A Guide to Natural Processing Language (With Tasks and Uses)

By Indeed Editorial Team

Published June 10, 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Machine learning and artificial intelligence can help streamline a variety of daily functions. Natural language processing is a type of computer science that strives to enhance the ability of machines to understand and respond to voice and text data. Understanding how to use this process effectively can help you boost efficiency and decrease time spent on specific tasks that natural language processing can easily automate. In this article, we define natural processing language, explain how it works, describe its common tasks, explain its uses, and discuss approaches and examples for using this technology to your advantage.

What is natural processing language?

Natural processing language, or natural language processing (NLP), refers to the branch of computer science involved in the manipulation of speech or text by a software program. NLP combines computational linguistics with machine learning to process natural language and understand its full meaning. Here are some examples of how a software program or application may translate a message and turn it into code to provide the user with technical assistance:

  • using voice-to-text on your smartphone to send a message

  • commanding voice operating systems to turn lights on or off, set alarms, or activate electronics

  • asking the voice assistant on your smartphone to call someone for you

  • searching for information on search engines

  • interacting with customer service chatbots on product or service websites

  • using an online language translation tool to change the language on a website or section of text

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Natural language processing explained

Using text vectorizations, or the transformation of text into meaningful numbers, NLP translates spoken words or text into a language a computer understands. Developers feed training datasets to machine learning algorithms and expected outputs to train machines to associate the input with the corresponding output. This allows machines to make predictions or perform a desired task. The machines then use either supervised or unsupervised learning methods to build their language understanding to make output predictions for new inputs. In unsupervised learning, the machines find patterns and infer conclusions, whereas supervised learning requires the developer's input for training.

Machine learning models are beneficial since the machines can learn on their own as long as developers submit relevant training data. A machine's knowledge base advances with each data set the developers input into the algorithms. For example, developers can train machines to classify text by sentiment, topics, or intent without defining rules manually. To test a machine's deep learning, developers may feed testing datasets to determine if they require entering additional training sets.

What are natural language processing tasks?

There are many ways you can use or reference NLP in daily life. Here are some common NLP tasks you may encounter:

Content categorization

Content categorization involves the linguistic analysis of text to determine topics, detect duplication, or search for relevant materials. For example, if you ask your smartphone assistant a question, it can use AI capabilities to process your language into its code to find search matches or specific answers. This categorization may include analyzing search results, indexing information, or highlighting similar content.

Discovery and modelling

Discovery and modelling tasks focus on understanding the meaning of a text or verbal communication. NLP uses machine learning to recognize themes and analytics with the text to help optimize results. For example, if you asked your smartphone what spices are in Italian seasoning, it might use analytics with your verbal text to find the answer. Depending on the system's abilities, it might offer a link for you to find the answer or it might respond with a complete verbal answer.

Contextual extraction

This task focuses on dividing a search to extract the noun phrases and themes, offering relevant information relating to the context. For example, if your smartphone assistant can't find an answer to your question, it can provide articles or similar searches related to the initial query. The process uses contextual extraction to look for items with similar themes, offering related articles or links. More complex NLP systems can identify related themes and ideas that link texts together, even if the original doesn't include the exact phrase.

Sentiment analysis

Sentiment analysis focuses on identifying the tone or biased opinions within text information. When performing this task, machines attempt to identify subjective qualities from the text. The application examines the message for any subjective information so that it can classify it based on past findings. For example, a machine might recognize the phrase, "The service was great!" as having a positive sentiment, classifying it as a good business review. Advanced NLP technologies can even identify sarcasm, although some nuanced phrases can be challenging for machines to parse.

Conversation transformations

Conversation transformation is the transformation of one communication format to another. For example, you might use voice-to-text capabilities to send a message on your phone. NLP systems transform your verbal communication into written communication or written into verbal. Many GPS systems use conversation transformation AI to communicate directions as verbal instructions to help drivers maintain their focus on the road.

Summarization of documents

NLP systems can generate comprehensive summaries of large amounts of information. This might allow you to condense larger texts for more rapid consumption, while still maintaining their original meaning. This function can help users save time while learning, as they are only consuming the most important information. Since manual summarization requires significant time and resources, this is becoming an increasingly popular function of NLP, specifically in education.

Machine translation

Machine translation is a system's ability to convert natural languages from one form to another, while still maintaining the text's meaning. Here are the translation types in NLP:

  • Statistical machine translation: This translation type focuses on the relationship between words used in original sources and similar words in other languages. Systems typically use this type to help with the basic translations of text between languages.

  • Rule-based machine translation: This NLP translation type relies on grammatical rules of the source language to help with translations. These systems often rely on dictionaries as references until they have increased their knowledge base.

  • Hybrid machine translation: This type uses both translation memory and human assistance to perform translations. This hybrid model can help boost translation quality.

  • Neural machine translation: This advanced translation type uses neural models to help build complex statistical translation models. The primary function of this type is to analyze sources and text on deeper levels.

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When do you use natural language processing?

Here are the purposes for which you might use natural language processing:

Working with large amounts of text-based data

NLP can help technology systems communicate with humans in their native languages to complete tasks. These AI systems allow devices to read text, listen to and understand verbal speech, measure sentiments, and extract summaries from a significant amount of data and information. Consumers use these systems daily when they prompt devices to search for specific information from the entire database of the internet. Individuals usually don't notice they're using NLP systems since automation is easily accessible and most smart devices have virtual assistance features.

Structuring data sources

When analyzing sources, NLP systems require an understanding of many languages and grammatical styles to comprehend the information. For example, sometimes people include abbreviations or slang terms in writing or have accents in verbal speech. NLP technology works to recognize and learn these elements to enhance its search information to help it structure the data sources it finds in intelligible ways.

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Approaches and examples of using natural language processing

Here are some examples of the different approaches and methods NLP can use to reach goals:

Text analysis

NLP can scan text to understand its relevancy or value, making it a valuable tool for analyzing textual information. After scanning, it can interpret the text, compare it to other findings, measure sentiment, and determine its importance. Here are some examples of how this process can help with text analysis:

  • Investigation: NLP technology can recognize patterns or clues in digital communications, such as e-mails or texts, or documented written communications to help resolve crimes.

  • Expertise: NLP helps organize content into purposeful groups, such as subject or individual, to help you identify important trends to save time.

  • Social media: Brands can use NLP to track sentiment and popularity for specific topics and can identify digital influencers.

Everyday functions

You might use NLP technology daily without even knowing that it's analyzing information. Developers design these systems to increase the user's efficiency of regular daily tasks, which can help save time and improve communication. Here are some examples of how this process helps with various functions outside of digital assistants:

  • Spam filtering: E-mail providers use NLP to identify patterns between actual e-mails and spam to separate junk mail from your primary inbox.

  • Predictive text: Some e-mail or text systems review past answers to offer prompts for quick responses based on the content of the message received.

  • Voicemail transcripts: Phone providers may offer a service to provide transcripts of voicemails with NLP technology that documents verbal speech.

  • Online searching: When you type in a query to an online search engine, NLP systems help populate the suggested searches offered to provide you with related information.


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