Everything You Need to Know About Text Annotation with Yao Xu Every day, we interact with different media (such as text, audio, images, and video), relying on our brain to process what media we are seeing and make meaning out of it to influence what we do. One of the most common types of media is text, which makes up …
“Appen’s platform is really easy to use. What makes it great is you can reach so many different channels because of its global outreach.” – Kenneth Benoit, Director of the Data Science Institute, LSE The University Founded in 1895, the London School of Economics and Political Science (LSE) has long been a global leader in social sciences among universities. One of …
Appen is so fast. Using their platform, we could do overnight what used to take us a month. Appen is wonderfully efficient. – Rick Britt, Vice President of AI, CallMiner The Company Founded in 2002, CallMiner is the pioneer of the artificial intelligence (AI)-powered speech analytics space. In the years since, the company has continued to advance its AI software …
We were expanding to a new market. Although we had a fully localized software, we were lacking resources, so our clients could not optimally use it. Appen helped us out with French lexicon data. – Ines Wendler, Product Manager, MediaInterface The Company For over 20 years, MediaInterface has delivered language technology solutions to primarily healthcare-related institutions in Germany and other …
We were able to quickly go through different iterations of annotation tasks with our crowd workers and understand what was working and what wasn’t. The fact that the quality control features are already built into the Appen platform makes task setup even easier. – Madeleine van Zuylen, Data Science Analyst, Allen Institute for AI The Company The Allen Institute for …
The Company A social media company needed large amounts of training data to improve its tool to better understand user-generated messages by identifying user intent, sentiment, and entities (people, places, events) from natural language. The Challenge A leading social media company needed a large amount of data to improve its machine learning model. This would allow its tool to better …
The Company A leading multinational technology company teamed up with us to help develop an automatic speech recognition (ASR) system designed from the ground up to specifically cater to children’s applications. The Challenge You might not be surprised to learn that most speech recognition systems are designed with adult speakers in mind. To date, the nuances and idiosyncrasies of …
The Company A leading global OEM has worked with us for over 10 years to develop the data needed to train its voice recognition systems for more than 20 languages. The Challenge With the proliferation of mobile devices and increasing consumer expectations to stay connected regardless of their environment, automotive makers are under growing pressure to deliver on the promise …
The Company A leading provider of vehicle electronics software approached us to collect audio and linguistic data to help develop automatic speech recognition (ASR) capabilities for its in-car infotainment system. The Challenge For an in-car infotainment system — or any automatic speech recognition system — to recognize and correctly process voice commands, it must be trained on speech data that accounts for …
Date: On-Demand Time: 1 hour Executing successful Natural Language Processing (NLP) and Speech projects in the real world is complicated. It is often difficult to find the right volume of raw data to annotate, especially if some categories/words/topics are very rare in the data. It is also difficult to find and manage the right people to annotate, transcribe or create the …
