What is Natural Language Processing (NLP)?

Natural language processing (NLP) enables machines to understand and analyze human language, serving as the foundation for various everyday tools such as translation software, chatbots, spam filters, search engines, grammar correction tools, etc. With the help of this article, you will gain insights into the fundamentals of NLP, its associated difficulties, and explore prevalent business applications. Let’s dive in and explore fascinating aspects of NLP.

What is Natural Language Processing?

Natural language processing (NLP) is an aspect of Artificial Intelligence (AI) that enables machines to comprehend human language. Specifically, it combines essential concepts from linguistics and computer science to investigate language rules and patterns, ultimately constructing smart systems (using machine learning and NLP algorithms) that can interpret, analyze, and derive significance from both text and speech.

How Does Natural Language Processing Work?

Text vectorization in NLP tools transforms text into a machine-readable format. Machine learning algorithms are then trained on labeled data to associate inputs with corresponding outputs. In addition, these algorithms use statistical methods to understand text features, enabling them to make predictions for new data. In other words, the more data these algorithms are trained on, the more accurate their predictions become.

In addition, sentiment analysis is a popular NLP task where models learn to sort text into positive, negative, or neutral categories based on expressed opinions.

Benefits of NLP

Analyze larger data sets

Companies often handle vast amounts of unstructured data, which conventional computers struggle to process manually due to time constraints and potential errors. NLP technology automates and streamlines these tasks, ensuring accuracy and efficiency.

Provide a more objective analysis

When humans repeatedly handle tasks involving reading or analyzing large amounts of text data, they may lose focus and make mistakes due to personal emotions. Computers, on the other hand, provide objective analysis and can complete these tasks quickly.

Streamline daily processes

Regularly checking customer feedback to enhance business strategy is essential. However, having employees work full-time on manual data analysis is not effective when compared to NLP tools. In other words, by linking NLP tools to your company’s data, you can promptly understand customer opinions without burdening employees with constant repetitive tasks.

Improve customer experience

Not responding promptly to customer requests can lead to a company’s downfall. Detecting potential crises requires understanding not just what customers say about products/services, but also why. Many NLP tools are available to help companies respond to customers quickly.

Extract actionable insights

Analyzing social media, surveys, and reviews for valuable insights can be time-consuming. AI-driven NLP tools automate this process, quickly identifying relevant conversations and providing actionable insights.

Challenges with NLP

Misspellings

Natural languages contain misspellings, typos, and inconsistencies in style. For instance, words like “process” can be spelled as “process” or “processing,” posing challenges for intelligent tutoring systems, especially when accents or non-standard characters are involved.

Language differences

An English speaker may say, “I am going to work tomorrow morning,” while an Italian speaker would say, “Domani Mattina vado al lavoro." Despite conveying the same meaning, NLP may not comprehend the latter unless it is translated into English first.

Innate Biases

NLP systems are built on human logic and datasets. However, they can sometimes reflect the biases of their programmers or the data they have trained on. This, in turn, can lead to different interpretations of context and potentially inaccurate results.

Training Data

A significant challenge in natural language processing is dealing with inaccurate training data. The quality of results improves with more training data. Nevertheless, providing the system with incorrect or biased data can lead to learning the wrong things or ineffective learning.

Use Cases of NLP

Intelligent document search

NLP documents can sift through free text data to find all relevant details. However, these solutions highly rely on identifying patterns within extensive amounts of unstructured data.

Sentiment analysis

Sentiment analysis involves analyzing emotions in text categorizing them as positive, negative, or neutral. By applying sentiment analysis to social media posts, product reviews, NPS surveys, and customer feedback, businesses can gain valuable insights into how customers perceive their brand.

Fraud detection

Combining NLP with ML and predictive analytics enhances cybersecurity in online learning by detecting fraud and misinformation in financial documents. For instance, research found that NLP linguistic models successfully identified deceptive emails based on patterns like decreased use of first-person pronouns and exclusive words, along with increased occurrences of negative emotion words and action verbs.

Conversational AI / Chatbot

Conversational AI refers to technology enabling automatic communication between computers and humans, powering chatbots and virtual assistants such as Siri or Alexa. In addition, chatbots find applications across various industries, facilitating customer conversations and automating rule-based tasks such as answering FAQs or making hotel reservations.

Named Entity Recognition

Named Entity Recognition (NER) enables the extraction of names of entities such as people, companies, places, etc., from your data.

Social Media Monitoring

Social media monitoring utilizes NLP to shift through the significant amount of comments and queries that companies receive on their posts or across all social channels. These monitoring tools employ sentiment analysis to identify emotions such as irritation, frustration, happiness, or satisfaction.

Predictive text

Every time you type on your smartphone, you witness NLP in action. The texting app suggests correct words after typing a few letters, improving accuracy with each use. Over time, it successfully recognizes commonly used words and names, often predicting them faster than you can type.

The Future of Natural Language Processing in Education

The future of AI in education promises personalized learning experiences through NLPs. Let’s discuss some of the most significant advancements that are yet to come.

• Companies such as Google are exploring Deep Neural Networks (DNNs) to advance NLP and create human-to-machine interactions that closely mimic human-to-human interactions.
• Basic words can be broken down into precise semantics for use in NLP algorithms.
• NLP algorithms can be applied to various languages, including regional languages or those spoken in rural areas, expanding accessibility.
• Translation of sentences from one language to another is a broad application of NLP.

Conclusion

Natural language processing enables computers to understand, interpret, and generate human language, revolutionizing communication and information processing.