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, the 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.
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How does data labeling work?
Machine learning 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 training data set. 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 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.
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
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 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?
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