Complete Guide to Natural Language Processing NLP with Practical Examples

This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals. IBM has launched a new open-source toolkit, PrimeQA, to spur progress in multilingual question-answering systems to make it easier for anyone to quickly find information on the web. The Python programing language provides a wide range of tools and libraries for attacking specific NLP tasks. Many of these are found in the Natural Language Toolkit, or NLTK, an open source collection of libraries, programs, and education resources for building NLP programs. Companies nowadays have to process a lot of data and unstructured text.

examples of nlp

As you can see, Gmail predicted the word “works” automatically. Such features are the result of NLP algorithms working in the background. If this hasn’t happened, go ahead and search for something on Google, but only misspell one word in your search. From the above output , you can see that for your input review, the model has assigned label 1. You should note that the training data you provide to ClassificationModel should contain the text in first coumn and the label in next column. You can classify texts into different groups based on their similarity of context.

Statistical approach

This technology even extends to languages like Russian and Chinese, which are traditionally more difficult to translate due to their different alphabet structure and use of characters instead of letters. Even the business sector is realizing the benefits of this technology, with 35% of companies using NLP for email or text classification purposes. Additionally, strong email filtering in the workplace can significantly reduce the risk of someone clicking and opening a malicious email, thereby limiting the exposure of sensitive data.

examples of nlp

Normalization is the process of converting a token into its base form. In the normalization process, the inflection from a word is removed so that the base form can be obtained. Tokenization is a process of splitting a text object into smaller units which are also called tokens. Examples of tokens can be words, numbers, engrams, or even symbols.

Smart Assistants

A widespread example of speech recognition is the smartphone’s voice search integration. This feature allows a user to speak directly into the search engine, and it will convert the sound into text, before conducting a search. To better understand the applications of this technology for businesses, let’s look at an NLP example. NPL cross-checks text to a list of words in the dictionary (used as a training set) and then identifies any spelling errors. Then, the user has the option to correct the word automatically, or manually through spell check.

examples of nlp

Most higher-level NLP applications involve aspects that emulate intelligent behaviour and apparent comprehension of natural language. More broadly speaking, the technical operationalization of increasingly advanced aspects of cognitive behaviour represents one of the developmental trajectories of NLP (see trends among CoNLL shared tasks above). Neural machine translation, based on then-newly-invented sequence-to-sequence transformations, made obsolete the intermediate steps, such as word alignment, previously necessary for statistical machine translation. The earliest decision trees, producing systems of hard if–then rules, were still very similar to the old rule-based approaches.

Language translation

One of the fundamentals that have driven technological advancement to the stage where it is today, Natural Language Processing or NLP has made human intelligence understandable. That’s a lot to tackle at once, but by understanding each process and combing through the linked tutorials, you should be well on your way to a smooth and successful NLP application. That might seem like saying the same thing twice, but both sorting processes can lend different valuable data. Discover how to make the best of both techniques in our guide to Text Cleaning for NLP. You can mold your software to search for the keywords relevant to your needs – try it out with our sample keyword extractor.

ChatGPT is a chatbot powered by AI and natural language processing that produces unusually human-like responses. Recently, it has dominated headlines due to its ability to produce responses that far outperform what was previously commercially possible. Although natural language processing might sound like something out of a science fiction novel, the truth is that people already interact with countless NLP-powered devices and services every day. NLP is special in that it has the capability to make sense of these reams of unstructured information. Tools like keyword extractors, sentiment analysis, and intent classifiers, to name a few, are particularly useful.

Analytics Vidhya App for the Latest blog/Article

It has advanced to such a level that machines everywhere are now using this technology to analyse data and carry out other functions as well. With humongous quantities of unstructured and unorganized data, NLP has helped big businesses to filter data and organize it well. This use case involves extracting information from unstructured data, such as text and images. NLP can be used to identify the most relevant parts of those documents and present them in an organized manner. The use of NLP has become more prevalent in recent years as technology has advanced.

  • We also have Gmail’s Smart Compose which finishes your sentences for you as you type.
  • NLP is used in consumer sentiment research to help companies improve their products and services or create new ones so that their customers are as happy as possible.
  • If users are unable to do something, the goal is to help them do it.
  • Through a set of machine learning algorithms, or deep learning algorithms and systems, NLP had eventually made data analysis possible without humans.
  • Chatbots and virtual assistants are used for automatic question answering, designed to understand natural language and deliver an appropriate response through natural language generation.
  • With NLP permeating so many different parts of our technological lives, it’s likely to be considered an integral part of any IT job.
  • The final key to the text analysis puzzle, keyword extraction, is a broader form of the techniques we have already covered.

NLP is not perfect, largely due to the ambiguity of human language. However, it has come a long way, and without it many things, such as large-scale efficient analysis, wouldn’t be possible. NLP tutorial is designed for both beginners and professionals. With the volume of unstructured data being produced, it is only efficient to master this skill or at least understand it to a level so that you as a data scientist can make some sense of it. A different type of grammar is Dependency Grammar which states that words of a sentence are dependent upon other words of the sentence. For example, in the previous sentence “barking dog” was mentioned and the dog was modified by barking as the dependency adjective modifier exists between the two.

SpaCy Text Classification – How to Train Text Classification Model in spaCy (Solved Example)?

Then, using text-to-speech translations with natural language generation (NLG) algorithms, they reply with the most relevant information. With the help of NLP, computers can easily understand human language, analyze content, and make examples of nlp summaries of your data without losing the primary meaning of the longer version. Machine translation is exactly what it sounds like—the ability to translate text from one language to another—in a program such as Google Translate.

examples of nlp

Anyone who has ever misread the tone of a text or email knows how challenging it can be to translate sarcasm, irony, or other nuances of communication that are easily picked up on in face-to-face conversation. These are the most common natural language processing examples that you are likely to encounter in your day to day and the most useful for your customer service teams. We all hear “this call may be recorded for training purposes,” but rarely do we wonder what that entails.

What is NLP?

Search autocomplete is a good example of NLP at work in a search engine. This function predicts what you might be searching for, so you can simply click on it and save yourself the hassle of typing it out.

2 What is Regular Expression Tokenization?

As we already established, when performing frequency analysis, stop words need to be removed. Let’s say you have text data on a product Alexa, and you wish to analyze it. The words of a text document/file separated by spaces and punctuation are called as tokens. In this article, you will learn from the basic (and advanced) concepts of NLP to implement state of the art problems like Text Summarization, Classification, etc.


You get to magically channel their characteristics and understand how they think and behave,” she explains. Rapport building techniques can help you connect with people more naturally. They’re mainly based on the importance of synchronizing your communication style with the other person’s in order to develop trust.

Bir cevap yazın

E-posta hesabınız yayımlanmayacak. Gerekli alanlar * ile işaretlenmişlerdir