11 Real-Life Examples of NLP in Action

• by siteadmin
• October 16, 2023

We, as humans, perform natural language processing (NLP) considerably well, but even then, we are not perfect. We often misunderstand one thing for another, and we often interpret the same sentences or words differently. In this article, we explore the basics of natural language processing (NLP) with code examples. We dive into the natural language toolkit (NLTK) library to present how it can be useful for natural language processing related-tasks. Afterward, we will discuss the basics of other Natural Language Processing libraries and other essential methods for NLP, along with their respective coding sample implementations in Python. 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.

However, as human beings generally communicate in words and sentences, not in the form of tables. In natural language processing (NLP), the goal is to make computers understand the unstructured text and retrieve meaningful pieces of information from it. Natural language Processing (NLP) is a subfield of artificial intelligence, in which its depth involves the interactions between computers and humans. As mentioned earlier, virtual assistants use natural language generation to give users their desired response. To note, another one of the great examples of natural language processing is GPT-3 which can produce human-like text on almost any topic. The model was trained on a massive dataset and has over 175 billion learning parameters.

Free Tools

Analytically speaking, punctuation marks are not that important for natural language processing. Therefore, in the next step, we will be removing such punctuation marks. Using NLP, more specifically sentiment analysis tools like MonkeyLearn, to keep an eye on how customers are feeling. You can then be notified of any issues they are facing and deal with them as quickly they crop up.

A lot of the data that you could be analyzing is unstructured data and contains human-readable text. Before you can analyze that data programmatically, you first need to preprocess nlp examples it. In this tutorial, you’ll take your first look at the kinds of text preprocessing tasks you can do with NLTK so that you’ll be ready to apply them in future projects.

Deep Q Learning

In this analysis, the main focus always on what was said in reinterpreted on what is meant. We have implemented summarization with various methods ranging from TextRank to transformers. You can analyse the summary we got at the end of every method and choose the best one. 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. Next, pass the input_ids to model.generate() function to generate the ids of the summarized output. You need to pass the input text in the form of a sequence of ids.

The concept is based on capturing the meaning of the text and generating entitrely new sentences to best represent them in the summary. Spacy gives you the option to check a token’s Part-of-speech through token.pos_ method. The stop words like ‘it’,’was’,’that’,’to’…, so on do not give us much information, especially for models that look at what words are present and how many times they are repeated. These techniques are based on our feelings and thoughts, bearing the capacity to shape our realities.In a nutshell, the NLP techniques discussed in this article could transform your life completely.

Smart Assistants

Notice that the term frequency values are the same for all of the sentences since none of the words in any sentences repeat in the same sentence. Next, we are going to use IDF values to get the closest answer to the query. Notice that the word dog or doggo can appear in many many documents. However, if we check the word “cute” in the dog descriptions, then it will come up relatively fewer times, so it increases the TF-IDF value. So the word “cute” has more discriminative power than “dog” or “doggo.” Then, our search engine will find the descriptions that have the word “cute” in it, and in the end, that is what the user was looking for. If a particular word appears multiple times in a document, then it might have higher importance than the other words that appear fewer times (TF).

• 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.
• You iterated over words_in_quote with a for loop and added all the words that weren’t stop words to filtered_list.
• Today, there is a wide array of applications natural language processing is responsible for.
• As the text source here is a string, you need to use PlainTextParser.from_string() function to initialize the parser.
• Microsoft ran nearly 20 of the Bard’s plays through its Text Analytics API.
• In life, action is one of the greatest equalizers among people with individuals who take the most actions correctly getting exactly what they want.

Based on this , the algorithm assigns scores to each sentence in the text . In fact, the google news, the inshorts app and various other news aggregator apps take advantage of text summarization algorithms. If you’d like to learn how to get other texts to analyze, then you can check out Chapter 3 of Natural Language Processing with Python – Analyzing Text with the Natural Language Toolkit. Some sources also include the category articles (like “a” or “the”) in the list of parts of speech, but other sources consider them to be adjectives.

NLP Search Engine Examples

This technology is still evolving, but there are already many incredible ways natural language processing is used today. Here we highlight some of the everyday uses of natural language processing and five amazing examples of how natural language processing is transforming businesses. We don’t regularly think about the intricacies of our own languages.

The below code removes the tokens of category ‘X’ and ‘SCONJ’. All the tokens which are nouns have been added to the list nouns. You can print the same with the help of token.pos_ as shown in below code. In spaCy, the POS tags are present in the attribute of Token object. You can access the POS tag of particular token theough the token.pos_ attribute. You can use Counter to get the frequency of each token as shown below.

Rule-based NLP vs. Statistical NLP:

Next, you can pass the input_ids to the function generate(), which will return a sequence of ids corresponding to the summary. Another awesome feature with transformers is that it provides PreTrained models with weights that can be easily instantiated through from_pretrained() method. It’s time to initialize the summarizer model https://www.globalcloudteam.com/ and pass your document and desired no of sentences as input. Text summarization in NLP is the process of summarizing the information in large texts for quicker consumption. In this article, I will walk you through the traditional extractive as well as the advanced generative methods to implement Text Summarization in Python.

You need to build a model trained on movie_data ,which can classify any new review as positive or negative. This technique of generating new sentences relevant to context is called Text Generation. For language translation, we shall use sequence to sequence models. The summary obtained from this method will contain the key-sentences of the original text corpus. It can be done through many methods, I will show you using gensim and spacy. This is the traditional method , in which the process is to identify significant phrases/sentences of the text corpus and include them in the summary.

What is Tokenization in Natural Language Processing (NLP)?

And yet, although NLP sounds like a silver bullet that solves all, that isn’t the reality. Getting started with one process can indeed help us pave the way to structure further processes for more complex ideas with more data. Ultimately, this will lead to precise and accurate process improvement. Owners of larger social media accounts know how easy it is to be bombarded with hundreds of comments on a single post. It can be hard to understand the consensus and overall reaction to your posts without spending hours analyzing the comment section one by one.

We, as humans, perform natural language processing (NLP) considerably well, but even then, we are not perfect. We often misunderstand one thing for another, and we often interpret the same sentences or words differently. In this article, we explore the basics of natural language processing (NLP) with code examples. We dive into the natural language…