Natural Language Processing Examples in Government Data Deloitte Insights

If we find out what makes Google Maps or Apple’s Siri such incredible tools, we could also implement this technology into our business processes. The secret is not complicated and lies in a unique technology called Natural Language Processing (NLP). Google Maps and Siri are the two great natural language processing examples that help much with our daily routines. A natural language processing expert is able to identify patterns in unstructured data.

To address these models’ inherent non-deterministic nature and make our result statistically sound, we conducted 5-fold cross-validation on the test set. Our experiments demonstrate, quite surprisingly, that relatively small domain-specific models outperform GPT 3.5 and GPT-4 in the F1-score for premise and conclusion classes, with 1.9% and 12% improvements, respectively. We hypothesize that the performance drop indirectly reflects the complexity of the structure in the dataset, which we verify through prompt and data analysis. Nevertheless, our results demonstrate a noteworthy variation in the performance of GPT models based on prompt formulation. We observe comparable performance between the two embedding models, with a slight improvement in the local model’s ability for prompt selection. This suggests that local models are as semantically rich as the embeddings from the OpenAI model.

Various Stemming Algorithms:

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. Many companies have more data than they know what to do with, making it challenging to obtain meaningful insights. 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.

With more and more consumer data being collected for market research, it is more important than ever for businesses to keep their data safe. With NLP-powered customer support chatbots, organizations have more bandwidth to focus on future product development. As you start typing, Google will start translating every word you say into the selected language. Above, you can see how it translated our English sentence into Persian. As much as 80% of an organization’s data is unstructured, and NLP gives decision-makers an option to convert that into structured data that gives actionable insights. 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.

The Power of Natural Language Processing

Let’s dig deeper into natural language processing by making some examples. Teaching robots the grammar and meanings of language, syntax, and semantics is crucial. The technology uses these concepts to comprehend sentence structure, find mistakes, recognize essential entities, and evaluate context. In this age of social media and online business era, text data are coming from everywhere. Because raw text may come in with all types of impurities, unnecessary noises, spelling mistakes, and more.

Any word, group of words, or phrases can be termed as Constituents and the goal of constituency grammar is to organize any sentence into its constituents using their properties. These properties are generally driven by their part of speech tags, noun or verb phrase identification. Despite having high dimension data, the information present in it is not directly accessible unless it is processed (read and understood) manually or analyzed by an automated system.

It’s an excellent alternative if you don’t want to invest time and resources learning about machine learning or NLP. Natural Language Generation (NLG) is a subfield of NLP designed to build computer systems or applications that can automatically produce all kinds of texts in natural language by using a semantic representation as input. Some of the applications of NLG are question answering and text summarization.

• Matt Gracie is a managing director in the Strategy & Analytics team at Deloitte Consulting LLP.
• SaaS platforms are great alternatives to open-source libraries, since they provide ready-to-use solutions that are often easy to use, and don’t require programming or machine learning knowledge.
• You iterated over words_in_quote with a for loop and added all the words that weren’t stop words to filtered_list.
• Now you can say, “Alexa, I like this song,” and a device playing music in your home will lower the volume and reply, “OK.

Every day, humans exchange countless words with other humans to get all kinds of things accomplished. But communication is much more than words—there’s context, body language, intonation, and more that help us understand the intent of the words when we communicate with each other. That’s what makes natural language processing, the ability for a machine to understand human speech, such an incredible feat and one that has huge potential to impact so much in our modern existence. Today, there is a wide array of applications natural language processing is responsible for. Human language is filled with ambiguities that make it incredibly difficult to write software that accurately determines the intended meaning of text or voice data. Natural language processing may have started as a purely academic tool, but real-world applications in content marketing continue to grow.

What is Natural Language Processing? Definition and Examples

In the following example, we will extract a noun phrase from the text. Before extracting it, we need to define what kind of noun phrase we are in other words, we have to set the grammar for a noun phrase. In this case, we define a noun phrase by an optional determiner followed by adjectives and nouns. Notice that we can also visualize the text with the .draw( ) function. Stemming normalizes the word by truncating the word to its stem word.

Then it adapts its algorithm to play that song – and others like it – the next time you listen to that music station. For further examples of how natural language processing can be used to your organisation’s efficiency and profitability please don’t hesitate to contact Fast Data Science. Businesses in industries such as pharmaceuticals, legal, insurance, and scientific research can leverage the huge amounts of data which they have siloed, in order to overtake the competition.

What is Natural Language Processing?

In 2019, artificial intelligence company Open AI released GPT-2, a text-generation system that represented a groundbreaking achievement in AI and has taken the NLG field to a whole new level. The system was trained with a massive dataset of 8 million web pages and it’s able to generate coherent and high-quality pieces of text (like news articles, stories, or poems), given minimum prompts. Finally, one of the latest innovations in MT is adaptative machine translation, which consists of systems that can learn from corrections in real-time. Automatic summarization consists of reducing a text and creating a concise new version that contains its most relevant information. It can be particularly useful to summarize large pieces of unstructured data, such as academic papers. Even humans struggle to analyze and classify human language correctly.

Facebook estimates that more than 20% of the world’s population is still not currently covered by commercial translation technology. In general coverage is very good for major world languages, with some outliers (notably Yue and Wu Chinese, sometimes known as Cantonese and Shanghainese). Today, Google Translate covers an astonishing array of languages and handles most of them with statistical models trained on enormous corpora of text which may not even be available in the language pair.

But this isn’t the text analytics tool for scaling your content or summarizing a lot at once. You can analyze your existing content for content gaps or missed topic opportunities (or you can do the same to your competitors’ content). 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.

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