The knowledge-intensive industry has evolved in recent years, owing to technological advancements and a growing demand for data-driven insights. Companies that provide media intelligence are no exception. As the volume of data generated by media sources grows, these companies must develop new methods for analyzing and interpreting the data in a timely and accurate manner.
Natural language generation (NLG) allows machine learning algorithms to process information and generate a more human-like response that is more likely to be understood by customers and result in better outcomes.
The Evolution of NLG
NLG is a subset of artificial intelligence (AI) that uses machine learning algorithms to transform data into natural language. This technology has the potential to significantly improve the speed and accuracy of media intelligence processes by allowing analysts to generate insights and summaries of large volumes of data in a matter of minutes.
NLG is an emerging technology that has been around since the early 2000s, when it was first used for conversational systems. NLG uses natural language processing (NLP) techniques to generate text from data sources and can be used in applications such as chatbots, automated reporting, email generation, etc.
NLG is a natural extension of NLP — it allows computers to create documents based on human input or pre-existing content using machine learning algorithms and AI. The ultimate goal of this process is to make information accessible through language instead of static visuals or icons.
NLG in Media Intelligence
NLG is a technology that can be used to automate the creation of content. Media intelligence companies should look for NLG technology that integrates with other advanced technologies, such as Natural Language Processing (NLP). NLP is a technology that enables machines to understand and interpret human language.
Here are some of the important NLG and NLP services that Identrics offers:
Custom-made automated abstractive summarisation. Automated abstractive summarisation enables you to read and comprehend lengthy texts efficiently by producing condensed, high-quality summaries. With custom-made solutions, you can enjoy the benefits of automated summarisation without sacrificing the accuracy and relevance of the output.
NLP technologies can offer a range of content enrichment tools such as named entity identification, sentiment analysis, and topic modeling. Learn more about the natural language processing enrichments that Identrics provides.
These solutions can be combined and used as components of various automated reports, allowing businesses to gain valuable insights from their text data.
Today everybody talks about: Large Language Models
Large language models are a type of machine learning model that can be used to identify content. Large language models can be trained using a large corpus of text documents (such as news articles), where each document contains multiple words. The model learns how each word relates to others in its context, which allows it to produce accurate predictions about what’s being described in an unseen document or sentence.
However, the importance of custom-made large language models in the face of upcoming regulations and uncertainties about the available GPT solutions cannot be overstated. A large amount of training data is required to build a custom language model, which must be carefully curated to ensure accuracy and relevance. Pre-built models may not be able to deliver the required level of precision in the absence of such data, particularly in the case of industry-specific terminologies and jargons. Furthermore, as media intelligence companies handle confidential and sensitive data, they need to ensure that their language models meet the highest standards of data privacy and security.
Learn more about the qualified training data for language models that Identrics can provide through Kaspian, a dependable product that provides data streams derived from archives dating back to 2013. Kaspian provides domain-specific subsets tailored to your business needs, making it simple to train language models that meet the highest standards and adapt to specific industries and domains.
The importance of knowledge bases
Knowledge bases play a crucial role in the modern information-driven world. In the media intelligence industry, knowledge bases serve as a vital resource for tracking, analysing, and understanding the ever-evolving media landscape, enabling businesses to make data-driven decisions and effectively adapt their strategies to stay ahead of the competition.
A comprehensive and well-maintained knowledge base not only improves productivity by saving time and resources, but also enhances decision-making, problem-solving, and collaboration. By providing easy access to accurate and up-to-date information, knowledge bases empower employees, customers, and stakeholders alike to make informed choices, leading to improved customer satisfaction, reduced support costs, and increased innovation. In an era where knowledge is power, a robust knowledge base is an indispensable asset for any organisation striving for success.
Furthermore, the integration of knowledge bases with large language models has the potential to revolutionize natural language processing, enabling AI-powered systems to generate more accurate, contextually relevant, and human-like responses, thus enhancing the overall user experience.
FIBEP Tech Day 2023, Rome, Italy
Our product manager, Nesin Veli, will present on NLG in the context of ChatGPT at the FIBEP Tech Day 2023, which will be held on April 21st in Rome, Italy.
In this presentation, Nesin will discuss traditional approaches to abstractive summarisation and how language models have revolutionized this field; he will delve into the challenges that pre-built models can present to media intelligence organizations and professionals; and, finally, he will share some insights into his own experience as a skilled professional with a wealth of knowledge in data transformation.