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What is natural language technology?

Author
Olga Miroshnyk
·
Jan 16, 2023
·
3 min read

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Introduction to Natural Language Technology

Natural Language Technology (NLT) is a subfield of language AI that specifically deals with the interaction between human natural language and computers. Its goal is to enable computers to understand and process human language in the same way that humans do.

NLT is becoming increasingly important in technology because it allows machines to understand and respond to human language. This is crucial for the development of applications such as chatbots, machine translation, and text-to-speech synthesis. Furthermore, NLT has a broad range of use cases from business intelligence, healthcare, customer service, and fintech. According to a market research report by ResearchAndMarkets, the global NLP market size is expected to grow from USD 11.6 billion in 2020 to USD 35.1 billion by 2026, an increase of over 200% in just 6 years. This is a clear indication of the growing importance of NLT in today's technology landscape.

In this article, we will clarify everything and sort out all the concepts that begin with NL: NLT, NLP, NLU, and NLG. 

Subfields of Natural Language Technology

Natural Language Processing (NLP), Natural Language Understanding (NLU), and Natural Language Generation (NLG) are all closely related subfields within the broader field of Natural Language Technology (NLT). While NLP is the most widely used term, it's important to note that this field is also known as NLT which encompasses the areas of natural language understanding and generation.

To kick off our exploration, here's a quick overview of the three terms that we will be delving into in this article.

  • NLP (Natural Language Processing) is the branch of AI that deals with teaching machines to understand and interpret human language in text form and transform it into structured data.
  • NLU (Natural Language Understanding) builds upon the foundation of NLP, by using the processed data, to comprehend the intent, context, and meaning of the language, and identify actions to take.
  • NLG (Natural Language Generation) takes the process a step further, by using the analyzed data to create human-like text, able to convey coherent and meaningful information.
NLT, NLU, NLG interrelations
NLT, NLU, NLG interrelations

As the visual representation illustrates, NLP, NLU, and NLG are interrelated technologies that are often utilized together to tackle complex natural language tasks. In the following sections, we will learn how NLP, NLU, and NLG work, explore the latest advancements and trends in the field and discuss how these technologies are shaping the future of various industries. 

NLP

Natural Language Processing (NLP) is a subfield of artificial intelligence and computer science that is focused on enabling computers to understand human language in both written and verbal forms. It involves machine learning and deep learning techniques to complete tasks such as language translation and question answering.

In NLP, methods from computer science, artificial intelligence, linguistics, and data science are combined with methods from computational linguistics. The main objective is to take unstructured data and convert it into a structured data format. This is done through several methods, such as named entity recognition, tokenization, stemming, and lemmatization.

Named entity recognition (NER) identifies named entities in text, for example, people's names, locations, and organizations. Tokenization, stemming, and lemmatization are processes that help to identify word patterns by breaking down the text into smaller units called tokens, and then identifying the root forms of words. This helps to identify and extract meaning from text.

Different NLP algorithms and approaches are used for different types of language tasks. For example, hidden Markov chains tend to be used for part-of-speech tagging, recurrent neural networks are used to generate the appropriate sequence of text, and N-grams are used as a simple language model to assign probabilities to sentences or phrases to predict the accuracy of response.

NLP plays a crucial role in popular technology such as chatbots, or speech recognition products like Amazon’s Alexa or Apple’s Siri, however, its application has been broader than that. The technology allows for the analysis of vast amounts of data, which can be used for research, decision-making, and automating business processes. The ability to extract insights from unstructured data and understand human language can provide significant advantages in industries like healthcare, where large amounts of patient data are collected, or in education, where it can be used to personalize and improve the learning experience. NLP is an essential tool that enables organizations to make sense of all the information they have and use it to drive their decisions and improve their operations.

NLU

Natural Language Understanding (NLU), in its turn, is a subfield of Natural Language Processing (NLP) that deals with the ability of computers to understand the meaning of text written in natural language. Unlike NLP which is mainly concerned with preprocessing and analyzing text, NLU goes a step further by using the processed data to understand the intent, context, and meaning of the text.

NLU typically utilizes a combination of rule-based systems and machine learning to analyze text. Rule-based systems involve the use of predefined rules and patterns to extract information from text, while machine learning methods use statistical models to identify patterns in the text.

One example of an NLU task is sentiment analysis, which involves determining the sentiment (positive, negative, or neutral) expressed in a piece of text. Sentiment analysis can be used in various applications such as monitoring customer feedback on social media, analyzing product reviews, or analyzing opinions on a political topic. In this case, the NLU system would use a combination of pre-defined rules and machine learning to analyze the text and determine the sentiment expressed.

NLG

Natural Language Generation (NLG) is an advanced Artificial Intelligence technique that transforms non-linguistic representations of information into human-like text. It is a crucial subfield of NLP, along with NLU, that work in harmony to enable natural language understanding and generation across various applications such as language translation, dialogue systems, and more.

There are several stages involved in the process of natural language generation (NLG):

  • Content determination: This is the first step in which the system decides what information to include in the generated text. It involves selecting relevant information from a database or other source and organizing it in a logical manner.
  • Microplanning: Once the relevant information has been selected, the system then organizes it into a coherent structure, such as a sentence or a paragraph.
  • Surface realization: In this stage, the system takes the structured information and generates the actual text. This involves deciding on the appropriate words and phrases to use, as well as the correct grammar and sentence structure.
  • Realization Review: The generated text is then reviewed by a human or another model to check for errors and to ensure that it is grammatically correct and semantically appropriate.
  • Evaluation: the generated text is evaluated on various parameters such as grammatical correctness, fluency, coherence, and relevance.
  • Iterative Improvement: Iterative improvements may be necessary by returning to earlier stages and adjusting the model based on the evaluation.

One of the most notable applications of NLG is in the field of customer service, where a chatbot powered by NLG can simulate human conversation and provide quick and efficient support to customers in various industries by generating natural language responses to customer inquiries based on information from a database or other source.

Techniques and Methods

Natural Language Technology (NLT) is a field that uses various techniques and methods to analyze, understand, and generate human language. Some of the most commonly used techniques and methods in NLT include:

Machine Learning: Machine learning is a method that uses algorithms to learn from data and make predictions or decisions. It is widely used in NLT for tasks such as sentiment analysis, named entity recognition, and text summarization.

Deep Learning: Deep learning is a subset of machine learning that uses neural networks with multiple layers to learn from data. It is particularly useful for tasks such as language generation, machine translation, and text classification.

Rule-Based Systems: Rule-based systems use a set of pre-defined rules and heuristics to analyze and understand the text. They are commonly used in tasks such as part-of-speech tagging, syntactic parsing, and named entity recognition.

Language Modeling: Language modeling is a statistical method used to predict the probability distribution of a sequence of words. It is used to train models that can generate text and improve the performance of other NLP tasks such as speech recognition and machine translation.

Transfer Learning: Transfer learning is a method that involves pre-training a neural network on a large dataset and then fine-tuning it on a smaller dataset for a specific task. This approach can be used to improve the performance of tasks such as sentiment analysis, named entity recognition, and language translation.

Reinforcement Learning: Reinforcement learning is a method in which an agent learns by taking actions in an environment to maximize a reward. It is used for tasks such as dialog systems and text generation.

These techniques and methods are used to solve a wide range of NLT tasks, such as language translation, text summarization, sentiment analysis, and question-answering systems. Each technique has its own strengths and weaknesses, and the choice of technique will depend on the specific task and the resources available.

Applications

Natural Language Processing (NLP) is a rapidly-evolving field that is revolutionizing the way businesses and industries operate. From customer service chatbots to machine translation, the applications of NLP are both varied and impactful.

In the realm of customer service, chatbots powered by NLT are delivering lightning-fast assistance to customers. These AI-driven programs simulate human conversation, providing quick and efficient support to customers in a variety of industries.

Machine translation, another application of NLT, is streamlining communication and increasing accessibility in industries such as e-commerce, tourism, and business. With the ability to automatically translate text from one language to another, machine translation is breaking down language barriers and opening up new opportunities for global commerce.

Text-to-speech synthesis and speech recognition, also NLT applications, are making waves in the realm of accessibility technology. By converting written text into spoken words and vice versa, these technologies are opening up new possibilities for the visually impaired and those with mobility limitations.

In the healthcare industry, NLT is being leveraged to extract vital information from unstructured medical texts, such as electronic health records (EHRs) and clinical notes, supporting decision-making and patient care. The finance industry is also seeing the impact of NLT, as it is used to analyze news and social media, predict stock prices, extract insights from financial reports and detect fraud.

E-commerce is also seeing the benefits of NLT, with businesses using this technology to analyze customer reviews, extract insights, and recommend products. Additionally, businesses are also using NLT in chatbots to provide better assistance to customers.

It's clear that NLT is having a significant impact across various industries, and this trend is only set to continue as more companies adopt this revolutionary technology. As the field of NLT continues to evolve, we can expect to see even more innovative and impactful applications in the future.

Challenges and Future Directions

The field of Natural Language Processing (NLP) has come a long way in recent years, and the advancements made in the technology have been nothing short of astounding. From the early days of spell-checkers and machine translation, NLP has evolved to include a wide range of applications, from chatbots and personal assistants to advanced analytics and language-based AI systems. In this section, we will take a look at some of the latest trends and future directions in NLP and how they are shaping the way we interact with technology.

Advanced AI Analytics

The rise of advanced AI analytics is one of the most exciting trends in the field of NLP. With the massive amounts of data generated by digital transformation initiatives, companies are now able to gain valuable insights that can be used to optimize business processes and make better decisions. Artificial intelligence analytics tools are able to process large amounts of real-time and historical data, providing in-depth predictive insights that can be used to identify and predict market trends, and customer behavior, and analyze risk. These findings can be used to make risk management, supply chain management, and decision-making quicker and more effective, leading to improved productivity, reduced downtime, and smoother processes.

Streamlining Customer Support: NLP-Powered Ticket Management and Next-gen Chatbots

As the volume of customer support tickets continue to rise, businesses are looking to automate simple customer service tasks to improve efficiency and response times. NLP tools can be integrated with help desk software to automate tasks like tagging and routing tickets, freeing up customer service agents to focus on more complex issues. Additionally, chatbots are becoming a popular solution for handling routine customer service requests. With advancements in NLP, we can expect to see an evolution in chatbot technology - from self-improvement to more advanced conversations and task-completion capabilities.

Low-Code Tools Going Mainstream

The world of NLP and machine learning used to be exclusive to those with advanced coding and data science skills. However, that is no longer the case. Low-code/no-code tools such as One AI are making it easier for non-technical users to access and utilize NLP capabilities. With a user-friendly interface, One AI's platform allows users to easily build, train, and integrate NLP models for tasks such as text classification and sentiment analysis. As a result, we can expect to see an increasing number of businesses implementing NLP tools in their operations in the near future.

Transformer-based models innovations

One of the most significant trends in NLT is the growing use of pre-trained language models such as GPT-3. These models have been pre-trained on massive amounts of data and can understand the context of words in a way that previous models could not. Innovations in NLP will continue to advance, with a focus on transformer-based models such as BERT (Bidirectional Encoder Representations from Transformers) and ELMo (Embeddings from Language Models). These models have been trained on colossal amounts of data which allows for more accurate and efficient results. As these models continue to evolve and improve, we can expect to see them being implemented in a wide range of industries and applications.

Transfer learning

Transfer learning is a technique in machine learning where a model that has been trained on one task is used as a starting point for a model on a different but related task. This technique is particularly useful for NLP tasks because of the large amount of labeled data and computational resources required to train deep learning models. Transfer learning has been a popular trend in NLP in recent years, as it allows for the reuse of pre-trained models, such as BERT and ELMO, to improve the performance of various NLP tasks such as text classification, sentiment analysis, and named entity recognition. This allows researchers and practitioners to build powerful models with relatively fewer data and computational resources.

Conclusion

Natural language techniques have come a long way from their humble beginnings in the 1960s. From language translation to sentiment analysis, NLP has proven to be a powerful tool for businesses looking to gain insights from unstructured data. As the field continues to evolve, we can expect to see even more sophisticated and accurate models that can understand the nuances of human language.

One such solution that stands out in the NLP landscape is One AI. This platform offers a unique blend of cutting-edge technology and ease of use, making it accessible to a wide range of users. With its intuitive interface and powerful functionality, One AI is the perfect choice for businesses looking to take advantage of the latest advancements in NLP. Its ability to easily integrate with existing systems and its user-friendly interface makes it an attractive option for businesses of all sizes. Check out One AI’s Language Studio or schedule a demo and discover the limitless potential of NLP for your business today.