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.
clickworker offers many services in the area of data sets for AI & ML. Image Annotation Services Datasets for Machine Learning
Have training data created and labeled from a single source:
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.
- 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.
- 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.
- 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:
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
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
- 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.
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
- 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
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.
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
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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