We may not be aware of the extent to which artificial intelligence has impacted our lives. Every time we ask our smartphones for directions, talk to chatbots or use speech-to-text services we’re dealing with AI.

Language AI, or natural language processing (NLP), is a branch of artificial intelligence (AI) that focuses on enabling computers to understand, interpret, and generate human language. From understanding and responding to voice commands to analyzing and summarizing large amounts of text, NLP has the potential to transform the way we interact with technology and process information. 

In this article, we'll explore the basics of NLP, clarify the terms: NLP, AI, and ML, and check relevant applications, and the potential of AI to shape the future of human-machine interaction. So, whether you're a tech enthusiast, a business professional, or just curious about the latest developments in AI, read on to learn more about the exciting world of language AI.

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AI, NLP, and ML: difference explained

The term AI (artificial intelligence), Language AI, NLP (natural language processing), and ML (machine learning)  are usually synonymously used and inseparably bound together. No wonder you can easily get lost in these terms.

In this section, we are going to explain each term in detail and the difference between them. 

What is AI?

Artificial intelligence or AI is a broad term used to refer to any technology that can make machines think and learn from tasks and solve problems like humans. Anything that makes a machine “smart” is referred to as artificial intelligence. It can be a robot, a “smart” refrigerator, a self-driven car, or a predictive system. 

What is Language AI/NLP?

Language AI or Natural language processing is an area of AI which makes it possible for computers to interpret human language and transform it into a machine-readable format. NLP enables machines to analyze and interpret large amounts of plain written text by extracting metadata from it. This advancement in technology gave an impetus for application development such as sentiment analyzers, topic classifiers, keyword extraction, and so on.  

What is ML?

Machine learning is also a subset of AI. In machine learning, computers learn from data and improve their performance on specific tasks without being explicitly programmed. Machine learning models and algorithms are used in natural language processing and in a variety of applications, including image recognition, spam filtering, and recommender systems.

In the picture below it’s seen how AI, NLP, and ML are interrelated. While NLP makes it possible for machines to understand written and spoken text, machine learning automates the process and makes predictions based on patterns.

Thus, both NLP and Machine Learning are essential components of successful text analysis. For example in chatbots, NLP works as a translator from human to machine language, and ML helps them become smarter and learn from the inputs they experience. 

How NLP works

The Natural Language processing pipeline involves a set of steps to break down human language into machine-readable chunks.

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Step 1: Sentence segmentation

It divides the entire paragraph into different sentences for better understanding. 

Example: "London is the capital and most populous city of England and the United Kingdom. Standing on the River Thames in the southeast of the island of Great Britain, London has been a major settlement for two millennia. It was founded by the Romans, who named it Londinium."

Source: Wikipedia

  After using sentence segmentation, we get the following result:

1. “London is the capital and most populous city of England and the United Kingdom.”
2. “Standing on the River Thames in the southeast of the island of Great Britain, London has been a major settlement for two millennia.”
3. “It was founded by the Romans, who named it Londinium.”

Step 2: Word tokenization

Word tokenization breaks a text into separate sentences or words called tokens. This makes text easier to understand.

Example: I am very satisfied with the service. = “I am” “very” “satisfied” “with” “the service”

Step 3: Lemmatization & Stemming

This step helps in preprocessing text. Usually, in speaking or writing, we use different grammatical forms of words. To help computers understand those words easier they need to be transformed back to their root form. 

Example: I ordered two chairs. = “I” “order” “2” “chair”

Step 4: Stop-word removal

Stop-word removal involves filtering out frequently occurring words that add little or no semantic value to a sentence, for example, I, they, which, to, at, for, is, etc.

Example: Hello, I’m having trouble logging into my account. = “trouble” “logging” “account”

Step 5: Dependency parsing

A dependency parser analyzes how words are related in a sentence and builds a tree where a single word becomes a parent word. The main verb will act as a root node.

Step 6: Part-of-speech (POS) tagging

POS tagging means identifying each token’s part of speech: verb, adjective, noun, pronoun, conjunction, preposition, intersection, etc. After all parts of speech are correctly identified, it’s possible to recognize entities, extract themes, and process sentiment. 

Applications of Natural Language Processing

There is no limit to the applications of NLP in any area of the business where natural language is involved. Here is a list of the most popular NLP tools that help companies provide the best customer services, organize their businesses, and automate repetitive and time-consuming tasks to increase efficiency. 

Text classification

It’s a core NLP task that assigns a set of pre-defined tags to categorize text. Companies can instantly manage huge amounts of data using the classification algorithm by allocating it according to these tags and categories. 

It can be used to automatically sort incoming customer feedback and automate processes in customer service, detect spam, resume evaluation, etc. 

Text extraction

The term text extraction is often interchangeable with the term keyword extraction. It uses ML to scan text and automatically extract relevant keywords and phrases (product specification, colors, codes, etc) and entities (like emails, names, addresses, places, etc) from unstructured data.

It can be used to automatically find positive or negative reviews, by finding the words “useful application” or “worst application” or pulling out all mentioned places from a travel blog.  

Sentiment analysis

Sentiment analysis is the process of identifying emotions and feelings and classifying them as positive, negative, or neutral. 

It’s impossible to find a business that is not interested in customers’ opinions. Extracting and classifying emotions help companies in all areas, like call centers, banking, healthcare, insurance, market research, etc. Moreover, this tool is widely used within companies to detect employees’ job satisfaction and create more effective workflows. 

Automatic summarization

The task of automatic summarization is a process of shortening text and creating a new version with the most relevant information. 

Every time there’s a task to study a huge amount of information from text, automatic summarization copes with it effectively and saves you time from manually sorting through the tons of content. Create meeting notes from a virtual conference, get a summary on an email thread or patient’s medical history, all that is possible due to this very tool. 

Speech recognition

Automatic speech recognition and speech-to-text is a type of software that converts human speech from its analog form to digital one that can be recognized by machines.

The practical use of speech-to-text recognition is hard to overestimate when it comes to analyzing information from audio or video. All the NLP techniques described above in this article can be applied only after audio/video is converted to text.

Generative AI & language AI

Language AI can be a component of generative AI. Generative AI refers to artificial intelligence systems that generate new data or content, such as images, music, text, and videos. Language AI, specifically language generation, is a subfield of AI that focuses on generating human-like text. Thus, language AI can be considered a type of generative AI. 

Conclusion

Artificial intelligence, natural language processing, generative AI, and machine learning are all cutting-edge technologies that are revolutionizing the way we live and work. By harnessing the power of these technologies, we can automate tasks that were once thought to be impossible. 

In the context of technological advancements and the availability of artificial intelligence, natural language processing has emerged as one of the most promising and fastest-growing fields. With the use of machine learning, NLP creates systems that can independently accomplish tasks and improve their success rate as they gain experience. 

OneAI is a Text Analytics service built for developers with no required background in AI/ML/NLP. Check out our text analytic solutions, such as transcribing audio to text, summarizing, keywords and entities extraction, and many more in our Language Studio. Or request a demo and let us help you to get started. 

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