text-to-speech conversion
- Document summarization: Generate a synopsis of large text blocks.
Since machines work on code, each of these processes needs to be written as a code before the computer can understand speech and text.
3. Generation
After analysis for recognition and understanding, the next step is generating responses through speech and text.
These responses are NLG-based. They convert the structured data and code back to a language. This involves programming the computer for a series of what-if scenarios and codification of the syntax and linguistics rules of the language.
NLP has its limitations because it lacks an intellectual understanding of language and it’s just predictive math.
NLP vs AI vs Machine Learning
While NLP, AI, and Machine Learning are interrelated, each has a different connotation.
NLP and Machine Learning are subsets of Artificial Intelligence. Artificial intelligence is an umbrella term used for intelligent machines that can simulate human intelligence.
Machine Learning and NLP are two of several applications that make up AI. To better understand the differences between the three terms, let’s look at each in a little more depth:
Artificial Intelligence
Artificial Intelligence allows machines to perform tasks that would earlier require human intervention. Today, computers routinely handle tasks like planning, problem-solving, and understanding languages.
AI works on algorithms designed around rules and probabilities. The algorithms allow the machine to learn from experience and apply this learning to make accurate decisions when presented with similar scenarios.
The ability to process and analyze vast amounts of data in milliseconds is the strongest suite of AI. Today, AI finds real-world applications in many areas, including digital assistants like Siri, customer support using chatbots, manufacturing, ecommerce, healthcare, tools for scheduling recurring emails, and tools that perform a grammar check on content.
Machine Learning
Machine Learning is an application of AI that allows machines to learn like humans. It’s the part of AI that enables systems to learn from experience and data input. There are three types of machine learning based on the learning process:
- Supervised learning (with human input);
- Unsupervised learning;
- Reinforced learning.
The learning process starts with observation of data, examples, inputs, and experience. Algorithms use statistical analysis to identify patterns in the data, and these patterns drive decisions. Machine Learning is concerned with pattern recognition and the accuracy of decisions
The aim is to create a self-sustained learning model within the machine. The classic machine algorithms treated text as a sequence of keywords, while algorithms today use semantic analysis to simulate human intelligence by understanding the meaning of the text.
Some common applications of machine learning include image and speech recognition, self-driving cars, traffic prediction, and product recommendations in e-commerce.
Natural language processing
NLP is another application of AI. Humans and computers communicate differently: humans use spoken and written words, while computers use binary code. NLP is the bridge between words and numbers.
Here’s an example of NLP at work:
In this example, a user uses spoken language to communicate with Alexa. In turn, Alexa uses speech recognition to break down sounds into recognizable words, then feeds the words into a cloud-based service that uses NLP to convert these words into calculable values. Alexa then comes up with a numerical response, then uses NLP to convert the numbers into words that are then transmitted to the user.
Because Alexa is equipped with machine learning technology, every question it’s asked adds to the server’s pool of knowledge. When another user asks the same question, Alexa is now able to supply the answer faster.
Machine learning and artificial intelligence are crucial to the development of NLP. While artificial intelligence helps machines figure out natural language, machine learning helps systems teach themselves natural language. AI and ML work together to create intelligent systems that don’t just understand natural language, but also teach themselves new languages as they go along.
NLP and machine intelligence are two components of artificial intelligence that deal with different aspects of AI. NLP and machine language work together to create intelligent systems.
NLP: The evolutions and Google’s movement
Alan Turing is the father of Natural language processing. In his 1950 paper Computing Machinery and Intelligence, he described a test for an intelligent machine that could understand and respond to natural human conversation.
NLP has evolved based on the evolution of its algorithms. As the algorithms got smarter and more complex, so did NLP’s capabilities. The graphic highlights the evolution of the algorithms:
Bag-of-words was the first model used in NLP. It involved counting the word frequency in a given document. However, the model had limitations in real-world applications where analysis needed to cover millions of documents.
Another issue was the frequency of common words like “is,” “a,” and “the.” This problem gave birth to TF-IDF, where common words were designated as “stop words” and excluded from the count.
The co-occurrence matrix was the first algorithm to address the semantic relation between words. The algorithm used word embedding to track the sentiment and context of the text. The drawback with the matrix was the memory and processing power required to store and run the algorithm.
Word2Vec was the first algorithm based on neural networks. It used current techniques like Skip Gram and Fast Text. The model uses character level information to generate text representation.
Transformer models use encoders and decoders (converting text and speech to binary code and converting binary code back to text and speech) to enhance NLP capabilities.
ELMo addressed the issue of homonyms (one word with multiple contexts) in speech and text.
Consider the following examples:
- “I like to play baseball.”
- “I am going to watch a Julius Cesar play tonight.”
The word “play” has two different contexts in the sentences above. To understand the context, you have to evaluate the word “play” along with the rest of the words in the sentence.
Google’s contribution to NLP: BERT
Google’s contribution to the evolution of NLP is BERT, its neural network-based algorithm for natural language processing. BERT is an acronym for Bidirectional Encoder Representations from Transformers.
BERT is an open-sourced code that allows anyone to create their own question answering system. It uses transformers that evaluate the relation of a word with all the other words in the sentence.
BERT is used in Google’s Search feature to understand the context of each search query and provide the most relevant results. BERT will enable NLP to progress to the next level with complex models that push the limits of traditional hardware.
Impact of NLP on Content Creation and Marketing
According to Salesforce, over 50% of digital marketers also use NLP for content creation and marketing. NLP is making a positive contribution to content creation and marketing in these areas:
- Using predictive intelligence to deliver a unique customer experience;
- Creating and curating content;
- Data-driven marketing strategies.
Digital marketers are increasingly using NLP applications as part of their content marketing strategies to drive customers through the marketing funnel.
1. NLP and user experience
Predictive intelligence provides a structure to raw data generated by businesses. It also impacts lead scoring and in identifying the customers who are ready for conversion. Once you identify the customer’s position on the buying journey, you can target them with relevant content.
Predictive analysis allows you to select the content that best serves the customer’s need at each stage in the marketing funnel. The targeted content helps in maximizing the user experience.
2. Creating and curating content
Content marketing requires daily curation of content. Creating engaging content relevant to customers at different stages of the marketing funnel is resource-intensive.
Identifying trending topics and researching keywords is time-consuming. NLP allows content marketers to create content relevant to audiences at different stages of their purchase journey, thereby raising engagement levels and conversion rates.
3. Data-driven intelligent strategies
Content marketers have traditionally relied on manual sorting of data while building their content strategies. Manually sorting high volumes of data runs the risk of the signal getting lost in the noise. NLP does a much better job of sorting through online data to create data-driven content.
NLP systems analyze manually created content to evaluate the projected performance of the content. NLP systems compare the content against similar content across websites and offer suggestions on areas like title, headings, keywords, and the context of your content. NLP tools allow you to create smarter and more impactful content.
Using NLP for more intelligent content
Natural language processing is the ability of machines to read and understand speech and written text. NLP, NLG, and machine learning are applications of artificial intelligence.
NLP is used for several real-world applications including, digital assistants, chatbots, and content creation and curating. The power of NLP is increasing as the algorithms become more complex and intelligent.
NLP is changing the landscape of content creation and marketing by improving user experience and creating engaging and relevant content for each stage of the buyer journey.
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