NATURAL LANGUAGE PROCESSING

What is natural language processing?

Natural language processing (NLP) is the ability of a computer program to understand human language as it's spoken and written -- referred to as natural language. It's a component of artificial intelligence (AI).

NLP has existed for more than 50 years and has roots in the field of linguistics. It has a variety of real-world applications in numerous fields, including medical research, search engines and business intelligence.

NLP uses either rule-based or machine learning approaches to understand the structure and meaning of text. It plays a role in chatbots, voice assistants, text-based scanning programs, translation applications and enterprise software that aids in business operations, increases productivity and simplifies different processes.

How does natural language processing work?

NLP uses many different techniques to enable computers to understand natural language as humans do. Whether the language is spoken or written, natural language processing can use AI to take real-world input, process it and make sense of it in a way a computer can understand. Just as humans have different sensors -- such as ears to hear and eyes to see -- computers have programs to read and microphones to collect audio. And just as humans have a brain to process that input, computers have a program to process their respective inputs. At some point in processing, the input is converted to code that the computer can understand. There are two main phases to natural language processing: data preprocessing and algorithm development.

Data preprocessing involves preparing and cleaning text data so that machines can analyze it. Preprocessing puts data in a workable form and highlights features in the text that an algorithm can work with. There are several ways this can be done, including the following:

• Tokenization. Tokenization substitutes sensitive information with nonsensitive information, or a token. Tokenization is often used in payment transactions to protect credit card data.
• Stop word removal. Common words are removed from the text, so unique words that offer the most information about the text remain.
• Lemmatization and stemming. Lemmatization groups together different inflected versions of the same word. For example, the word "walking" would be reduced to its root form, or stem, "walk" to process.
• Part-of-speech tagging. Words are tagged based on which part of speech they correspond to -- such as nouns, verbs or adjectives.

Once the data has been preprocessed, an algorithm is developed to process it. There are many different natural language processing algorithms, but the following two main types are commonly used:

• Rule-based system. This system uses carefully designed linguistic rules. This approach was used early in the development of natural language processing and is still used.
• Machine learning-based system. Machine learning algorithms use statistical methods. They learn to perform tasks based on training data they're fed and adjust their methods as more data is processed. Using a combination of machine learning, deep learning and neural networks, natural language processing algorithms hone their own rules through repeated processing and learning.

Why is natural language processing important?

Businesses use large amounts of unstructured, text-heavy data and need a way to efficiently process it. Much of the information created online and stored in databases is natural human language, and until recently, businesses couldn't effectively analyze this data. This is where natural language processing is useful.

The advantages of natural language processing can be seen when considering the following two statements: "Cloud computing insurance should be part of every service-level agreement" and "A good SLA ensures an easier night's sleep -- even in the cloud." If a user relies on natural language processing for search, the program will recognize that cloud computing is an entity, that cloud is an abbreviated form of cloud computing, and that SLA is an industry acronym for service-level agreement.

These are the types of vague elements that frequently appear in human language and that machine learning algorithms have historically been bad at interpreting. Now, with improvements in deep learning and machine learning methods, algorithms can effectively interpret them. These improvements expand the breadth and depth of data that can be analyzed.

Likewise, NLP is useful for the same reasons as when a person interacts with a generative AI chatbot or AI voice assistant. Instead of needing to use specific predefined language, a user could interact with a voice assistant like Siri on their phone using their regular diction, and their voice assistant will still be able to understand them.

Benefits of natural language processing

The main benefit of NLP is that it improves the way humans and computers communicate with each other. The most direct way to manipulate a computer is through code -- the computer's language. Enabling computers to understand human language makes interacting with computers much more intuitive for humans.

Other benefits include the following:

• Offers improved accuracy and efficiency of documentation.
• Enables an organization to use chatbots for customer support.
• Provides an organization with the ability to automatically make a readable summary of a larger, more complex original text.
• Lets organizations analyze structured and unstructured data.
• Enables personal assistants such as Alexa to understand the spoken word.
• Makes it easier for organizations to perform sentiment analysis.
• Organizations can use NLP to better understand lead generation, social media posts, surveys and reviews.
• Provides advanced insights from analytics that were previously unreachable due to data volume.