The revolutionizing potential of the new Natural Language Processing

Author(s): Navruzbek Ibadullaev

Originally published on Towards AI.

In the tech world, NLP represents a technological shift and approach to human communication with technology entirely differently. The NLP helps in developing artificial intelligence thanks to the recognized machines that can understand, interpret, and form human language including virtual assistants, sentiment analysis, and language translation. This article will break down the complicatedness of NLP, its applications in sectors, and the impact it fully possesses on the industry and society. Natural Language Processing (NLP) is the frontier, namely, the field of quick developing interest as it aims to provide computers with the ability to detect and identify human language that is being spoken or written. This ability means that many applications have been created, such as machine translation, that frees the language barrier or spam filters, which keep our inbox clean, information extraction, from which comes very useful data, text summarization, or finding health documents that are full of useful insights and question explaining systems (Khurana et al., 2023).

Natural Language Processing

Natural language processing encompasses computational linguistics and multidisciplinary areas of artificial intelligence that tackle the problems of computers understanding and generating human-like languages. Deeper down, we can see that the main concept behind NLP is to create algorithms and models that allow computers to understand, examine, and spit out natural language data. These algorithms achieve this feat by utilizing the diverse techniques available in machine learning, particularly the advanced (deep learning) techniques, to process and analyze text data in the way human beings understand words and concepts.

Key Components of NLP

1. Tokenization: Tokenization is a word-to-word or phrase-to-phrase conversion process in which a segment of text is digitized into a unit group, called tokens. This is a necessary step, for the text will be comprehensible if the tool does not help in the analysis and meaning extraction from the provided text.

2. Part-of-Speech (POS) Tagging: POS tagging is the performance of tying a grammatical label to every word in the sentence, like the nouns, verbs, and adjectives, among others. Here, users get to grasp the syntax structure and parsing or the semantic analysis.

3. Named Entity Recognition (NER): Entity recognition (NER) is another one of the important NLP tasks, it identifies the named entities in the text such as names of people, organizations, locations, dates et al. The development of the language model plays a major role in the application of the model for activities of information extraction, entity linking, and sentiment analysis.

4. Sentiment Analysis: Sentiment analysis, which is commonly referred to as sentiment mining, is a branch of research that tries to understand and interpret the sentiment or tone of the emotions implied in the analyzed text. It labels the text as either positive, negative, or neutral, which allows companies to know if it is customer opinion about their brand, feels of the market, and consumer trends.

5. Language Translation: Language translation involves bulk processing, transforming words from one language into another. While previously NLP models were lagging in providing more reliable translations with correct grammar and sentence structure, the recent developments in computer science, such as transformer architectures, have greatly improved translation services by bringing better accuracy and flow between different languages.

Applications of NLP

The NLP technology is used for high-value purposes like healthcare and EHRs (Electronic Health Records) review. EHRs showcase a variety of data about patients in disarray: doctor notes, for example, their text structure is often not structured properly. NLP approaches can be put into use to penetrate this data, aiding the researchers and the health care providers to narrow this data to obtain valuable insights for decision-making. However, problems like the fact that there is no standardized data format and elucidating more evidence for complex NLP tools continue to be the main problems in the process. However, these flaws have good points such as recognizing medications, distinguishing the given words among others, and summarizing patients’ history, which are all responsible for better decision-making (Hossain et al., 2023).

Some other applications

1. Virtual Assistants: The use of NLP is the basis of virtual assistance like Siri, Alexa, and Google Assistant which not only understands what the user wants but also does it for him/her using a natural language. Thus, these helpers engage in various functions including reminder setting, questions answering, or controlling smart home accessories, and thus, we cannot do without them.

2. Text Summarization: NLP approaches are utilized to achieve the task of intuitive text summarization where one can shorten a lengthy document or article but still hold the main points. Users can search for specific information, documents can be grouped, as well as content for recommendations can be made based on the relevant information.

3. Chatbots and Conversational Agents: NLP makes it possible to design chatbots and natural language-based conversational agents that simulate face-to-face communication with human agents. Bots, which are the front end for customer service, sales aids, and lead generation, are deployed across all sectors of businesses and improve their standing and efficiency.

4. Social Media Analysis: NLP equips businesses with tools for analyzing social media data to know customer opinions, pick up trends, and review their brand repute. Through the analysis of the ocean of social media content, companies can design their marketing strategies based on their insights and get their important info across to the users.

5. Medical Text Mining: In the healthcare industry, NLP empowers clinicians to peruse clinical notes, research articles, and electronic health records to find valuable details. This would be very helpful in diagnostics, treatment recommendation, and pharmacovigilance and, therefore, will increase the quality of treatment and results.

Challenges and Future Directions

The Natural language process (NLP) doesn’t lag at the developmental frontiers, but it goes against many critical challenges that are worth mentioning. To begin with, the intrinsic difficulty and the context-bound nature of the language hinder the application of machines to formulate and understand texts, especially in complex contexts. On the other hand, the issue of field-based discrimination when it comes to NLP models is the other problem that is part of the training data bias raises ethical issues, mainly in contexts such as health care settings and criminal justice where the prejudiced outcomes have unimaginable implications of aggravating the discrimination issues to the stakeholders. Another consideration that requires more attention is the growth of NLP concerning diverse languages and modalities by developing models with high accuracy and scalability that can process multilingual and multimodal data successfully. In turn, the growing array of NLP applications brings a discourse concerning privacy and security laws, stressing the necessity of compliance with privacy norms and security requirements to protect the individual’s confidential data. Coming next, a bright future for NLP is foreseen, where deep learning, reinforcement learning, and transfer learning will be the springboard for NLP’s constant innovation. Interdisciplinary interactions with areas like linguistics, cognitive science, and neuroscience will bring more comprehensive knowledge of HLPs and THI, which in turn will result in the design of more effective NLP systems that change HCI.

Conclusion

The field of Natural Language Processing (NLP) continues to modernize the relations of humans with technology and advances the level of human-machine interaction up to where conversations can be natural and fluent. NLP apps have been involved in different industries other than customer services, like virtual assistant, chatbots, sentiment analysis, and language translation, just to name a few. They have played the role of driver for these domains to increase efficiency, innovation, and user engagement. As we navigate the complexities and challenges of this burgeoning field, one thing remains clear: while the promising influence of NLP seems obvious, the future of AI and remaking the borders of the human-computer connection remains unpredictable.

References

Khurana, D., Koli, A., Khatter, K., & Singh, S. (2023). Natural language processing: State of the art, current trends and challenges. Multimedia tools and applications, 82(3), 3713–3744. https://link.springer.com/article/10.1007/s11042-022-13428-4

Hossain, E., Rana, R., Higgins, N., Soar, J., Barua, P. D., Pisani, A. R., & Turner, K. (2023). Natural language processing in electronic health records in relation to healthcare decision-making: a systematic review. Computers in Biology and Medicine, 155, 106649. https://www.sciencedirect.com/science/article/pii/S0010482523001142

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI