8 Natural Language Processing NLP Examples

NLP, with the support of other AI disciplines, is working towards making these advanced analyses possible. 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. Companies nowadays have to process a lot of data and unstructured text. Organizing and analyzing this data manually is inefficient, subjective, and often impossible due to the volume. However, trying to track down these countless threads and pull them together to form some kind of meaningful insights can be a challenge.

Even MLaaS tools created to bring AI closer to the end user are employed in companies that have data science teams. Consider all the data engineering, ML coding, data annotation, and neural network skills required — you need people with experience and domain-specific knowledge to drive your project. Deep learning or deep neural networks is a branch of machine learning that simulates the way human brains work. It’s called deep because it comprises many interconnected layers — the input layers (or synapses to continue with biological analogies) receive data and send it to hidden layers that perform hefty mathematical computations. Machines understand spoken text by creating its phonetic map and then determining which combinations of words fit the model. To understand what word should be put next, it analyzes the full context using language modeling.

Language detection

This allows them to communicate more effectively with customers in different regions. It also allows their customers to give a review of the particular product. NLP comprises multiple tasks that allow you to investigate and extract information from unstructured content. Every time you go out shopping for groceries in a supermarket, you must have noticed a shelf containing chocolates, candies, etc. are placed near the billing counter. It is a very smart and calculated decision by the supermarkets to place that shelf there. Most people resist buying a lot of unnecessary items when they enter the supermarket but the willpower eventually decays as they reach the billing counter.

• Several retail shops use NLP-based virtual assistants in their stores to guide customers in their shopping journey.
• Every piece of content on the site is generated by users, and people can learn from each other’s experiences and knowledge.
• There are even chrome extensions that can help you out, though it might be hard to scale content summaries that way.
• Even MLaaS tools created to bring AI closer to the end user are employed in companies that have data science teams.
• For example, even grammar rules are adapted for the system and only a linguist knows all the nuances they should include.

It offers solutions based on search technologies for human interaction. For example- developing a deep understanding of the linguistic structure, making search engines, and bots mimic real-life sales agents like roles. For instance, it handles human speech input for such voice assistants as Alexa to successfully recognize a speaker’s intent. Natural language processing or NLP is a branch of Artificial Intelligence that gives machines the ability to understand natural human speech.

NLP Projects Idea #1 Sentiment Analysis

Developing the right content marketing strategies is an excellent way to grow the business. MarketMuse is one such company that produces marketing content strategy tools powered by NLP and AI. Much like Grammarly, the software analyses text as it is written, thereby giving detailed instructions about the direction to ensure that the content of the highest quality. MarketMuse also analyses current affairs and recent news stories, thus providing users to create relevant content quickly. One of the best ways for NLP to improve insight and company experience is by analysing data for keyword frequency and trends, which tend to indicate overall customer sentiment about a brand. Even though the name, IBM SPSS Text Analytics for Surveys is one of the best software out there for analysing almost any free text, not just surveys.

Some of the most popular grammar checkers that use NLP include Grammarly, WhiteSmoke, ProWritingAid, etc. Till the year 1980, natural language processing systems were based on complex sets of hand-written rules. After 1980, NLP introduced machine learning algorithms for language processing. It enables the automated analysis of large-scale surveys, saving time and resources while providing a deeper understanding of participants’ opinions and preferences.

Microsoft has explored the possibilities of machine translation with Microsoft Translator, which translates written and spoken sentences across various formats. Not only does this feature process text and vocal conversations, translates interactions happening on digital platforms. Companies can then apply this technology to Skype, Cortana and other Microsoft applications.

• In earlier days, machine translation systems were dictionary-based and rule-based systems, and they saw very limited success.
• Natural language processing has the ability to interrogate the data with natural language text or voice.
• But, sometimes users provide wrong tags which makes it difficult for other users to navigate through.
• “Question Answering (QA) is a research area that combines research from different fields, with a common subject, which are Information Retrieval (IR), Information Extraction (IE) and Natural Language Processing (NLP).
• NLP algorithms in these systems analyze the context and patterns in users’ typing behavior to predict the next word or phrase they intend to type.

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