An Overview of Top Applications of NLP

Before delving into the specific applications of NLP across various industries, it’s worth exploring some of the most common yet innovative ways NLP is being used to revolutionize the way we communicate and interact with technology.

Text classification:
Text classification involves categorizing unstructured text into predefined categories using rule-based systems or machine learning. Text classifiers assign relevant tags to content, aiding in efficient document management and extracting valuable insights.

A prime example is LinkedIn, which employs text classification to flag profiles containing inappropriate content. Machine-based classifiers often use the “bag of words” representation, which simplifies text by focusing on word frequency to provide a clear understanding of the content’s context.

Text extraction:
Text extraction utilizes machine learning to automatically identify and extract relevant words, phrases, and specific information from unstructured text, such as articles, surveys, and customer service tickets.

This process, also known as keyword extraction, offers various benefits, including summarizing texts, cleaning data, and uncovering common or recurring themes.

For instance, a text extractor can analyze a statement like, “While the new design is visually appealing, the cost is rather high and the speed is impressive,” to extract keywords such as “visually appealing,” “cost,” and “speed,” providing valuable insights for decision-making.

Text extraction simplifies large amounts of data by identifying key phrases and words efficiently.

Text summarization:
Text summarization simplifies large volumes of data by extracting relevant information, making it easier for users to access vital points. It works through extraction-based methods, selecting key phrases, or paraphrasing the original content.

A unique example of text summarization is the automatic generation of news headlines, providing readers with a concise understanding of the article without delving into every detail.

Machine translation:
Machine translation (MT) translates text between languages using machine learning algorithms and neural networks to process linguistic data.

MT provides numerous benefits, especially in business applications and social media platforms, by enabling seamless communication across different languages. For example, Facebook and Instagram integrate MT in their applications, connecting users globally. Despite challenges in handling idioms and ambiguity, MT has made significant progress in recent years, offering fast and cost-effective translations.

Sentiment analysis:
Sentiment analysis automatically interprets and summarizes emotions within textual data. Using machine learning algorithms, it can detect emotions such as “positive,” “negative,” or “neutral,” along with more specific sentiments like “anger,” “joy,” or “disgust.” Sentiment analysis is invaluable for businesses to gauge customer opinions on brands, products, and services based on online conversations or direct feedback.

Conversational agents:
Conversational agents, such as chatbots and virtual assistants like Siri, Alexa, and Google Assistant, facilitate seamless communication between humans and computers via text or speech. These agents work by understanding user commands, processing the language, and providing appropriate responses.

We’ve discussed the most well-known examples, but NLP is also being used in a variety of other useful contexts, including market intelligence, auto-correct and auto-prediction, grammar checkers, email classification and filtering, speech recognition, intent classification, urgency detection, etc. Now, let’s understand how these applications are used across various industries.

Discover the potential of NLP applications across various domains, from customer service to healthcare, and witness the future of AI.