NLP Examples: How Natural Language Processing is Used?

nlp example

However, there any many variations for smoothing out the values for large documents. Let’s calculate the TF-IDF value again by using the new IDF value. Notice that the first description contains 2 out of 3 words from our user query, and the second description contains 1 word from the query. The third description also contains 1 word, and the forth description contains no words from the user query. As we can sense that the closest answer to our query will be description number two, as it contains the essential word “cute” from the user’s query, this is how TF-IDF calculates the value.

nlp example

Therefore, Natural Language Processing (NLP) has a non-deterministic approach. In other words, Natural Language Processing can be used to create a new intelligent system that can understand how humans understand and interpret language in different situations. 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.

Question-Answering with NLP

The words which occur more frequently in the text often have the key to the core of the text. So, we shall try to store all tokens with their frequencies for the same purpose. Now that you have relatively better text for analysis, let us look at a few other text preprocessing nlp example methods. To understand how much effect it has, let us print the number of tokens after removing stopwords. The words of a text document/file separated by spaces and punctuation are called as tokens. To process and interpret the unstructured text data, we use NLP.

nlp example

Take NLP application examples for instance- we often use Siri for various questions and she understands and provides suitable answers based on the asked context. This is where the use of NLP takes place without us realizing it! Alexa on the other hand is widely used in daily life helping people with different things like switching on the lights, car, geysers, and many other things. Using the NLP system can help in aggregating the information and making sense of each feedback and then turning them into valuable insights. This will not just help users but also improve the services rendered by the company. In any of the cases, a computer- digital technology that can identify words, phrases, or responses using context related hints.

Search results

As a result, many businesses now look to NLP and text analytics to help them turn their unstructured data into insights. Core NLP features, such as named entity extraction, give users the power to identify key elements like names, dates, currency values, and even phone numbers in text. By capturing the unique complexity of unstructured language data, AI and natural language understanding technologies empower NLP systems to understand the context, meaning and relationships present nlp example in any text. This helps search systems understand the intent of users searching for information and ensures that the information being searched for is delivered in response. We all hear “this call may be recorded for training purposes,” but rarely do we wonder what that entails. Turns out, these recordings may be used for training purposes, if a customer is aggrieved, but most of the time, they go into the database for an NLP system to learn from and improve in the future.

Machine learning models or rule-based models are applied to obtain the part of speech tags of a word. The most commonly used part of speech tagging notations is provided by the Penn Part of Speech Tagging. SaaS solutions like MonkeyLearn offer ready-to-use NLP templates for analyzing specific data types. In this tutorial, below, we’ll take you through how to perform sentiment analysis combined with keyword extraction, using our customized template.

Everyday Roles of NLP

According to industry estimates, only 21% of the available data is present in a structured form. Data is being generated as we speak, as we tweet, as we send messages on WhatsApp and in various other activities. The majority of this data exists in the textual form, which is highly unstructured in nature. You can import the XLMWithLMHeadModel as it supports generation of sequences.You can load the pretrained xlm-mlm-en-2048 model and tokenizer with weights using from_pretrained() method. After loading the model, you have to encode the input text and pass it as an input to model.generate(). For this, use the batch_encode_plus() function with the tokenizer.

  • The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves.
  • This article will help you understand the basic and advanced NLP concepts and show you how to implement using the most advanced and popular NLP libraries – spaCy, Gensim, Huggingface and NLTK.
  • However, it can be used to build exciting programs due to its ease of use.
  • These artificial intelligence customer service experts are algorithms that utilize natural language processing (NLP) to comprehend your question and reply accordingly, in real-time, and automatically.