Natural Language Processing to Analyze Growing Data

Natural Language Processing (NLP) has completely changed the way we interact with technology. From getting our daily tasks completed by virtual assistants like Siri and Alexa to sophisticated chatbots for enhanced customer service, NLP is at the core of many AI innovations.

But what exactly is NLP?

NLP is a subfield of artificial intelligence (AI) that bridges the gap between human communication and machine understanding, helping computers analyze human language and leading to a treasure trove of exciting applications.

Imagine chatting with a virtual assistant that can answer your questions just like a human would or effortlessly navigating sign boards and menus in a foreign country by having real-time translation at your fingertips. These are just a few examples of how NLP makes our lives easier.

Whether it is breaking down language barriers and streamlining everyday processes, NLP is transforming user experiences while making technology more intuitive and available. According to a new study by Grand View Research, the global natural language processing market size is estimated to reach USD 439.85 billion by 2030, expanding at a CAGR of 40.4% from 2023 to 2030. The immense potential and rapid growth of this field are not ending anytime soon. Join us as we dive deeper into the world of NLP, exploring its techniques, applications and overall potential.

Understanding NLP: The Core Concepts

NLP: The modern translator between computers and humans enables machines to understand, interpret and talk to us in natural language. For NLP to achieve this remarkable feat, what really works behind the scenes is the underlying technologies, such as machine learning and deep learning to process and analyze natural language data. More on these key technologies and tasks that drive NLP applications below:

Technologies Fueling NLP

Machine Learning (ML): The ability of NLP to understand natural language comes from ML. An ML algorithm identifies patterns and generates predictions by being trained on massive volumes of data. For example, an algorithm trained on millions of emails develops the pattern and can distinguish between work and spam emails with accuracy. NLP uses ML algorithms to perform tasks like sentiment analysis and text classification.

Deep Learning: A subset of ML, deep learning uses complex neural networks to process information similar to how the human brain would do. This capability enables NLP systems to understand the intricacies of natural language, such as different types of tones (sarcastic, positive or slang) by copying how a human brain learns these fine distinctions.

Using the combination of these powerful technologies, NLP has made human-computer interaction more intuitive than ever before, from enabling search engines to become smarter powering virtual assistants and chatbots with intelligence.

NLP Tasks and Functions

Text Classification: Text classification is a core NLP functionality. Its main purpose is to organize large amounts of unstructured text (meaning the raw text data in the system). NLP algorithms are trained on huge volumes of labeled text consisting of documents and snippets of a specific category. For example, an email classification system might be trained on millions of emails labeled as “work,” “spam” or “personal.” The system then takes this text data and structures it for further analysis for specific features, such as keywords and sentence structure. Based on these features and training data, the NLP models assign a category to a new text piece.

Sentiment Analysis: How do businesses measure or understand customer satisfaction from their reviews? NLP does the real work in the background by using sentiment analysis. It enables computers to recognize the emotional tone (positive, negative or neutral) based on the written text. It is specially used in monitoring social media interactions, analyzing customer feedback and creating chatbots that can respond based on the emotions detected in the input text.

Named Entity Recognition: NER is an NLP technique used to identify and classify specific elements within text. NER enables computers to recognize, understand and extract structured information out of the input unstructured text, allowing them to categorize these entities in a meaningful way. This technique is used for applications like text summarization, question answering, knowledge graph building, etc. Consider entities as the key characters in a story. They can be the names of people, companies, locations, quantities or dates. These pre-defined entities are specifically categorized in a way to help computers understand the context (who, what, when and where) of the input text.

Machine Translation: The world just got a whole lot smaller, thanks to machine translation. It refers to the use of AI and ML algorithms to convert one language to another in the form of text or speech. This enables seamless communication across languages in real-time. Machine translation in NLP aims to not only produce grammatically correct translations but also retain their original meaning. As an example, if a Spanish tourist reads a store sign that says “Closed” in English and uses a machine translation tool to decode its meaning in Spanish. The tool would look for words that correspond to translations in the database and give out “Cerrado” as an output to the tourist.

Simply Explained: How NLP Works?

While the specific steps involved in each NLP application can vary, here’s a brief overview of the techniques that are used in NLP.

1. Text Preprocessing – As the data is fed into the system, the first step it goes through is text preprocessing. In this, the system gets rid of irrelevant information and works on organizing the given data. It removes punctuation marks, extra spaces, stop words, checks for correct spellings and makes the overall text consistent.
2. Tokenization– In the second step, the sentences from given data are broken down into smaller fragments of individual words. These smaller units are called tokens (each word is assigned a token responding to it). The system then takes these tokens to understand and analyze further. For example, a sentence like “I love my dog” can be given tokens as “I=1, love=2, my=3 and dog=4”. These tokens (numbers) enable the machine to understand the data for processing.
3. Lemmatization – In this step, different variations of each word are filtered and categorized for the true meaning. As an example, “running” and “ran” are two different words but their root meaning or relating action stays the same, no matter where they are used. Lemmatization converts these words into a common form to make the machine understand their meaning.
4. Part-of-Speech (POS) Tagging – Like the grammatical tags we used in school, POS tagging recognizes the function (is it a noun, verb, adjective, adverb, etc.) of all the words in each statement. Defining this enables the machine to understand the structure of the sentence and how the words are related to each other.
5. Text Analysis – Based on the type of NLP task, this step takes the processing further by gathering the data, prepping it and finally analyzing it for outputs. The prepping of the data is done by combining and using different techniques mentioned above. Categories are assigned to the input text, e.g., spam or important email, followed by tone identification (positive, negative or neutral) along with extracting specific entities (people, places or organizations).

Understanding the Components of NLP

NLP uses techniques from both computational linguistics and ML to analyze massive volumes of data in natural language. Broadly speaking, there are three main components of NLP:

1. NLG- Natural Language Generation, as the name suggests, is the process of enabling computational machines to generate information for effective communication. It is a branch of AI that focuses on transforming data into human-readable text or speech. To execute this, the system starts selecting relevant data from the larger set and decides what information needs to be included in the generated text. Next, it creates the structure of the text including its tone, style and the overall message. As a final step, the system chooses the right words, grammatical syntax and converts planned sentences into natural language.
2. NLU- Natural Language Understanding refers to the process of helping machines comprehend human language and grasp the meaning of the given words and sentences. The process requires breaking down human language into smaller components, sentences, words and phrases, as the first step. Once this is done, it converts the input into machine-understandable format. Finally, it extracts meaning by understanding the input text with context, ambiguity and synonyms.
3. NLQ- Natural Language Query is a specific application within the broader field of NLP to allow easy information retrieval. NLQ is a self-service BI approach that allows users to ask questions to a database or information system using business terms and get responses in natural language. NLQ enables business users to ask data-related queries, both via typing and through voice commands, so that they can analyze their enterprise data for better decision-making. The process uses different techniques like keyword search, voice interaction and language translation to facilitate these functions. Below are two approaches used in NLQ:
   • Search-based NLQ – In this approach, users ask their question in natural language by typing in a text box (think web searching). Once the query is run, the system analyzes the keywords and maps them to data points or earlier asked questions. The answer’s accuracy in this system depends on the detailing of the query and the capability of the system to map the user’s intent while searching for the query response. Think of it as searching for a book in a library. If someone knows the correct title and author, the chances of finding the book are higher.
   • Guided NLQ– This approach provides more structure and assistance as it helps users to get in-depth information on their original query by providing prompts, suggestions and drop-down menus. Users can refine their query further and select the right data fields without having to think about the underlying data. In the context of the book-searching example, guided NLQ is like asking a librarian to guide through the library’s organization and narrow down the search to find what a person is looking for.

The Kyvos Angle: How it Uses Gen AI and NLQ for Supercharged Analytics

As enterprise data grows by the second, business users often find themselves drowning in it, struggling to find relevant insights. And that’s exactly where Kyvos Copilot enters the picture. The platform leverages the power of Gen AI and NLQ and allows users to interact with complex datasets effortlessly. Generative AI is a sub-field of AI that creates original content in the form of text, images or other formats. It learns from huge datasets and creates new, original results. Leveraging this technology, users can ask questions in plain business language, and Kyvos will translate them into powerful queries and deliver relevant visualizations.

• Conversational Analytics for Everyone– Kyvos Copilot’s chat interface lets users talk directly to their data. For any natural language question, it chooses the best-suited semantic model to deliver super-fast, accurate answers in the form of visualizations or insightful reports. It also retains the context of previous inquiries, understands its connection with new questions and tailors its response accordingly.
• From Text to Powerful Queries– It empowers power users with its text-to-query capabilities by seamlessly converting natural language questions into sophisticated MDX and SQL formulas, unlocking the true power of data.
• Natural Language Summarization– Extracting key takeaways from vast datasets is another great advantage of using Kyvos Copilot. The platform analyzes anomalies, identifies KPIs, unveils trends and summarizes business insights in a human-readable format without getting wrapped up in technical details. These summaries are then delivered directly to the users’ inboxes so that they never miss any important metric.

By harnessing the power of NLQ, Kyvos Copilot allows users to have a dynamic conversation with their data and achieve superfast, actionable insights. Contact our experts to know more and understand how we deliver true self-serve analytics to global enterprises.

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