AI and Natural Language Processing Techniques for Real-World Applications

Artificial Intelligence (AI) has been widely discussed over the past few years, as its development and application have significantly impacted how various industries operate today.

Natural Language Processing (NLP) has been one of the fastest-growing fields of AI. It focuses on how computers and humans interact with the help of natural language. This allows machines to understand, interpret, and generate human language. The evolution of AI technologies has led to NLP also becoming more complex and popular in daily use.

This article analyzes some of the key NLP techniques and their applications in the modern world as well as their overall impact on the tech world and different areas of daily life.

Text Classification

Text classification is an NLP technique used to sort texts into different categories. It includes training a model to recognize patterns in texts and label these data based on the content. This technique is widely utilized in various applications, here are a few examples of text classification use:

• Spam Detection

Email services use text classification to filter out spam and categorize emails. By analyzing the content and context of emails, these systems can identify and block unwanted messages, ensuring a cleaner inbox for users.

• Sentiment Analysis

Businesses use sentiment analysis to receive and process public opinion about their brand, products, or services. By classifying text data from social media, reviews, etc., companies can understand customers better and adjust their strategies accordingly.

• Content Moderation

Social media platforms and different online forums use text classification to detect and manage inappropriate, misleading, or harmful content. AI helps these systems to identify offensive language, hate speech, or misinformation, i.e., creating a safer online environment.

Text classification has become particularly important today since more and more business carry out their activities online, hence, they can gain important insights into their operations and ways to improve. Secondly, the number of younger users with access to not only social media but web content in general is growing. Therefore, being able to monitor their behavior and interactions can help prevent cyberbullying or other potentially hazardous activities.

Speech Recognition

Speech Recognition technology allows spoken language conversion into an actual (written) text. This technique undergoes significant changes and improvements owing to the advancements in deep learning and is also frequently used in different real-world applications:

• Voice Assistants

Virtual assistants like Siri, Alexa, and Google Assistant use speech recognition to understand and respond to voice commands. These systems allow users to interact with their devices hands-free, hence, making everyday tasks more convenient. To make these assistants usable companies also focus a lot more on the localizations of their products adding more languages to the ML models and, therefore, expanding geographically.

• Transcription Services

One of the most frequent uses of speech recognition is a transcription service that helps to convert oral content from various events like meetings, lectures, interviews, etc. into text. This type of service comes in handy for professionals, researchers, or students by providing accurate and easily searchable text records, and saves time while studying or preparing for important events.

Additionally, speech recognition is extremely inclusive, especially for people who have hearing problems or other issues that prevent them from reading texts or making notes.

Text Generation

Text Generation is one of the areas where artificial intelligence has found a lot of praise. With its help, NLP models can generate different kinds of content. Today we also have multiple tools that can generate pictures, videos, presentations, etc., however, in this section, we will focus on those used for text.

Among the popular tools, we can single out automated content generation tools. With their help, we can create articles, blog posts, and other materials. You provide instructions to the system and it delivers a text that can further be used for whatever. The main benefit of these tools is of course time-saving. However, it is important to use them wisely. You can lose audience and ratings in search engines if all your content is AI-generated. Today, we have multiple tools to check whether the content is human-written or not. These might not be 100% accurate but are still very helpful. However, for ideas and inspiration, feel free to take advantage of content-generation tools.

Other areas where text generation is widely used are virtual assistants and chatbots. They can generate responses that seem human and often can guide customers through some common problems. This takes some load off of your customer support departments. Of course, there are cases, where human assistance is necessary. Then AI assistant redirects a customer to a live person to solve their problems.

Named Entity Recognition (NER)

Named Entity Recognition (NER) is used to identify and classify entities in text, e.g., names of people, organizations, locations, and dates. One of the key benefits of NER is that is great for finding meaningful information in large volumes of text and has several major uses in practice:

• Search engines and digital assistants use NER to improve search results by identifying key entities in queries. For example, when users search for a place to visit, like a mall or a restaurant in a certain city or country, NER helps recognize specific words as entities to deliver the most relevant results from the queries.
• In the finance industry, NER is used to extract names of companies, financial instruments, and other relevant entities from financial reports, articles, and publicized market analyses. This helps analysts and investors make informed decisions based on real-time data. As an example, you are looking for data on trends in FinTech in 2024. NER will analyze the existing data online and provide the best results.
• In healthcare, NER is utilized to extract information from medical records, research papers, and other medical notes and materials. NER helps to improve patient care and research efficiency by identifying entities such as diseases, prescribed or recommended medications, patient names, etc.

Sentiment Analysis

Sentiment Analysis helps to define the emotional tone and opinion expressed in a text. Companies use sentiment analysis to monitor social media and online reviews to understand public attitudes toward their brand. By defining positive, negative, and neutral sentiments, businesses can better understand what areas of their operations require improvement and react to customer feedback.

Secondly, sentiment analysis is helpful in market research for analyzing consumer opinions and trends. This information can improve existing product development, marketing strategies, and customer engagement efforts.

Sentiment analysis is also used to review public opinion on various political issues, conflicts, and even certain candidates. By analyzing social media posts, news articles, and surveys, political analysts can obtain important insights into public opinion regarding a certain situation or figure.

Conclusion

Natural Language Processing (NLP) techniques are here to stay. Different businesses and industries adopt NLP solutions to help better communicate with their audience, receive valuable insights, and, therefore, grow and improve. As AI continues to advance, the power of NLP will expand, leading to even better interactions between humans and machines.

These technologies can provide significant benefits across different fields. From customer service and content creation to healthcare and global communication, we are to observe a drastic shift toward automation and transformation of the world.