What is the purpose of tagging data?

What is the purpose of tagging data? When we get to interact with modern speech, text, image, and video recognition devices, we don't always think about the way such devices can accomplish what they are doing. A few years ago, the capabilities of computers were insufficient for this kind of technology. However, now every year the possibilities of such devices are growing, and we never cease to be amazed by their superpowers.

All this has become possible thanks to artificial intelligence (AI) and machine learning (ML), which operate thanks to tagging. Further on, we will take a closer look at these terms and explore why we need to add a correct tag to each data point.

Tagging of data for machine learning

Most of the time, whenever anyone hears about artificial intelligence and machine learning, they imagine human-like androids from movies whose intellectual capabilities are comparable or even superior to those of human beings.

Other people believe these machines simply consume information and learn from it on their own. In reality, computer systems have limited capabilities without human help, and tagging is necessary to make them "intellectual".

Data tagging (sometimes called annotation or labeling) refers to the process of adding tags to raw data to indicate to a machine learning model the target responses it needs to predict. A large amount of this tagged info constitutes a dataset.

The success of machine learning is directly related to the amount of raw data: the more data there is, the better the AI will perform. This kind of machine learning approach is called supervised learning, and later in this post we will be referring to this particular kind of ML.

Common data tagging challenges

Most of the time, specialists employ dedicated software intended to simplify and speed up labeling such raw data as video, audio, text, or images. Although it is not a particularly mentally challenging task, it is still a serious challenge. The people involved in assigning tags have to be extremely diligent since each error or omission adversely affects the quality of the dataset and the overall performance of the predicting model. In general, it is a meticulous job that comes with a number of challenges.

Experts involvement

You may need to hire experts from certain fields in order to add tags. This may, for instance, increase the cost of the project, or there might simply not be enough experts available at the moment.

High costs in time and labor

To develop a trained model, data preparation and tagging may take up to 80% of the total time of a whole project. The challenge is not only to get enough data. It is also a time-consuming job requiring the labor of human annotators to manually add tags to each data item. Among other things, quality checks of a tagged dataset may be very time-consuming and increase the time to prepare a dataset.

Error risks

No matter how skilled or attentive a specialist is, manual tagging is susceptible to human error. This is unavoidable since annotators tend to handle massive amounts of raw data. Also, cross-labeling may result in two or more specialists disagreeing about certain tags. Since people have different levels of experience, the tagging criteria and the tags themselves may be uncoordinated, which can also entail the risk of errors.

Approaches to these challenges

Nevertheless, in spite of these shortcomings, tagging is still the foundation of the majority of machine learning projects. Different techniques for it are available. The choice of strategy varies depending on the complexity of the objective, volumes, the size of the team, and the financial resources and time available. Here are some ways in which tagging can be performed.

Outsourcing to individuals

Looking for freelance employees at one of the many recruiting and freelancing sites is another approach to accelerate the tagging process. To make sure applicants will do the job right, you may examine their skills through tests. However, you might need to involve your own employees to manage these people and the process itself.

Outsourcing to companies

Outsourcing companies that focus on preparing data for training are an option as well. Such organizations emphasize that their professional team delivers high-quality training data. This allows the client's employees to avoid the need to manage the labeling, and instead be able to concentrate on more complex tasks.

Crowdsourcing

Crowdsourcing platforms deliver services of a workforce on a made-to-order basis. Clients sign up on it as contractors and create their projects. Thanks to the vast community of contributors on the platforms, it can take just a couple of days or weeks to gather data or tag thousands of images instead of months or even years. Crowdsourcing platforms usually provide quality control tools and means to automate labeling pipelines, which helps improve the quality of labeled datasets.

Data tagging process

Regardless of what the subject matter is and whatever the approach, the tagging process is most often done in the following order:

1. Data gathering and preparing it for tagging

All projects start with data acquisition. Generally, in an effort to produce a more refined model output, a large and diverse body of data is required. The required raw dataset may be retrieved from a variety of sources. Companies may spend years collecting information on some projects, while others involve external information and publicly accessible sets.

Before tagging can be done, it is important to refine and pre-process the data, as it is often corrupted or inappropriate for the particular case.

2. Data tagging

This stage involves the tagging itself, where specialists evaluate the data and attach tags to it. For example, it may be tags describing objects in images or handwritten text tags that assist the model in understanding a handwritten text.

3. Quality assurance

Despite requiring more effort, this essential process should be given as much attention as the tagging phase itself, as it strongly influences the accuracy of the results. The dataset has to be of the highest possible quality, reliable, accurate, balanced, consistent, and oftentimes even domain-specific.

Quality in ML model training is determined by how accurately tags are appended to a particular training set. Continuous quality assurance (QA) checks must be put in place to guarantee the precision of the tags and to optimize them.

It is also crucial to define what kind of data is required for model training and make sure that all cases (for instance, classes) are equally represented in tagged datasets. Large amounts of improperly selected and imbalanced tagged data is a common cause of poorly performing models.

The unbiased measure of any system's performance is the outcome. Machine learning makes no exception, so you can judge its performance by the number of false positives and false negatives. The fewer there are, the better the quality of the work.

False negatives occur when a tag is not applied to data that should have been tagged. This means that the machine learning algorithm will not recognize patterns in the data and may miss important information. For example, if we were tagging medical records for patients with a certain disease, a false negative would occur if a patient with the disease was not tagged as having it.

False positives occur when a tag is applied to something that should not have been tagged. This means that the ML algorithm will learn to recognize a pattern that does not exist, leading to incorrect predictions. For example, if we were tagging emails as spam or not spam, a false positive would occur if a legitimate email was tagged as spam and sent to the spam folder. A proper model only needs true positives in its training datasets.

These errors can have serious consequences in various fields, so it is important to minimize false positives and false negatives as much as possible. By using multiple taggers and having a human expert review the tags, we can increase the accuracy of tagging and improve the performance of machine learning algorithms.

4. Trained model testing

This process, which is a logical continuation of all the steps described above, commonly involves model testing on a validation set to confirm whether the model provides the anticipated predictions or estimates.

It is a common practice to perform such testing regularly, to make sure that a model doesn't drift, i.e. quality of its predictions doesn't diminish.

Types of data tagging

The most common and simplest approach to tagging is, of course, entirely manual processing. A specialist obtains a set of raw, untagged data and has to label it according to a series of rules. As previously described, standard or manual tagging is generally a very time-consuming and labor-intensive process. Moreover, the two costliest stages of it are the data tagging itself and its quality control checks.

For this reason, some consider another type of tagging, such as auto tagging, to be a solution to the problem of tedious and resource-demanding manual labeling. This is a relatively new process that is frequently discussed today. Unlike manual type which consumes many hours to complete one set of data, auto tagging presents a simpler, faster, and more contemporary way to handle the data using the AI itself.

Auto tagging refers to a feature of annotation tools that employ artificial intelligence to enrich or tag a dataset. Tools with this feature reinforce the work of humans and help save time and money. The technologies that make it possible are evolving at a rapid pace.

Auto text tagging is a process of assigning relevant tags to pieces of text automatically, without human intervention. But the tools for auto tagging could not have been created without human input. As a result of people's efforts, auto tagging software can operate without human assistance, but it is still human-generated, and it also requires pre-tagged datasets.

The first step in auto tagging model creation is to gather a large dataset of tagged content that can be used to train the algorithm. This dataset may come from a variety of sources, such as user-generated tags or pre-existing metadata. Once the dataset is compiled, the algorithm for auto tagging is fed the data and begins to learn the patterns and relationships between the content and the tags.

AI tagging tools on the basis of machine learning models enable a much smoother and more efficient object identification, handle a much larger number of images, automatically perform the majority of manual tasks, and can be further taught to interpret new information more accurately. So now let's move on to an overview of typical types of tagging.

AI text tagging

No matter how smart machines are, human language may at times be too complicated to decipher, even by the people themselves. Text tagging is the process of tagging a piece of text or its various elements.

Text tagging involves identifying sentence components or structures according to certain criteria in order to prepare datasets for training a model that will be able to "understand" human language, connotations, or emotions behind words effectively.

Text tagging programs simplify the process of tagging for projects related to Natural Language Processing (NLP), for instance, tone analysis, linking and recognition of named entities, text categorization, part-of-speech tagging, and so on.

NLP is a machine learning technology that empowers computers to interpret and understand human language. Modern NLP-based AI solutions involve voice assistants, machine translators, smart chatbots, and the list of systems continues to grow.

Automatic tagging of text through AI-ready solutions is also possible. One of the main advantages of text auto tagging is its ability to save time and resources. Instead of manually labeling each piece of text, which can be a time-consuming and tedious task, auto tagging of text allows for large amounts of data to be tagged quickly and accurately.

This is particularly useful in industries such as journalism, where news articles need to be categorized and tagged for easy retrieval and analysis.

A separate technology for extracting textual information from scanned documents or images into data is available. It is referred to as optical character recognition (OCR). This handy technology analyzes printed or handwritten text and turns it into an editable digital file.

OCR software is dedicated to simplifying the accessibility of information to end-users. They assist in business and workflow, saving time and resources that would be needed for data management. Once converted, OCR-processed text information may be utilized more easily and conveniently by businesses. The pros of OCR include no need for manual text entry, reduced errors, increased productivity, and so on.

OCR and NLP are the two main domains that strongly depend on machine learning, tagging text in particular. Achievements in both of these domains led to the rise of large language models (LLMs) which power AIs like OpenAI's ChatGPT and Google's Bard.

Tagged text datasets are typically created by linguistic professionals and are used both for linguistic research and machine learning. Since assigning tags to text is a rather laborious and time-consuming job that requires expertise, crowdsourcing is often employed to speed up the creation of datasets.

Finding so-called real-world text tags is another way to do this. For example, text reviews that have already been tagged may be utilized to train ML models to assess the tone of text reviews.

Types of text tagging

Entity tagging

It is a way of labeling unstructured sentences with essential information. This type may be summarized as locating, retrieving, and tagging entities in text in one of the following ways:

Named entity recognition (NER)

The best way to tag critical info in the text is via NER, which can be people's names, geographical locations, commonly encountered objects or characters.

Part-of-speech tagging (POS tagging)

It assists in sentence evaluation and recognition of grammatical units (nouns, verbs, adjectives, pronouns, adverbs, prepositions, conjunctions, etc.).

Keywords tagging

It can be described as searching for and tagging keywords or phrases in text data.

Named entity linking

While entity tagging assists in discovering and extracting entities from text, entity linking, also known as named entity linking (NEL), is the act of connecting these named entities to larger datasets.

Text classification

When dealing with large collections of documents, classification models are relevant. Text classification is one of the main tasks of computer linguistics.

Computer Linguistics is a field of knowledge, which is concerned with the computer simulation of natural language skills and solving applied problems of automatic processing of texts and speech. It resolves such issues as the ability to classify text content according to its subject, the authors of the text, emotional coloring of statements, etc.

Text classification refers to the assignment of each text fragment to a certain class with predefined parameters. Machine learning techniques are applied to solve these problems. In general, we may say that text classification is the process of assigning predefined categories or tags to sentences, paragraphs, text reports, or other unstructured text forms.

A text classifier is a powerful tool used to categorize and organize large volumes of text data. These are algorithms that classify text according to predefined categories or classes. Text classification is gaining more and more widespread adoption. It assists, for example, in recognizing spam, classifying SMS messages, etc.

A text classification model or a text classifier can be employed for:

• Sentiment analysis is a use case which means figuring out whether a text sample is a positive, negative, or neutral message.

• Topic categorization, which involves identifying the topic of a document or passage of text.

• Language categorization, which involves recognizing the language of text content.

The process of building a custom classifier involves training the algorithm on a set of tagged examples, where each example is a piece of text that has been manually classified into one of the predefined categories. The algorithm then uses techniques to learn patterns in the data and develop a model to classify new, unseen text.

AI video tagging

Video tagging is the act of adding tags to frames, that is, still images retrieved from recordings. It is basically the same as image tagging, except that there are thousands or even millions of frames in a single file.

There is a subset of artificial intelligence called computer vision (CV) allowing machines to "see". In other words, they can extract essential data from visual data like digital images, thus imitating human visual perception.

Annotation of videos entails tagging each object in the recordings. Doing so helps machines and computers recognize moving objects in a re frame by frame. A human carefully inspects the video, tags each frame-by-frame image, and compiles it into pre-determined datasets that are utilized to train machine-learning algorithms.

Just as with text data, auto tagging is also possible for videos. But, as mentioned earlier, it is not achievable without human input. With their help, experts have developed special auto tagging tools that can significantly reduce the time it takes to tag raw data.

It involves using a machine learning algorithm to analyze videos and generate tags based on the visual cues present in the video. These tags are then added to the file, for instance, making it easier for search engines to identify and categorize videos.

Types of video tagging

These are some of the types of tagging that are commonly employed by annotators.

2D bounding box

The 2D bounding box approach is arguably the most frequently employed tool for such tagging. With this technique, the annotators position rectangular frames around the objects of interest to identify, categorize, and tag them. Rectangular boxes are drawn manually around items on the footage when they are in motion.

Semantic segmentation

Semantic segmentation refers to another type of tagging that assists in training more advanced artificial intelligence models. Each pixel present in the image is assigned to a specific class as part of this method.

3D cuboid tagging

This type of tagging technique is employed to accurately represent objects in a three-dimensional space. The 3D bounding box approach is designed to indicate the length, width, and depth of an object in motion and analyze how it interacts with its surroundings.

Conclusion

Tagging involves adding labels or tags to data so that they can be easily categorized and analyzed by computers. It is essential for artificial intelligence and machine learning algorithms, as these systems rely on large amounts of structured data to learn and make predictions. The higher quality and the more data provided, the better the final trained model will be.

Tagging makes it possible to train AI and ML models. By providing labeled datasets, developers can teach their algorithms to recognize patterns and make predictions based on real-world examples.

This is particularly important for applications such as natural language processing and image recognition, where algorithms need to understand the nuances and complexities of human language and visual cues.

Tagging also allows machines to quickly and accurately analyze large amounts of data, which would be impossible for humans to do manually. This can lead to more accurate predictions and insights, which can be used to improve everything from business operations to healthcare outcomes. Auto tagging is also possible thanks to labeling.