14 Natural Language Processing Examples NLP Examples

For this project, a GPT-2 is trained on more than 2,000 article titles extracted from arXiv. You can use this application on other things, like text generating tasks for producing song lyrics, dialogues, etc. From this project, you can also learn about web scraping, because you will need to extract text from research papers in order to feed it to your model for training.

PaLM isn’t just a research achievement; it has practical uses across various business domains. It can assist in building chatbots, providing answers, translating languages, organizing documents, generating ads, and aiding in programming tasks. It’s trained on 2,500 million Wikipedia words and 800 million words of the BookCorpus dataset. Google Search is one of the most excellent examples of BERT’s efficiency. Other applications from Google, such as Google Docs, Gmail Smart Compose utilizes BERT for text prediction.

NLP methods and applications

In this case, NLP enables expansion in the use of automatic reply systems so that they not only advertise a product or service but can also fully interact with customers. The more comfortable the service is, the more people are likely to use the app. Uber took advantage of this concept and developed a Facebook Messenger chatbot, thereby creating a new source of revenue for themselves. Natural Language Processing (NLP), Cognitive services and AI an increasingly popular topic in business and, at this point, seems all but necessary for successful companies. NLP holds power to automate support, analyse feedback and enhance customer experiences.

• By leveraging these technologies, organizations can create powerful chatbots and virtual assistants that provide instant support and enhance the user experience.
• Individual words are analyzed into their components, and nonword tokens such as punctuations are separated from the words.
• Dependency parsing reveals the grammatical relationships between words in a sentence, such as subject, object, and modifiers.

For instance, if your customers are making a repeated typo for the word “pajamas” and typing “pajama” instead, a smart search bar will recognize that “pajama” also means “pajamas,” even without the “s” at the end. Instead of showing a page of null results, customers will get the same set of search results for the keyword as when it’s spelled correctly. Named entity recognition is a core capability in Natural Language Processing (NLP). It’s a process of extracting named entities from unstructured text into predefined categories. Examples of named entities include people, organizations, and locations. An NLP system can be trained to summarize the text more readably than the original text.

Techniques and methods of natural language processing

Lemmatization, similar to stemming, considers the context and morphological structure of a word to determine its base form, or lemma. It provides more accurate results than stemming, as it accounts for language irregularities. With lexical analysis, we divide a whole chunk of text into paragraphs, sentences, and words.

• Monitoring and evaluation of what customers are saying about a brand on social media can help businesses decide whether to make changes in brand or continue as it is.
• To that point, Data Scientists typically spend 80% of their time on non-value-added tasks such as finding, cleaning, and annotating data.
• In 2017, it was estimated that primary care physicians spend ~6 hours on EHR data entry during a typical 11.4-hour workday.
• Question-answer systems are intelligent systems that are used to provide answers to customer queries.
• The ability to understand and generate human language has allowed these systems to provide personalized and accurate responses to users, improving efficiency and scalability.

Cem’s work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School. Conversational banking can also help credit scoring tools analyze answers of customers to specific questions regarding their risk attitudes.

Advancements in Multi-Lingual NLP

Applying language to investigate data not only enhances the level of accessibility, but lowers the barrier to analytics across organizations, beyond the expected community of analysts and software developers. To learn more about how natural language can help you better visualize and explore your data, check out this webinar. It involves the use of algorithms to identify and analyze the structure of sentences to gain an understanding of how they are put together. This process helps computers understand the meaning behind words, phrases, and even entire passages. Natural language processing focuses on understanding how people use words while artificial intelligence deals with the development of machines that act intelligently.

CallMiner is trusted by the world’s leading organizations across retail, financial services, healthcare and insurance, travel and hospitality, and more. Natural language processing (NLP) is one of the most exciting aspects of machine learning and artificial intelligence. In this blog, we bring you 14 NLP examples that will help you understand the use of natural language processing and how it is beneficial to businesses. Natural language processing (NLP) is a field of artificial intelligence focused on the interpretation and understanding of human-generated natural language. It uses machine learning methods to analyze, interpret, and generate words and phrases to understand user intent or sentiment.

Natural Language Processing (NLP): 7 Key Techniques

Notice that we still have many words that are not very useful in the analysis of our text file sample, such as “and,” “but,” “so,” and others. Next, we can see the entire text of our data is represented as words and also notice that the total number of words here is 144. By tokenizing the text with sent_tokenize( ), we can get the text as sentences. Gensim is an NLP Python framework generally used in topic modeling and similarity detection. It is not a general-purpose NLP library, but it handles tasks assigned to it very well.

NLP can be used to interpret free, unstructured text and make it analyzable. There is a tremendous amount of information stored in free text files, such as patients’ medical records. Before deep learning-based NLP models, this information was inaccessible to computer-assisted analysis and could not be analyzed in any systematic way.

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