13 Natural Language Processing Examples to Know

It is not a general-purpose NLP library, but it handles tasks assigned to it very well. The NLTK Python framework is generally used as an education and research tool. However, it can be used to build exciting programs due to its ease of use. Pragmatic analysis deals with overall communication and interpretation of language. It deals with deriving meaningful use of language in various situations.

Now that you have relatively better text for analysis, let us look at a few other text preprocessing methods. As we already established, when performing frequency analysis, stop words need to be removed. 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. NLP is used for a wide variety of language-related tasks, including answering questions, classifying text in a variety of ways, and conversing with users. Plus, tools like MonkeyLearn’s interactive Studio dashboard (see below) then allow you to see your analysis in one place – click the link above to play with our live public demo. However, trying to track down these countless threads and pull them together to form some kind of meaningful insights can be a challenge.


You mistype a word in a Google search, but it gives you the right search results anyway. It is a way of modern life, something that all of us use, knowingly or unknowingly. Through this blog, we will help you understand the basics of NLP with the help of some real-world NLP application examples. Microsoft ran nearly 20 of the Bard’s plays through its Text Analytics API.

examples of natural language processing

But have you ever wondered how these assistants process the things we’re saying? They manage to do this thanks to Natural Language Processing, or NLP. Natural language processing, or NLP, enables computers to process what we’re saying into commands that it can execute. From the above output , you bitbucket jenkins integration 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. Context refers to the source text based on whhich we require answers from the model.

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

If you have just learned about Natural Language Processing(NLP) or are thinking about why it is useful, you are the right place. The emergence of NLP came with the Turing test in the 1950s as an unprecedented criterion of artificial intelligence and is considered as an amalgamation of artificial intelligence, linguistics and computer science. Understanding natural language is the focal point of NLP and the center of many revolutionary technologies i.e. translation and question answering. NLP has been a challenge for computer scientists and has numerous interdisciplinary applications.

The TF-IDF score shows how important or relevant a term is in a given document. We can use Wordnet to find meanings of words, synonyms, antonyms, and many other words. In the following example, we will extract a noun phrase from the text.

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Text extraction also has a variety of uses that can help IT and business professionals alike. Text extraction can be used to scan for specific identifying information across customer communications or support tickets, making it easier to route requests or search for select incidences. NLP is also a driving force behind programs designed to answer questions, often in support of customer service initiatives. Backed by AI, question answering platforms can also learn from each consumer interaction, which allows them to improve interactions over time. 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.

examples of natural language processing

That is the reason why most of the natural language processing machines we interact with frequently seem to get better over time. Semantic analysis is the process of understanding the meaning and interpretation of words, signs and sentence structure. This lets computers partly understand natural language the way humans do. I say this partly because semantic analysis is one of the toughest parts of natural language processing and it’s not fully solved yet. By combining machine learning with natural language processing and text analytics. Find out how your unstructured data can be analyzed to identify issues, evaluate sentiment, detect emerging trends and spot hidden opportunities.

GPTs vs. Human Crowd in Real-World Text Labeling: Who Outperforms Who?

It tells us how the words are arranged, how clauses are marked, sentence correctness, part of speech and in general the knowledge of grammar in the language. Every language operates differently and cannot be understood without the syntactic understanding of that language. Syntactic analyses are the core of NLP and have many practical examples such as word writing software i.e. Even word predictors in your smartphones use syntactic rules of NLP for the next possible prediction. Part of speech tags is defined by the relations of words with the other words in the sentence. Machine learning models or rule-based models are applied to obtain the part of speech tags of a word.

Each piece of text is a token, and these tokens are what show up when your speech is processed. Though natural language processing tasks are closely intertwined, they can be subdivided into categories for convenience. The earliest decision trees, producing systems of hard if–then rules, were still very similar to the old rule-based approaches. Only the introduction of hidden Markov models, applied to part-of-speech tagging, announced the end of the old rule-based approach. Transformers library has various pretrained models with weights.

Bibliographic and Citation Tools

These assistants are a form of conversational AI that can carry on more sophisticated discussions. And if NLP is unable to resolve an issue, it can connect a customer with the appropriate personnel. In the form of chatbots, natural language processing can take some of the weight off customer service teams, promptly responding to online queries and redirecting customers when needed. NLP can also analyze customer surveys and feedback, allowing teams to gather timely intel on how customers feel about a brand and steps they can take to improve customer sentiment. Speech recognition, for example, has gotten very good and works almost flawlessly, but we still lack this kind of proficiency in natural language understanding.

  • Text Processing involves preparing the text corpus to make it more usable for NLP tasks.
  • Predictive text has become so ingrained in our day-to-day lives that we don’t often think about what is going on behind the scenes.
  • While a human touch is important for more intricate communications issues, NLP will improve our lives by managing and automating smaller tasks first and then complex ones with technology innovation.
  • However, it has come a long way, and without it many things, such as large-scale efficient analysis, wouldn’t be possible.
  • Every token of a spacy model, has an attribute token.label_ which stores the category/ label of each entity.

It involves processing natural language datasets, such as text corpora or speech corpora, using either rule-based or probabilistic (i.e. statistical and, most recently, neural network-based) machine learning approaches. The goal is a computer capable of „understanding” the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves. Natural language processing is an aspect of artificial intelligence that analyzes data to gain a greater understanding of natural human language. NLP can affect a multitude of digital communications including email, online chats and messaging, social media posts, and more.

Reinforcement Learning

While a human touch is important for more intricate communications issues, NLP will improve our lives by managing and automating smaller tasks first and then complex ones with technology innovation. Natural language processing (NLP) is an area of computer science and artificial intelligence concerned with the interaction between computers and humans in natural language. The ultimate goal of NLP is to help computers understand language as well as we do. It is the driving force behind things like virtual assistants, speech recognition, sentiment analysis, automatic text summarization, machine translation and much more.

At the same time, if a particular word appears many times in a document, but it is also present many times in some other documents, then maybe that word is frequent, so we cannot assign much importance to it. For instance, we have a database of thousands of dog descriptions, and the user wants to search for “a cute dog” from our database. The job of our search engine would be to display the closest response to the user query. The search engine will possibly use TF-IDF to calculate the score for all of our descriptions, and the result with the higher score will be displayed as a response to the user. Now, this is the case when there is no exact match for the user’s query.

Language Translator can be built in a few steps using Hugging face’s transformers library. I am sure each of us would have used a translator in our life ! Language Translation is the miracle that has made communication between diverse people possible. The parameters min_length and max_length allow you to control the length of summary as per needs. Then, add sentences from the sorted_score until you have reached the desired no_of_sentences. Now that you have score of each sentence, you can sort the sentences in the descending order of their significance.

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