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natural language processing (NLP)

By Alexander S. Gillis

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:

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:

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.

Techniques and methods of natural language processing

Syntax and semantic analysis are two main techniques used in natural language processing.

Syntax is the arrangement of words in a sentence to make grammatical sense. NLP uses syntax to assess meaning from a language based on grammatical rules. Syntax NLP techniques include the following:

Semantics involves the use of and meaning behind words. Natural language processing applies algorithms to understand the meaning and structure of sentences. Semantic techniques include the following:

Current approaches to natural language processing are based on deep learning, a type of AI that examines and uses patterns in data to improve a program's understanding. Deep learning models require massive amounts of labeled data for the natural language processing algorithm to train on and identify relevant correlations, and assembling this kind of big data set is one of the main hurdles to natural language processing.

Earlier approaches to natural language processing involved a more rule-based approach, where simpler machine learning algorithms were told what words and phrases to look for in text and given specific responses when those phrases appeared. But deep learning is a more flexible, intuitive approach in which algorithms learn to identify speakers' intent from many examples -- almost like how a child would learn human language.

Three open source tools commonly used for natural language processing include Natural Language Toolkit (NLTK), Gensim and NLP Architect by Intel. NLTK is a Python module with data sets and tutorials. Gensim is a Python library for topic modeling and document indexing. NLP Architect by Intel is a Python library for deep learning topologies and techniques.

What is natural language processing used for?

Some of the main functions and NLP tasks that natural language processing algorithms perform include the following:

The functions listed above are used in a variety of real-world applications, including the following:

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:

Challenges of natural language processing

There are numerous challenges in natural language processing, and most of them boil down to the fact that natural language is ever-evolving and somewhat ambiguous. They include the following:

The evolution of natural language processing

NLP draws from a variety of disciplines, including computer science and computational linguistics developments dating back to the mid-20th century. Its evolution included the following major milestones:

Natural language processing plays a vital part in technology and the way humans interact with it. Though it has its challenges, NLP is expected to become more accurate with more sophisticated models, more accessible and more relevant in numerous industries. NLP will continue to be an important part of both industry and everyday life.

As natural language processing is making significant strides in new fields, it's becoming more important for developers to learn how it works. Learn how to develop your skills in creating NLP programs.

15 Feb 2024

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