What is natural language processing?

Depiction of sensory input into and output of hard wired brain

Feb 13, 2023

Natural language processing (NLP) is the branch of artificial intelligence that enables machines and computer systems to mimic the way human beings process and understand language. It brings together aspects of computer science and linguistics, and covers both written text and the spoken word.

How does natural language processing work?

Natural language processing is made possible through:

  • computational linguistics, which analyses and synthesises speech and language through rule-based computer science techniques.
  • statistical models.
  • machine learning methods.
  • deep learning models.

These components allow machines to collect and gather language inputs, convert it into code, and then process it in order to understand things like meaning, sentiment, and intent.

There are two main stages in natural language processing. 

Data pre-processing

The data pre-processing stage is where text data is prepared for machine analysis. This can occur through:

  • Tokenisation, which involves breaking text down into smaller pieces. This segmentation makes text easier to work with.
  • Stop word removal, which removes common words from text, leaving only unique words that are more likely to offer important information about the content.
  • Lemmatisation and stemming, which reduce words to their root forms.
  • Part-of-speech tagging, which labels words as nouns, verbs, or adjectives.

Algorithm development

The algorithm stage takes the pre-processed data and processes it according to the algorithm to which it’s applied. These NLP algorithms typically fall into one of two types:

  1. Rules-based algorithms. Rules-based algorithms are constructed of linguistic rules that are developed and then applied during data processing. 
  2. Machine learning-based algorithms. Machine learning algorithms for natural language processing is a newer area of the field, and more sophisticated than rules-based methods. They use machine learning, and even deep learning and neural networks, to continually refine and adjust their rules as they process data. The more data the machine processes, the more – and the better – it understands.

How is natural language processing used in artificial intelligence (AI)?

Natural language processing is a branch of artificial intelligence, so all NLP tasks fall under the AI umbrella. However, there are a number of areas within natural language processing that are pushing the boundaries of machine intelligence, including:

  • Sentiment analysis, which considers the more subjective aspects of language, such as sarcasm, confusion, and pragmatics. 
  • Natural language generation (NLG), which produces text – in a recognisable human language – from data, structured information, or code. 
  • Natural language understanding (NLU), which focuses on a machine’s reading comprehension. It works to identify concepts and emotions, and performs tasks such as sentiment analysis, summarisation, and named entity recognition, with applications in machine translation, voice activation, and many other areas. 

What is the difference between natural language processing and artificial intelligence?

As a branch of artificial intelligence, natural language processing has an intrinsic link to AI. But while natural language processing is AI, AI is more than just natural language processing, including other important areas such as machine learning, robotics, and so on.

What are the most common types of natural language processing?

Natural language processing technology is used by many people every day. Common real-world applications include:

  • Text translation, such as Google Translate and similar apps, which translate a piece of text written in English into another language, and vice versa.
  • Digital assistants and virtual agents, such as Siri from Apple or Alexa from Amazon, which process spoken commands and respond accordingly.
  • Speech-to-text dictation, which uses speech recognition, and which many people use for sending text messages and emails.
  • Customer service chatbots, which are frequently used on e-commerce and service provider websites.
  • Predictive text, often seen when using search engines or in text messaging apps.

Examples of natural language processing

There are a number of other use cases and applications for natural language processing that many people may be unaware of. These include:

  • Spam detection. Through natural language processing, email providers can scan messages for language or phrases that typically appear in spam or phishing emails, and then move potential threats into a spam folder.
  • Social media sentiment analysis. Natural language processing technology allows social media professionals to gain greater insight into social media data. For example, they can better understand how social media users feel about their organisation or brand, and then create products, campaigns, or promotions based on this information.
  • Text summarisation. Text summarisation is perfect for creating “too long; didn’t read” (Tl;dr) content. This technology is tasked with parsing large datasets of unstructured text and then creating summaries or synopses about the content. It often uses semantic reasoning and semantic analysis technology – which can make inferences based on the data – and natural language generation.
  • Topic modeling. Topic modeling is a tool used in text mining, which allows machines to extract new, high-quality information from text. Topic models are the statistical models that help make this technology possible.

What are the advantages and disadvantages of natural language processing?

The advantages of natural language processing are numerous, and continue to grow as the technology evolves. For example, organisations spanning business and big data to healthcare and education can use natural language processing to process and analyse huge quantities of unstructured data in order to:

  • Gain objective, accurate insights through text analytics and analysis.
  • Streamline processes and workflows, which in turn can reduce costs.
  • Better understand customers and clients, patients and students.

However, that’s not to say that natural language processing is without its challenges. The technology can be costly, and may still struggle to pick up ambiguity, irony, or word sense disambiguation within text or speech. It may also struggle to create content with accurate grammar and syntax – although there have been significant improvements in this area in recent years.

Natural language processing resources

Regardless of the natural language processing project – whether it’s for business automation, data science optimisation, or beyond – there are a number of free resources available to access and use. 

  • Those using the Python programming language have access to what’s called the Natural Language Toolkit, an open source collection of libraries, programmes, and resources for natural language programming projects.
  • Awesome NLP, a GitHub repository of natural language processing resources such as tutorials, libraries, and datasets.
  • Microsoft NLP recipes, a repository curated by Microsoft to document and standardise NLP best practices.

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