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What is Natural Language Processing?

Posted by Sai Khung Noung on October 25, 2022
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For example, the tag “Noun” would be assigned to nouns and adjectives (e.g., “red”); “Adverb” would be applied to adverbs or other modifiers. Many data annotation tools have an automation feature that uses AI to pre-label a dataset; this is a remarkable development that will save you time and money. An NLP-centric workforce builds workflows that leverage the best of humans combined with automation and AI to give you the “superpowers” you need to bring products and services to market fast. The meaning of words and phrases may change depending on context and audience. Older generations might think flex means to bend or be pliable, whereas younger generations use flex to mean to show off. Also consider homonyms, words pronounced and spelled the same but carry different meanings in different contexts.

Heard on the Street – 2/21/2023 – insideBIGDATA

Heard on the Street – 2/21/2023.

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NLP is a fast-paced and rapidly changing field, so it is important for individuals working in NLP to stay up-to-date with the latest developments and advancements. Much like programming languages, there are way too many resources to start learning NLP. Choose a Python NLP library — NLTK or spaCy — and start with their corresponding resources. NLTK is a Python library that allows many classic tasks of NLP and that makes available a large amount of necessary resources, such as corpus, grammars, ontologies, etc. It can be used in real cases but it is mainly used for didactic or research purposes.

Introducing CloudFactory’s NLP-centric workforce

Semantic tasks analyze the structure of sentences, word interactions, and related concepts, in an attempt to discover the meaning of words, as well as understand the topic of a text. Natural language processing tools can help machines learn to sort and route information with little to no human interaction – quickly, efficiently, accurately, and around the clock. In this guide, you’ll learn about the basics of Natural Language Processing and some of its challenges, and discover the most popular NLP applications in business. Finally, you’ll see for yourself just how easy it is to get started with code-free natural language processing tools. The NLTK includes libraries for many of the NLP tasks listed above, plus libraries for subtasks, such as sentence parsing, word segmentation, stemming and lemmatization , and tokenization .

nlp algo can be used to interpret free, unstructured text and make it analyzable. There is a tremendous amount of information stored in free text files, such as patients’ medical records. Before deep learning-based NLP models, this information was inaccessible to computer-assisted analysis and could not be analyzed in any systematic way. With NLP analysts can sift through massive amounts of free text to find relevant information. Natural language processing is the ability of a computer program to understand human language as it is spoken and written — referred to as natural language. Low-level text functions are the initial processes through which you run any text input.

Technologies related to Natural Language Processing

Although there are doubts, natural language processing is making significant strides in the medical imaging field. Learn how radiologists are using AI and NLP in their practice to review their work and compare cases. Natural language processing is also challenged by the fact that language — and the way people use it — is continually changing. Although there are rules to language, none are written in stone, and they are subject to change over time. Hard computational rules that work now may become obsolete as the characteristics of real-world language change over time.

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As a business, there’s a lot you can learn about how your customers feel by what they post/comment about and listen to. Stemming “trims” words, so word stems may not always be semantically correct. For example, stemming the words “change”, “changing”, “changes”, and “changer” would result in the root form “chang”. Even beyond what we are conveying explicitly, our tone, the selection of words add layers of meaning to the communication.

Up next: Natural language processing, data labeling for NLP, and NLP workforce options

Consider Liberty Mutual’s Solaria Labs, an innovation hub that builds and tests experimental new products. Solaria’s mandate is to explore how emerging technologies like NLP can transform the business and lead to a better, safer future. Syntax analysis is analyzing strings of symbols in text, conforming to the rules of formal grammar.


This blog post discussed various NLP techniques and tasks that explain how technology approaches language understanding and generation. NLP has many applications that we use every day without realizing- from customer service chatbots to intelligent email marketing campaigns and is an opportunity for almost any industry. Natural language processing extracts relevant pieces of data from natural text or speech using a wide range of techniques. One of these is text classification, in which parts of speech are tagged and labeled according to factors like topic, intent, and sentiment. Another technique is text extraction, also known as keyword extraction, which involves flagging specific pieces of data present in existing content, such as named entities. More advanced NLP methods include machine translation, topic modeling, and natural language generation.

Techniques and methods of natural language processing

The possibility that a specific document refers to a particular term; this is dependent on how many words from that document belong to the current term. Abstractive text summarization has been widely studied for many years because of its superior performance compared to extractive summarization. However, extractive text summarization is much more straightforward than abstractive summarization because extractions do not require the generation of new text. Text summarization is a text processing task, which has been widely studied in the past few decades. Designed specifically for telecom companies, the tool comes with prepackaged data sets and capabilities to enable quick …

  • This is the process by which a computer translates text from one language, such as English, to another language, such as French, without human intervention.
  • But data labeling for machine learning is tedious, time-consuming work.
  • These libraries are free, flexible, and allow you to build a complete and customized NLP solution.
  • Essentially, the job is to break a text into smaller bits while tossing away certain characters, such as punctuation.
  • Completely integrated with machine learning algorithms, natural language processing creates automated systems that learn to perform intricate tasks by themselves – and achieve higher success rates through experience.
  • Text classification has many applications, from spam filtering (e.g., spam, not spam) to the analysis of electronic health records .

This is a common Machine learning method and used widely in the NLP field. Edward Krueger is the proprietor of Peak Values Consulting, specializing in data science and scientific applications. Edward also teaches in the Economics Department at The University of Texas at Austin as an Adjunct Assistant Professor. He has experience in data science and scientific programming life cycles from conceptualization to productization. Edward has developed and deployed numerous simulations, optimization, and machine learning models.

Top NLP Algorithms & Concepts

Online, chatbots key in on customer preferences and make product recommendations to increase basket size. The healthcare industry also uses NLP to support patients via teletriage services. In practices equipped with teletriage, patients enter symptoms into an app and get guidance on whether they should seek help. NLP applications have also shown promise for detecting errors and improving accuracy in the transcription of dictated patient visit notes.

  • Today, DataRobot is the AI leader, with a vision to deliver a unified platform for all users, all data types, and all environments to accelerate delivery of AI to production for every organization.
  • Machine translation is used to translate one language in text or speech to another language.
  • Even humans struggle to analyze and classify human language correctly.
  • Customer service chatbots are one of the fastest-growing use cases of NLP technology.
  • As computer systems cannot explicitly understand grammar, they require a specific program to dismantle a sentence, then reassemble using another language in a manner that makes sense to humans.
  • This leads to a reduction in the total number of staff needed and allows employees to focus on more complex tasks or personal development.

A specific implementation is called a hash, hashing function, or hash function. Automate business processes and save hours of manual data processing. However, building a whole infrastructure from scratch requires years of data science and programming experience or you may have to hire whole teams of engineers. It involves filtering out high-frequency words that add little or no semantic value to a sentence, for example, which, to, at, for, is, etc. Even though stemmers can lead to less-accurate results, they are easier to build and perform faster than lemmatizers. With a large amount of one-round interaction data obtained from a microblogging program, the NRM is educated.


NLP/ ML systems leverage social media comments, customer reviews on brands and products, to deliver meaningful customer experience data. Retailers use such data to enhance their perceived weaknesses and strengthen their brands. Natural language processing and machine learning systems have only commenced their commercialization journey within industries and business operations. The following examples are just a few of the most common – and current – commercial applications of NLP/ ML in some of the largest industries globally.

How does natural language processing work?

Natural language processing algorithms allow machines to understand natural language in either spoken or written form, such as a voice search query or chatbot inquiry. An NLP model requires processed data for training to better understand things like grammatical structure and identify the meaning and context of words and phrases. Given the characteristics of natural language and its many nuances, NLP is a complex process, often requiring the need for natural language processing with Python and other high-level programming languages.

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