Applications of Natural Language Processing

Applications of Natural Language Processing

Natural Language Processing or NLP is an interdisciplinary subfield that draws concepts or principles from the fields of computer science, artificial intelligence, statistics, and linguistics. It is concerned with the interaction between humans and computers with the goal of equipping computer systems with the ability to understand and generate written texts and spoken words in a manner that is similar to humans.

It is also important to highlight the fact that NLP is one of the major fields and goals of artificial intelligence. Advancements in language models and the more specific large language models, machine learning and deep learning, as well as the expansion of applications of linguistics and statistics in the development and deployment of AI algorithms and models have resulted in the advancements of natural language processing and the leveraging of NLP applications.

A Look Into the Importance and Real-World Applications of Natural Language Processing

General NLP Applications

Remember that the purpose of NLP is to allow humans and computers to communicate in a human-like fashion by equipping computer systems with the ability to understand and generate written texts and spoken words. NLP research is essential in advancing AI and in achieving artificial general intelligence or AGI.

The following are the general applications of natural language processing that also represent common NLP tasks:

• Human-Computer Conversation: NLP can equip computer systems such as personal computers, smartphones, smart devices, and even software or applications with the capabilities to converse with humans. An NLP-powered computer can both comprehend and produce spoken words and written texts.

• Conversion Between Text and Speech: Another application of NLP is in converting text to speech or speech to text. This has been demonstrated in the voice recognition technologies used in personal computers and smartphones or applications such as search engines and virtual assistants including Siri and Alexa.

• Organization and Categorization of Content: NLP can also be used to organize and categorize content such as large bodies of text, documents, and data. It provides a linguistic document summary that includes content alerts, duplicate detection, tagging and classification, search retrieval, and indexing.

• Analysis of Sentiments from Bodies of Texts: Sentiment analysis is an NLP task that determines the sentiment expressed in a piece of text. There are two approaches to this task: rule-based sentiment analysis uses a set of pre-defined rules and lexicons and machine learning-based analysis that uses machine learning algorithms.

• Generative Artificial Intelligence: Advancements in NLP through large language models have become the foundation of several generative AI applications or services. These services can produce text content using text inputs. Other are even capable of producing other content such as images and performing specific tasks.

• Translation of Multiple Languages: NLP can also perform language translation from one language to a long list of other languages. Specific applications include document translation or on-demand and live speech translation. Advanced NLP models can translate language into another with correct grammar and composition.

• Specific Writing and Speaking Capabilities: Higher-level applications of natural language processing using NLP tasks such as text and speech processing, morphological and syntactic analyses, and lexical and relational semantics can correct grammar, provide text summarization, automatic translation, and produce long-form texts.

Specific NLP Applications

The specific applications of natural language processing or examples of NLP applications are now present in day-to-day use cases. Personal computers, web applications, smartphones, and mobile apps are equipped with some degree of NLP capabilities. Organizations have also improved their processes by leveraging natural language processing.

The following are the specific applications of natural language processing that also represent practical examples of NLP uses:

• Search Functions and Search Engine: Features such as built-in search functions of computer systems or databases and services such as search engines use NLP to comprehend text inputs or search queries and produce search results. Google Search uses language models such as BART to improve its search performance.

• Chatbots and Virtual Assistants: ChatGPT is based on the GPT-3 model of OpenAI to perform human-like responses to text-based human inputs. Apps such as Siri and Alexa or their derivative devices use NLP and their respective large language models to perform tasks commanded through text or speech.

• Writing Assistants and Tools: Remember that higher-level NLP applications have writing capabilities. Grammarly and Quillbot are prime examples of commercial services used for correcting grammar and improving composition. Other services offer full-blown writing capabilities based on minimal user input.

• Conversation and Translation Features: Google Translate is based on NLP. The multiple language settings of devices also use language models. There are specific apps that can scan and translate texts, translate spoken words into another language, transcribe speech to texts, or generate speech from texts.

• Question-and-Answer Systems: Chatbots and virtual assistants, as well as search engines, are NLP applications that are also positioned as question-and-answer systems. These systems are also found in knowledge-based systems. Businesses have integrated these systems as part of their sales and after-sales processes.

• Other Content Generation Tools: Note that texts can also be used as input to produce other non-text contents through advanced NLP models and LLMs. DALL-E is a generative AI from OpenAI that produces new and original images or artworks from text prompts. Others can produce audio or write codes based on text instructions.

• Smart Devices and Internet of Things: Combining the aforementioned different applications natural language processing and NLP tasks in a single system can create a true smart device or a smart system in accordance to the Internet of Things. The system can perform specific tasks with text and voice commands.