What is data labeling? – Short term explanation

Data labeling or data tagging in machine learning is a process to identify raw data and add a single or more than one informative and meaningful label to offer context so that an ML model can better learn from it. It is used to train the ML model so that the computing system can give out accurate information to use in business decision-making and analytics. Data labeling is needed for many use cases, including natural language processing, computer vision, and speech recognition.

What is data labeling used for?

Many computer applications use data labeling. It is required for speech recognition, NLP, and computer vision. Even though it is mainly used in three applications, data labeling can be used in small applications developed for consumer products and corporate analytics.

When it comes to computer vision, data labeling can algorithms to identify all items within a photo. Users will type a text for an image search and data labeling will enable algorithms to identify the elements of an image to get relevant results. Labeling used in computer vision pinpoints items in images.

Words or elements of phrases tagged in NLP can help algorithms to identify nuances in the manner humans communicate. Labels when assigned to text will enable NLP algorithms to identify special characters and use the same phrases and colloquialisms as humans with certain accents or dialects. Organizations use labels for working with chatbots, spam detection, and virtual assistance.

Products that work with speech input and output to perform a certain action or transform it to text will need speech recognition. Transcribing applications use data labeling to understand video input and output or take the user’s speech input on a home automation system and take an action depending on the user input.

Data labeling service for machine learning

Artificial intelligence (AI) is a field that is becoming more and more important in our lives. Whether it concerns speech recognition on our smartphones or autonomous driving and parking systems – the technologies are varied and they keep on evolving. In order to do that, however, data labeling is vital. Systems need to understand what is shown on a photograph, said in a voice recording, or written in a text, among many other things. By labeling all this data, machines can improve their learning and AI keeps evolving.


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Have training data created and labeled from a single source:

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What are the benefits of data labeling?

Data labeling comes with many advantages. Let’s take a closer look at them-

  • More Accurate Predictions: Proper data labeling will improve quality assurance within machine learning techniques, enabling the network to train and offer the desired output. Data, which has been labeled properly will serve as the ground truth to test and iterate future models.
  • Increases Data Usability: Data labeling helps in improving data usability within a model. For instance, to make a variable convenient for a model, you can reclassify it as a binary variable. This data aggregation can optimize the model by reducing the number of model parameters or enabling the inclusion of control variables.
  • Increases In-House Efficiency: By leveraging labels from within the organization or using services from reliable vendors who know the best ways to get the job done.
  • Improve Customer Service: Data labeling will help you learn which customers are experiencing problems and where they are located. It will also help customer service executives to provide better support and resolve problems more efficiently.

What are the challenges of data labeling?

Now let’s understand the roots of data labeling challenges. It is the first step to solving them and improving the artificial intelligence project success rates.

  1. Workforce Management

Successful data labeling can be a workforce challenge for two different reasons-

  • The need to handle enough workers for processing the large volume of unstructured data
  • The need to ascertain high quality across a big workforce

Even though data labeling is a high-volume task, quality is as important as quantity. Organizations have to perform a tricky balancing act between their expanding workforce quickly and managing and training, such a disparate and large group.

  1. Handling Consistent Data Quality

It is obvious that good data depends on higher dataset quality, but it comes with its own challenges. Organizations need to look for ways to ensure that labelers have the ability to create consistent dataset quality.

There are two kinds of dataset quality-

  • Subjective: It is concerned with how to define the label in cases where there is not one source of truth. The labeler’s domain language, expertise, cultural associations, and geography can influence the way they interpret data.
  • Objective: It does have one answer but is still challenging. For instance, there is a risk that the data labeler might not have the required domain expertise to answer the question correctly.

Finally, it is almost impossible to eliminate human error, regardless of how good the dataset quality verification system is.

  1. Monitoring Financial Cost

Many organizations struggle to budget correctly for labeling in the absence of any established metric and standard pricing. And 26% of the organization cited a lack of budget as a reason behind their projects failing. Without responsible monitoring, metrics, and objective standards for the success of data labeling, organizations are limited in their capability to track outcomes in relation to time spent on any work.

Organizations outsourcing data labeling need to choose between paying for data labeling per task or per hour. Paying per task can be more affordable. However, it incentivizes rushed work since labelers try getting more tasks done within a given time.

In-house manual data labeling professionals are expensive due to the training and time required to reach true expertise. As the data scales, prices are growing, too and it is impossible to predict the ultimate volume of data for processing.

What are the Data Labeling Approaches?

Data labeling is important to develop a high-performance machine learning model. Even though data labeling appears to be simple, it might not be easy to implement. Thus, companies need to consider different factors and methods to decide on the best approach to data labeling. As every data labeling method has its own pros and cons, a comprehensive assessment of task complexity along with the scope, size, and duration of the project is recommended.

Check out the paths to label your data:

  • Internal Labeling: To simplify tracking, offer greater accuracy, and improve quality, use in-house data science. Nevertheless, the approach needs more time and favors big companies with more resources.
  • Programmatic Labeling: The automated data labeling processes use scripts to truncate time consumption and the requirement for human annotation. Nevertheless, the possibility of technical issues needs HITL to be a part of the quality assurance process.
  • Synthetic Labeling: It is an approach, which generates new project data from already existing datasets that can improve time efficiency and data quality. Nevertheless, labeling needs extensive computing power that can raise the pricing.
  • Crowdsourcing: The approach is faster and more affordable because of its web-based distribution and micro-tasking capability. Nevertheless, worker quality, project management, and QA vary with crowdsourcing platforms.
  • Outsourcing: It is an optimal choice for temporary quality-level projects but creating and managing workflow that is freelance-oriented can be time-consuming. Even though freelancing platforms offer comprehensive candidate details to make the vetting process easier, hiring managed data labeling professionals offers pre-vetted staff, and pre-developed data labeling tools.

Gathering metadata: Humans or machines?

Artificial intelligence has come a long way since the first developments in the field. Today, software can perform tasks that were unthinkable just a few decades ago. But the quality of AI still depends on human input that helps the systems learn. The algorithms can only function properly if there is some sort of human interaction. By learning from people, machines can develop ways of providing human-like results. This is why it is so important to provide data labeling to software developers. Every bit of data gives the system a better understanding of how we see, hear, or define things. The quality of data that is achieved through human input is greatly superior to what a machine would be able to develop on its own.

How does data labeling work?

Machine learning (ML) depends on a labeled set of data that the algorithm can learn from. This dataset is gathered by giving the unlabeled data to humans and asking them to make certain judgments about them. For example, the question might be: “Does this photo contain a car?” The labeler then looks at each photo and determines whether a car can be seen. Of course, there are differences in how detailed the tagging is. It can simply be a yes or no to the question. It could also require identifying the specific pixels in the photo that show a car.

Once this data has been labeled, the machine can use this information to understand the underlying patterns. Thus, the machine learns to make predictions on new images based on the AI training data. The accuracy of the algorithm depends on the accuracy of this training data. Therefore, it is vital that high-quality data is gathered and labeled that the machine can learn from.

What types of data labeling are there?

There are a number of different types of data labeling. The following are some of the most common:

  • Natural language processing: Natural language processing (NLP) is used to analyze texts. For example, labelers can identify the intent or sentiment of a given text, classify places, people, as well as other proper nouns, or identify parts of speech. NLP can also be used to identify text in PDFs or images. This process requires labelers to identify sections of text, e.g. by drawing bounding boxes around it, and then tagging the text with specific labels or transcribing it. NLP is used for entity name and optical character recognition as well as sentiment analysis.

  • Computer vision: Computer vision is required to teach a machine to recognize images or specific features in them. In order to do that, images or pixels need to be labeled. This can be done by classifying images by type or content. Labelers can also segment images in a much more detailed way at the pixel level. With the help of this training data, machines can learn to automatically categorize images or identify key points in them. They can also learn to segment images automatically.

  • Audio processing: Audio processing is used to convert sound – e.g. speech or building sounds such as alarms – into a structured format. Once this processing has been completed, this becomes the audio training dataset. Audio processing is done by manually transcribing the sounds into written text. Furthermore, tags can be added to specify more information about the sound.

Data quality and accuracy in data labeling

Datasets for machine learning need to be accurate and high quality. The terms accuracy and quality are often used interchangeably, however there is a difference between the two:

  • Accuracy describes how consistent the labeling of each piece of data is with the real world, i.e. how close it is to the so-called “ground truth”
  • Quality measures the accuracy across the entire dataset. This includes whether the work of all labelers looks the same and if the labeling is consistent across the datasets.

Creating and validating machine learning models requires reliable data – both during model training and when the model learns from the labeled data to inform future decisions.

What affects quality and accuracy in data labeling?

There are a number of potential issues that can affect the quality and accuracy of your labeled data:

  • No knowledge or context:
    If the labelers do not have context for the data they are labeling, this affects the overall quality. For example, the word “bank” can refer to a financial institution or the shallow area in a body of water. In order to tag this correctly, the labeler needs to know if the text is about finance or natural geography. Therefore, labelers should understand key details about what the business or product does for which they are labeling data.

  • Flexibility:
    Machine learning takes many rounds of testing and tuning. This means that new datasets will need to be prepared or existing ones need to be adjusted. Labelers therefore have to be able to react to changes, e.g. more data, higher complexity of the tasks or a longer duration. A flexible team of data labelers will provide higher quality data.

  • Relationship and communication:
    In addition to having a labeling team that can react to changes, it is also important that the communication between the client and the labeling team works. Ideally, there is a closed feedback loop that allows for changes to quickly be incorporated into the datasets. This usually works best when there is a leader on the labeling team that has a direct connection to the client to discuss and implement changes.

How can the quality of labeled data be measured?

There are several different ways that can be used to measure the quality of data labeling:

  • Sample review: An experienced labeler – e.g. project managers or the team leader – reviews a random sample of completed tasks for accuracy.
  • Gold standard: When there is a correct answer for a task, the number of correct and incorrect tasks determines the overall quality of the dataset.
  • Consensus: A number of people perform the same task. Whichever answer comes back from the majority of labelers is the correct one.
  • Intersection over union (IoU): This combines results from humans and machines by comparing results of hand-labeled data (the so-called ground truth) with the algorithm’s results. This is often used for bounding boxes within images.

What are the reasons for outsource data Labeling?

Scaling and reducing overhead costs for organizations will become easier by outsourcing data labeling services. When organizations outsource, they can focus on the core and important tasks. It helps in saving money without compromising on quality. As businesses outsource data labeling services, they can communicate and trust a professional provider. They can evaluate a shortlist of providers for finding the best one for their requirements.

How to find the right data labeling services for your requirements?

When you look for data labeling services, it is crucial to look for an organization that provides customized workflows created to adapt to your certain requirements. The organizations should offer an easier way to upload the labeling and data instructions. Furthermore, it helps in finding a data labeling service, which employs experts in data labeling to get the optimum results.

Microjobs – keeping data labeling service interesting

How can data labeling be achieved in a quick and efficient manner that still allows the people involved to enjoy what they are doing? At clickworker, we offer lots of microjobs that can be taken up by the thousands of Clickworkers around the world. Any Clickworker can choose which tasks to work on and thus find the jobs that interest them the most or work on a variety of different tasks. This keeps the work interesting and exciting.

There are, of course, some specifications regarding who can perform each of the microjobs. Some of them only require the Clickworker to speak a particular native language or come from a specific region. In some cases, however, a more detailed know-how of the individual field is necessary. With every task, we create a profile based on what is needed by the customer and offer the jobs to all Clickworkers that fit this profile.

Data labeling service by clickworker

A data labeling service comprises many different tasks. This includes, for example, putting electronic markings on image files (e.g. bounding boxes), placing marks on significant areas on pictures of faces, tagging pictures with relevant keywords, or rewording texts with regard to the word order or the chosen person perspective.

Bounding Boxes

Bounding Boxes

Image Segmentation

Image Segmentation

Tagging of image elements

Tagging of image elements

Face marking with points

Face marking with points

Another important facet of data labeling service is categorizing texts, audio files, or videos according to their content.
This so-called sentiment analysis lets your system know what customers feel and mean when they are getting in touch with you.

Bounding boxes, tagging, etc. – data labeling services for images

As mentioned above, putting markings on images is an important part in data labeling service. This can take different forms. Bounding boxes, for example, are used to mark recurring elements in one image, such as multiple vehicles (see image). This allows the algorithm to recognize different shapes in various positions and sizes as belonging to the same category (vehicle). It is also possible to tag the elements and thus teach AI what is shown in each image. If the goal is to classify different parts of an image, segmentation can be useful. In this case, labels are applied to every part of the image. Every part that has the same label is then represented in the same way which makes it easier to be analyzed.

To improve facial recognition software, face markings can be used. Points are placed to indicate the shape of the face, the lips, eyebrows, and more. By learning from these markings, algorithms can more easily identify faces, even if they are shown from different perspectives or if the entire face is not visible.

Text and sentiment analysis: Teaching machines what we mean

Understanding text can be difficult for AI. Natural language is unlike constructed or formal language and can therefore not easily be parsed by machines. People use repetitions, idioms, or tropes such as irony, often without conscious planning. It takes human understanding of this language to allow machines to learn from it. One way to achieve this is text mining or text analysis: During this process, natural language is structured to help AI work out the meaning.

One type of text analysis is sentiment analysis. This lets machines learn what people mean when they say or write something. Simply knowing the words used is – in most cases – not enough to understand the meaning. For a spoken utterance, for example, tone needs to be taken into account. Multiple variables can be used to determine whether the sentiment is positive or negative or, even more advanced, whether it can be ascribed to a specific emotion such as “happy,” “sad,” or “angry.”

Would you like to find out more about our data labeling service?
Contact our sales team and let us know what you need in order to improve your algorithm. We have great solutions for you to help you improve your AI.


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