In modern text data analysis, NLP tools and NLP libraries are indispensable. Researchers and businesses use natural language processing tools to draw information from text data analysis. This analysis includes analyzing customer feedback, automating support systems, improving search and recommendation algorithms, and monitoring social media. There are a wide array of NLP tools and services available, and knowing their features is key to good results. While some tools are perfect for small projects, others are better for experts working on big data.
American localization specialist Lionbridge Technologies has been employing machine translation tools for many years. Eventually, its customers started asking for multilingual training data. Today, Lionbridge has a separate division entirely dedicated to AI, doing everything from collection of chatbot training data to image annotation, audio transcription and even multilingual content moderation services. To find out more about the work of the division, AI Business talked to Aristotelis Kostopoulos, vice president of product solutions, artificial intelligence at Lionbridge. Q: The AI division at Lionbridge grew out of the machine translation business, but today it does so much more.
The growth of the AI industry has led to an increasing demand for data annotation services and the birth of more and more data annotation companies. Just what are annotation services and how do you use them to their full potential? This article will go over the types of annotation services, how to ensure good data annotation quality, and tips to help minimize annotation costs. Within the field of machine learning, annotation service providers are companies that annotate and process raw data, for the purpose of training AI models. Due to the large scale of data labelling tasks, annotation companies often employ crowdworkers to label the data and complete the project within the client's timeframe.
For many companies, text analysis tools are at the heart of understanding their business, product, and customers. All of this text data is very valuable. Analyzing it means understanding how people feel and talk about our company and products. It means discovering trends, deepening our understanding of feedback, and uncovering surprising insights. However, text data analysis requires time and manpower.
Natural language is the conversational language that we use in our daily lives. If machines can understand natural language, then the potential use for technology like chatbots would increase dramatically. Since 2016, chatbot innovation has received a lot of media attention as a new interface that was expected to surpass smartphones. Now, chatbot services are becoming popular at AI conferences, but at a global scale, chatbot technology is still in the beginning stages. That is because natural language processing technology has not advanced far enough to support chatbots.