Building a human-like AI or ML model requires a lot of training data. For a model to make decisions and take action, it must be trained through data annotation to understand specific information. But what is data annotation? Data labeling refers to the classification and labeling of data for artificial intelligence applications. We must properly organize and label training data for a specific use case. With high-quality human-annotated data, companies can build and improve AI implementations to create products that enhance customer experience, such as product recommendations, relevant search engine results, computer vision, speech recognition, chatbots, and more. The main types of data include text, audio, images, and video , and many companies are taking advantage of different types of data. In fact, according to the State of AI and Machine Learning 2022 report, organizations say they are using 25% more data types than the previous year. With the variety of data types required across industries and workplaces, investing in reliable training data has never been more important. Next, let's take a closer look at each label type. We list practical use cases for each data type to help you understand the different types of data labeling.
text annotation
Text annotations are still the most commonly used type of data annotation. In the Machine Learning Report, 70% of companies surveyed said they rely heavily on textual data. Essentially, text annotation refers to the use of metadata tags to highlight keywords, phrases or sentences to teach machines how to correctly recognize and understand human emotions through text. The highlighted "emotions" were used as training data to improve the machine's processing and engagement in natural human language and digital text communication. In text annotation, accuracy is everything. If not labeled properly, it can lead to misunderstandings, and it can also increase the difficulty of understanding words in a specific context. Machines need to understand all the potential wordings for a particular question or point of view based on how humans talk or interact over the internet. Take chatbots, for example. When a consumer asks a question in a way that is unfamiliar to a machine, the machine may not be able to understand the problem and offer a solution. The more accurate the annotation of the text involved, the more machines can perform time-consuming tasks that humans would normally have to deal with. This not only optimizes customer experience, but also helps businesses meet profit targets and make better use of human resources. But do you know the different forms of text annotation? Text annotation includes various annotation types such as sentiment, intent, and search intent.
Sentiment Annotation
Sentiment analysis refers to assessing attitudes, sentiments, and opinions, ultimately providing valuable insights that inform important business decisions. Therefore, having the right data is critical at the initial stage. To obtain this data, human annotators are often relied upon, as they can perform sentiment assessment and content moderation across different online platforms. From commenting on social media and e-commerce sites, to flagging and reporting profanity, sensitive or emerging keywords, humans are exceptionally good at analyzing sentiment data because they understand nuance and modern trends, slang and other language usage. If information is poorly presented and understood, it can affect or damage an organization's reputation.
Intent Annotation
As people increasingly communicate using human-machine interfaces, machines must be able to understand natural language and user intent. If the machine cannot recognize the intent, it cannot proceed with the request and may require the interactor to reformulate the language. If after reorganizing the problem, the machine still can't recognize it, it will hand over the problem to humans, and in this case, the machine loses the meaning of existence in the first place. Multi-intent data collection and classification divides intent into several key categories, including requests, orders, reservations, recommendations, and confirmations. These categories help machines easily understand the initial intent behind a query, allowing them to better respond to requests and find solutions.
Semantic Annotation
Semantic annotation involves marking a particular document as the most relevant semantic concept for the information. This involves adding metadata to the file, enriching the content with concepts and descriptive words that explain the depth and meaning of the text. Semantic tagging can both improve product listings and ensure customers can find what they are looking for. This helps convert browsers into buyers. The Semantic Annotation service helps train algorithms to identify components and improve overall search relevance by labeling product titles and individual semantic components in search queries.
Named Entity Annotation Named Entity Recognition
Named entity recognition (NER) is used to recognize certain entities in text to detect key information in large datasets. Official names, places, brand names, and other identifiers are all information detected and organized by named entity annotation. The NER system requires a large amount of manually labeled training data. Companies like Appen apply named entity labeling for a wide variety of use cases, such as helping e-commerce customers identify and label a set of key descriptors, or helping social media companies label entities such as people, places, companies, organizations, and titles, to help better target ad content. Multi-intent data collection and classification divides textual intents into several key categories, including requests, orders, reservations, recommendations, and confirmations. These categories can help machines understand the initial intent behind queries so they can better respond to requests and find solutions.
Microsoft Bing & Appen: Optimizing Search Quality
Microsoft's search engine, Bing, requires large-scale data sets to continuously improve the quality of its search results and match the culture of different countries and regions. We've exceeded expectations and fueled the rapid growth of Microsoft Search in new markets. In addition to project delivery and management, we also provide high-quality data sets to promote the continuous improvement of the quality of Microsoft Bing search. As the Bing team continues to explore new heights in the search quality experience, we are constantly developing, testing, and proposing solutions to improve the data quality of the Bing team. Click here to read the full case study analysis.
audio annotation
Today, with enhanced machine learning capabilities, almost any audio recorded on a digital platform can be recognized regardless of its format. Therefore, audio labeling, speech data transcription and time stamping become possible for enterprises. Audio annotation also includes transcription of specific voices and intonations, and identification of language, dialect, and speaker demographics. The use cases for audio annotation vary, and some use cases require a very specific approach. For example: in security and hotline technical applications, flagging aggressive voice indicators and non-speech sounds like breaking glass, useful in emergency situations. Giving more context to the noise and sounds in a conversation or event can make it easier for people to fully understand the situation.
Dialpad & Appen: audio transcription and classification optimization
Dialpad is all about improving conversations with data. They collect phone call audio, transcribe those conversations with an in-house speech recognition model, and use natural language processing algorithms to understand each conversation. To make every sales call a success, they leverage this one-on-one conversation to identify what each rep (and the company as a whole) is doing well and not doing well. After six months of working with Appen's competitor, Dialpad found that the model was struggling to reach the accuracy threshold needed to succeed. After only a few weeks of working with Appen, Dialpad was able to successfully create the transcription and NLP training data needed for the model. Today, Dialpad's transcription model leverages the Appen platform for audio transcription and classification, as well as in-house transcription validation and model output.
image annotation
In the digital age, image annotation can be regarded as one of the most important functions of computers, as this can explain the world through a visual lens or new and enlightening perspective. Image annotation is critical in a wide range of applications, including computer vision, robotic vision, facial recognition, and solutions that rely on machine learning to interpret images. To train these solutions, images must be assigned metadata in the form of identifiers, titles, or keywords. From computer vision systems used by self-driving vehicles and machines that pick and sort products, to medical applications that automatically identify medical conditions, there are many use cases that require large numbers of annotated images. By efficiently training these systems, image annotation can improve precision and accuracy.
Adobe Stock & Appen: Mass Image Marking
Adobe Stock, a flagship product of Adobe, is a curated collection of high-quality images. The library itself is staggering in size: over 200 million pieces of data (including 15 million videos, 35 million vectors, 12 million editable pieces, and 140 million photos, illustrations, templates, and 3D data). Although it sounds like an impossible task, it is very important that these two hundred million files can be searched correctly. Faced with this dilemma, Adobe needed a quick and effective solution. Appen provides extremely accurate training data to create a model that can distinguish these subtle attributes in a library of more than 100 million images, with hundreds of thousands of new images uploaded every day. This training data helps Adobe deliver the most valuable images to its vast customer base. Users can quickly find the most useful images without having to scroll through pages of similar images, freeing up time to create powerful marketing materials. Through the collaborative machine learning practice of humans and machines, Abode benefits from more efficient, powerful and useful models that clients can rely on. Click to read Adobe Stock's image marking case study .
video annotation
Manually annotated data is critical to the success of machine learning. Humans are far better than computers at managing subjectivity, understanding intent, and dealing with ambiguity. For example, deciding whether a search engine result is relevant requires the input of many people to reach a consensus. When training computer vision or pattern recognition solutions, humans are required to identify and label specific data, such as circling all pixels in an image that contain trees or traffic signs. Using this structured data, machines can learn to recognize these relationships in test and production.
HERE Technologies & Appen: Refine Maps with Video Annotation
HERE's goal is to create three-dimensional maps accurate to a few centimeters, and HERE has been an innovator in this field since the mid-1980s. HERE has always been driven to provide detailed, precise and actionable location data and insights to hundreds of businesses and organizations, and that drive never wanted to change. HERE has an ambitious goal, which is to label ground truth data for tens of thousands of kilometers of driving roads and provide support for its signal detection model. However, achieving this goal by parsing video into images is simply impossible. Annotating individual video frames is not only time-consuming, but also tedious and expensive. Therefore, finding ways to fine-tune the performance of symbol detection algorithms becomes a top priority. Appen also began to provide solutions for HERE. Our machine learning-assisted video object tracking solution offers a great opportunity to realize this ambition. This is because we combine artificial intelligence with machine learning to greatly increase the speed of video annotation. After several months of implementing the solution, HERE believes that the solution will help improve the speed at which model training data is collected. HERE creates more videos of landmarks than ever before, giving researchers and developers the information they need to better fine-tune their maps.