Natural language processing is one the most exciting areas of artificial intelligence. NLP spending has increased to 30% in certain markets. The market for NLP products is expected to grow to over $25 billion by 2024.
Natural language generation is a closely related term, but it is a different one. NLP and NLG applications are part of everyday life.
This article will provide a bird’s-eye view on NLP as well as insights into its use in machine learning marketing, content creation, and machine learning marketing.
Introduction To Natural Language Processing (NLP).
“Alexa! I like this song.”
Alexa responds when the volume of the music drops.
“Thanks John, I have taken note of your preference.”
Alexa adds John’s song to John’s playlist. She also changes the algorithm to increase playback frequency. You are now in the NLP/NLG world.
Natural language processing, a subset in AI, gives machines the ability understand and deduce meaning from human languages. NLP, in short, is the ability for computers to understand what we are saying. NLG refers to their ability communicate with us in our native language.
Three types of cues are present in every sentence we write or speak.
- StructuralSyntax: Linguistics and the rules for each language.
- Contextual: The message we want to send.
- Emotional:tone, mood.
These cues are intuitively understood by humans and they can be interpreted as such. Each spoken and written sentence is unstructured data, which must be converted into structured data for the computer to understand what we are saying. This is NLP for one particular language.
NLP transformed John’s spoken sentences into structured data that Alexa can understand. NLG used that data to trigger the responses. It added the song to the playlist and changed the playback frequency algorithm. Finally, NLG converted the structured data back into language using the spoken response.
NLP: How it works
Three core tasks are performed by natural language processing:
Computers must convert spoken and written sentences into structured data (binary codes) in order to recognize them.
These rules include:
- Parsing and tokenization
- Stemming and lemmatization
- Part-of-speech tagging;
- Language detection;
- Identification of semantic relations
These rules allow computers to separate sentences of speech into individual words. They also help recognize the language, relationship between words, syntax, semantic rules, and other details.
Unstructured data, such as speech and written text, can be converted into structured data by using rules. This is binary code (series 0s and ones). These rules can be viewed as NLP-based speech recognition.
The output of the recognition stage is a binary code. To establish relationships and meanings, the understanding stage uses algorithms that run statistical analysis on binary codes.
Here are some of the methods used to accomplish this:
- Content categorization – Create a summary document based on linguistics.
- Topic Discovery and Modeling: Capture Meaning and Themes in Text Collections.
- Contextual extract: Extract structured information from text-based resources.
- Sentiment analysis Identify mood and opinions of text or speech.
- Text-to-speech and speech-to-text conversion
- Summarization: Create a summary of large text blocks.
Because machines are built on code, each process must be written as code before it can understand text and speech.
Next, we will analyze the data for recognition and understanding. The next step is to generate responses via speech or text.
These answers are NLG-based. These responses convert structured data and code to a language. This involves programming the computer to run a series of what-if situations and codifying the syntax and linguistics rules.
NLP is not perfect. It lacks an intellectual understanding and predictive math.
NLP vs AI vs Machine Learning
Although NLP, AI and Machine Learning are all interconnected, each one has its own connotation.
NLP and Machine Learning are two subsets within Artificial Intelligence. Artificial intelligence refers to intelligent machines capable of simulated human intelligence.
NLP and Machine Learning are just two examples of AI applications. Let’s take a closer look at the differences between these terms to better understand them.
Artificial Intelligence allows machines and humans to do tasks that were previously impossible. Computers can now perform tasks such as problem-solving, planning, and understanding languages.
AI is based on algorithms that are based on probabilities and rules. These algorithms enable the machine to learn through experience and use this learning to make precise decisions in similar situations.
AI’s strongest feature is its ability to analyze and process large amounts of data in a matter of milliseconds. AI has many real-world applications today. These include digital assistants such as Siri, customer service using chatbots and manufacturing tools.
Machine Learning is an AI application that allows machines to learn just like humans. This is the AI component that allows systems to learn from data input and experience. Machine learning is based on how the learning process works. There are three types:
- Supervised learning (with input from humans);
- Unsupervised learning
- Reforcement of learning.
Learning begins with observation, example, inputs, and practice. Algorithms make use of statistical analysis to find patterns in data. These patterns are used to drive decisions. Machine Learning is concerned about pattern recognition and accuracy in decisions
It is the goal to build a machine-learning model that can be sustained. While the classic machine algorithms dealt with text as a series of keywords, algorithms today use semantic analysis to mimic human intelligence by understanding the meanings of text.
Machine learning is used in many areas, including image and speech recognition and self-driving cars. It also predicts traffic and provides product recommendations for e-commerce.
Natural Language Processing
NLP is another example of AI. Computers and humans communicate in a different way. Humans use spoken and written language, while computers use binary codes. NLP bridges the gap between numbers and words.
Here’s an example showing NLP at work.
This example shows how a user can communicate with Alexa using spoken language. Alexa then uses speech recognition to translate sounds into words. The words are then fed into a cloud-based service using NLP to convert the words into calculateable values. Alexa generates a numerical answer, and then uses NLP for the conversion of the numbers into words.
Alexa has machine learning technology that allows it to answer any question. Alexa can now answer the question faster if another user asks it.
NLP development is dependent on machine learning and artificial intelligence. Machine learning allows systems to learn natural language while artificial intelligence assists machines in understanding natural language. AI and ML combine to create intelligent systems that not only understand natural language but also learn new languages.
NLP and machine Intelligence are two components of artificial intelligent that deal with different aspects. NLP and machine languages work together to create intelligent system.
NLP – The evolutions and Google’s movement
Natural language processing is the birthplace of Alan Turing. He described in his 1950 paper Computing Machinery and Intelligence a test to see if an intelligent machine could understand and respond naturally to human conversation.
NLP’s evolution has been influenced by the evolution of its algorithms. NLP’s capabilities grew as the algorithms became more sophisticated and smarter. This graphic shows the evolution of algorithms:
The first NLP model was bag-of-words. It was a method of counting the word frequency within a document. The model was not suitable for real-world applications, where analysis had to be performed on millions of documents.
Another problem was the frequent use of common words such as “is”, “a,” or “the.” This led to TF-IDF. Common words were then designated “stop words” and removed from the count.
The co-occurrence matrix was first to deal with the semantic relationship between words. To track the sentiment and context, the algorithm used word embedding. The problem with the matrix was the amount of memory and processing power needed to store and run it.
Word2Vec was an algorithm that relied on neural networks. It utilized current techniques such as Skip Gram and Fast text. To generate text representations, the model relies on character-level information.
To enhance NLP capabilities, transformer models use decoders and encoders to convert text and speech into binary code and back to speech.
ELMo dealt with the problem of homonyms in speech and text.
These are just a few examples.
- “I love to play baseball.”
- “I’m going to see a Julius Cesar perform tonight.”
In the sentences above, “play” is used in two different contexts. You will need to understand the context by evaluating the word “play” with the rest.
Google’s contribution in NLP: BERT
Google’s contribution in the evolution of NLP was BERT, its neural network-based algorithm to natural language processing. BERT stands for Bidirectional Emcoder Representations From Transformers.
BERT is an open-source code that allows anyone the ability to create their own question answering systems. It uses transformers to evaluate the relationship between a word and all other words in a sentence.
Google’s Search feature uses BERT to analyze the context of each query and deliver the most relevant results. BERT will allow NLP to move to the next level by enabling complex models to be developed that exceed the limitations of traditional hardware.
The Impact of NLP in Content Creation and Marketing
Salesforce reports that over half of digital marketers use NLP to create content and market. These areas are seeing NLP make a positive impact on content creation and marketing.
- Using predictive intelligence to deliver a unique customer experience;
- Content creation and curating
- Data-driven marketing strategies.
NLP applications are being increasingly used by digital marketers to help drive customers through their marketing funnel.
1. NLP and user experience
Predictive intelligence gives structure to the raw data that businesses generate. It can also help in lead scoring and identifying customers who are ready to convert. Targeting customers with relevant content is possible once you have identified their position in the buying process.
Predictive analytics allows you to choose the content that will best serve the customer at each stage of the marketing funnel. Targeted content can maximize the user experience.
2. Content creation and curating
Content marketing involves daily curation. It takes a lot of resources to create engaging content that customers will find useful at various stages of the marketing funnel.
It can be time-consuming to identify trending topics or research keywords. NLP allows content marketers the ability to create content that is relevant to their audiences at different stages in their buying journey. This increases engagement and converts rates.
3. Data-driven intelligent strategies
Traditional content marketers relied on manual data sorting to build their content strategies. The risk of signal loss in high volumes of data is a concern when manually sorting them. NLP is a better way to sort through online data and create data-driven content.
NLP systems evaluate manually generated content to determine its potential performance. NLP systems can compare content from different websites to provide suggestions about areas such as title, headings and keywords. NLP tools enable you to create more intelligent and impactful content.
Using NLP for more intelligent content
Natural language processing refers to the ability of computers to understand and read written and spoken speech. Artificial intelligence can be applied in NLP, NLG and machine learning.
NLP can be used in many real-world applications, including digital assistants, chatbots, content creation, and curating. As the algorithms get more complex and smarter, NLP’s power is growing.
NLP is revolutionizing content creation and marketing. It improves user experience and creates relevant and engaging content for every stage of the buyer’s journey.
Scoop.it Blog: How Natural Language Processing Will Impact Content Creation
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