Natural language processing is an effective ability of machines and AI systems in the modern era thanks to the boost it provides for businesses, schools, hospitals, and other types of establishments. How exactly does NLP improve workflow and enhance efficiency? Look into the most well-known applications of natural language processing to find out.
What Are Some Applications of Natural Language Processing?
NLP in Business and Customer Support
Naming every single application of NLP in business can be difficult as it can cover anything from content generation to enhancing chatbots. So let’s start with the most common function of NLP in business.
NLP-powered chatbots and virtual assistants offer near-instant and round-the-clock customer support, especially for frequently asked questions. While the bots itself are not about NLP, the addition of this ability allows them to interpret the intent behind customer inquiries and provide accurate responses or at least direct them to the right human workers when needed.
Overall, NLP helps reduce customer wait times and optimize operational costs by handling routine tasks automatically.
NLP plays a tremendous role in customer support
Another application of NLP is automated document summarization and content generation. This will save a tremendous amount of time without jeopardizing the quality of content output. Also, summarization allows businesses to quickly gather key points from lengthy documents and sift through large amounts of information efficiently.
With that said, the role of human editors are still indispensable as the NLP technology merely recognizes and mimics the normal language patterns, so it is up to them to make sure that the content still has a unique flair and perfectly captures the identity of the business or the content creator.
The third major application of NLP in business is sentiment analysis. In other words, it comes up with the general consensus of consumer opinions from online feedback, reviews, or social media posts.
Since there are millions of ways that users can state their opinions, NLP-driven models can divulge the true meaning, therefore identifying whether feedback is positive, negative, or neutral. From there, companies can understand customer satisfaction trends, address complaints, and adjust marketing strategies accordingly.
NLP in Healthcare
Known as one of the most important industries, does the use of NLP hinder the trustworthiness of healthcare operations? In fact, it is quite the opposite, as NLP can greatly enhance the efficiency of the workers.
Same as regular businesses, the summarization ability also applies to medical documentation. With this, doctors and nurses can automate the extraction process of essential information such as diagnoses, medications, and patient histories. NLP also assists with regulatory compliance, ensuring that medical records meet legal and industry standards.
NLP can significantly enhance healthcare operations
With this information ready in a moment’s notice, doctors can accelerate the diagnostic process, analyze patient data, look up relevant information surrounding different health conditions and medications, and spot potential health risks based on symptoms and medical histories. For example, by scanning clinical notes, an NLP-powered system might highlight high-risk factors that require further investigation to help doctors make informed decisions promptly.
Additionally, patient-doctor communication is also key in any treatment process and healthcare chatbots with NLP are increasingly used to answer patient questions, offer basic health advice, and assist with appointment scheduling. While all healthcare facilities offer 24/7 support, NLP chatbots can reduce the strain on medical staff and make it easier for patients to access healthcare information.
NLP in Finance
Finance is an industry where developments occur at any given time, so NLP can easily slot into the operation of businesses in this industry. Starting with fraud detection, this is something that requires prompt responses, so the textual analysis ability of NLP is absolutely perfect.
Specifically, financial institutions look at transaction descriptions and customer communications. Looking into these aspects with the help of NLP will help them point out irregularities in language or transaction data, therefore flagging potential fraudulent transactions.
Compliance is essential in finance, where failure to meet regulations can result in heavy fines. Prevention is always better, so NLP aims to simplify this process by automating document analysis, helping financial institutions keep up with developments on the market as well as polish their operations.
Financial services can benefit greatly from NLP integration
Examples of NLP Techniques
Key NLP Techniques Used in Applications
The benefits of NLP are plenty, and to make it happen, it involves blending a wide variety of techniques. To work NLP into your systems, it is essential to understand these following techniques:
Tokenization and Text Preprocessing: It is very easy to come across the term “tokens” when using generative AI models, but few really understand what it means. Since NLP-driven systems analyze texts by breaking them down into units, those units are referred to as tokens. This will help the analyzing process go by smoother, allowing for quicker and more relevant results.
Named Entity Recognition (NER): Identifying and classifying names, places, dates, and other entities within the text is a valuable ability. AI-powered machines can store these information as well as tailoring their responses according to these entities.
Part-of-Speech Tagging (POS): Labeling words as nouns, verbs, adjectives, etc., to understand grammatical structure. This is most commonly found in tools like machine translation, grammar check, paraphrasing, and content generation.
Text Classification and Sentiment Analysis: Reading between the lines has been a major issue of machines and this technique addresses the core of the problem. Categorizing text data and determining sentiment can help algorithms figure out the context as well as the connotation meaning of words and sentences.
Machine Translation and Text Summarization: The previous techniques will contribute to the AI models’ ability to translate, summarize, proofread, and generate texts.
NLP techniques are diverse to serve numerous applications
Advanced NLP Techniques
The previous techniques are plainly the tip of the iceberg as they mostly address the simpler tasks. These following techniques, on the other hand, lay the foundation for NLP systems:
Deep Learning Models: Models like GPT, BERT, and Transformer networks have transformed NLP. As the name suggests, these models draw insights from various sources as well as user input to recognize and understand natural language patterns.
Word Embeddings: These systems use vectors to represent words, which makes it easier for them to understand the relationship and similarities between words. Word2Vec and GloVe are two of the most commonly used techniques in this group.
Neural Machine Translation Systems: To address the increasing demands of text translation, NLP-driven translation systems like Google Translate, Memsource, Matecat and more can handle complex language pairs. They learn from a variety of sources to recognize specific contexts and terminologies for the best translation.
Application of NLP to Design Expert System
How NLP is Applied in Expert Systems
Expert systems essentially simulate the analyzing and processing flow of experts in a certain field using the power of AI. This means it is a highly-trained AI model that can analyze large datasets and extract domain-specific knowledge with greater efficiency.
Now with the various capabilities of NLP, these systems can become invaluable in fields like law, medicine, and finance by automating decision-making processes. For instance, an NLP-powered legal expert system could analyze case law and provide legal opinions, while a medical expert system could suggest diagnoses based on symptoms.
NLP can enhance the effectiveness of expert systems
Examples of Expert Systems Using NLP
Depending on the industry they are working in, these NLP-driven systems come with different uses:
- Legal Expert Systems: Assist with document review and legal analysis, playing a role in offering legal advice and filling out forms and documents.
- Healthcare Expert Systems: Provide diagnostic recommendations and treatment options based on patient data and enable healthcare workers to look up medications and health conditions quickly on their system.
- Financial Expert Systems: NLP can help analyze market data and news for automated trading and risk assessment.
Leverage the applications of NLP with the help of BytePilot
The capabilities of NLP have translated to various applications in many key industries including healthcare, business, and finance. They play a huge role in automation efforts that can greatly enhance work efficiency and allow human workers more time to address more complicated tasks.
If your business wants to know more about NLP and its applications, contact BytePilot to learn more about our AI and data services.