10 Examples of Natural Language Processing in Action

It is primarily concerned with giving computers the ability to support and manipulate human language. 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.

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 natural language example 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.

NLP Example for Sentiment Analysis

Spacy gives you the option to check a token’s Part-of-speech through token.pos_ method. Hence, frequency analysis of token is an important method in text processing. 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. We resolve this issue by using Inverse Document Frequency, which is high if the word is rare and low if the word is common across the corpus. Another common use of NLP is for text prediction and autocorrect, which you’ve likely encountered many times before while messaging a friend or drafting a document. This technology allows texters and writers alike to speed-up their writing process and correct common typos.

Next , you can find the frequency of each token in keywords_list using Counter. The list of keywords is passed as input to the Counter,it returns a dictionary of keywords and their frequencies. Iterate through every token and check if the token.ent_type is person or not. Every token of a spacy model, has an attribute token.label_ which stores the category/ label of each entity. NER is the technique of identifying named entities in the text corpus and assigning them pre-defined categories such as ‘ person names’ , ‘ locations’ ,’organizations’,etc..

What is natural language processing? Examples and applications of learning NLP

Online chatbots, for example, use NLP to engage with consumers and direct them toward appropriate resources or products. While chat bots can’t answer every question that customers may have, businesses like them because they offer cost-effective ways to troubleshoot common problems or questions that consumers have about their products. Each area is driven by huge amounts of data, and the more that’s available, the better the results. Bringing structure to highly unstructured data is another hallmark.

As the technology advances, we can expect to see further applications of NLP across many different industries. Natural language processing is a technology that many of us use every day without thinking about it. Yet as computing power increases and these systems become more advanced, the field will only progress. Search engines have been part of our lives for a relatively long time. However, traditionally, they’ve not been particularly useful for determining the context of what and how people search.