1. Description
In deep learning, data sets and data set labeling are essential links in AI projects, and it is necessary for ordinary developers to understand this series of processes systematically. For the above developers, this article will specifically explain the problem of data annotation to them.
2. Types of data tags
2.1 Computer Vision
Developing and labeling high-quality data makes it easier for computer vision models to process images and extract relevant information. Models can be trained to organize images based on factors such as pixel size, color, or theme. With this kind of data, machine learning algorithms can recognize faces , detect objects, classify images and otherwise analyze digital images.
2.2 Natural Language Processing
To help natural language processing models find and process textual information, the data can be labeled by labeling the entire file or by labeling specific parts of the text with bounding boxes. Models can leverage this labeled data to perform sentiment analysis , pinpoint proper nouns, and extract text from images, among other functions.
2.3 Audio Processing
Audio processing involves taking specific sounds or background noises and converting this information into data that machine learning models can study and learn from. After converting audio to written text, tags can be applied to label the data. In addition to being able to pick out certain voices, machine learning models can use this data to detect the sound of individual voices and even determine the speaker's emotion.
3. Data labeling use cases
3.1 Self-driving cars
Rely on object detection to sense whether cars, pedestrians, animals, and other non-vehicular objects are in front of or around you while driving.
3.2 Conversational Chatbot
Many chatbots are trained with NLP models to maintain online text conversations with customers. They might look for specific keywords or phrases to understand a customer's problem and resolve it quickly.
3.3 Advanced Agriculture
Farmers can use machine learning models to spot nuisances like pests and weeds, while autonomous tractors trained on labeled data can pick out healthy produce while avoiding damaged or rotting produce.
3.4 File Organization
NLP Models developed AI and machine learning models to classify files and documents, eliminating the need for workers to manually classify online and physical documents.
3.5 Retail Experience
Object recognition enables cashierless checkouts, processing item prices as customers scan items. Computer vision can monitor shelves and report when items are out of stock or when products need to be replaced.
3.6 Measuring Customer Satisfaction
After being trained on large amounts of labeled data, machine learning models can perform sentiment analysis in real time to gauge customer satisfaction levels during phone calls, look for specific words and sense a speaker's tone of voice to determine their mood .
3.7 Disease detection
Radiologists can use labeled data to train machines to recognize signs of disease during MRI, CT and X-ray scans. Based on the scans and their preprogrammed knowledge, the machine learning model can accurately predict whether a patient contains signs of disease.
3.8 Virtual Assistants
Virtual assistants like Amazon's Alexa and Apple's Siri also rely on labeled data in the form of human conversation, which is fed into their algorithms. These assistants can learn from this data, not only to understand requests and statements, but also how to apply the correct tone of voice and voice inflection when providing spoken responses.
4. Data labeling method
Because data labeling is critical to developing good machine learning models, companies and developers place a high value on it. However, data labeling can be time-consuming, so some companies may use tools or services to outsource or automate the process.
We can label data using various methods; the decision between these methods depends on the size of the data, the scope of the project, and the time required to complete the project. One way to classify the different labeling methods is whether a human or a computer does the labeling. If humans are doing labelling, it can take one of three forms.
4.1 Internal Labels
This approach is used in large companies with many expert data scientists who can work on labeling data. In-house labeling is safer and more accurate than outsourcing because it is done in-house without sending data to external contractors or suppliers. This approach protects your data from disclosure or misuse if the outsourced agent is unreliable.
4.2 Outsourcing
For large, advanced projects that require more resources than your company has spare, this option may be the way to go. That said, it requires managing freelance workflows, which can be expensive and time-consuming, as companies hire different teams to work in parallel to meet deadlines. To maintain workflow and quality, all teams need to use a similar approach when delivering results. Otherwise, more effort is required to get the results into the same format.
4.3 Crowdsourcing
In this approach, companies or developers use services to quickly label data at low cost. One of the most famous crowdsourcing platforms is reCAPTCHA, which basically generates CAPTCHAs and asks users to label the data. The program then compares results from different users and generates labeled data.
However, if we want to automate labeling and use a computer to do it, we can use one of two methods.
4.4 Synthetic tags
In this approach, we use raw data to generate synthetic data to improve the quality of the labeling process. While this approach leads to better results than programmatic markup, it requires a lot of computing power because you need more features to generate more data. This method is a good choice if the company has access to a supercomputer or a computer that can process and generate large amounts of data in a reasonable amount of time.
4.5 Programmatic tags
To save computing power, this method uses a script to perform the labeling process instead of generating more data. However, programmatic labeling usually requires some human annotation to guarantee the quality of the labels.
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5. Advantages of data labeling
Data labeling enables users, teams, and companies to better understand data and its uses. Primarily, data labeling provides a way to provide more precise predictions and improve data usability.
5.1 More precise predictions
Accurate data labeling ensures better quality assurance in machine learning algorithms than using unlabeled data. This means your model will train with higher quality data and produce the expected output. Correctly labeled data provides the ground truth (i.e. how labels reflect the real scene) for testing and iterating subsequent models.
5.2 Better Data Availability
Data labeling can also improve the usability of data variables in the model. For example, a categorical variable can be reclassified as a binary variable to make it easier for the model to use. Aggregating data can optimize the model by reducing the number of model variables or enabling the inclusion of control variables. Whether you're using data to build computer vision or NLP models , using high-quality data should be your top priority.
6. Disadvantages of data labeling
Data labeling is expensive, time-consuming and prone to human error.
6.1 Expensive and time-consuming
While data labeling is crucial for machine learning models, it can be costly from a resource and time perspective. Suppose a business takes a more automated approach. In this case, the engineering team still needs to set up the data pipeline before data processing . Manual labeling is almost always expensive and time-consuming.
6.2 Prone to human error
These labeling methods are also susceptible to human errors (e.g., coding errors, manual entry errors ), which can reduce data quality. Even small errors can lead to inaccurate data processing and modeling. Quality assurance checks are critical to maintaining data quality.
7. Best practices for data labeling
No matter which labeling method you choose for your data labeling project, there is a set of best practices that can improve the accuracy and efficiency of your data labeling process. For example, we build machine learning models using large amounts of high-quality training data, which is expensive and time-consuming. To develop better training data, we can use one or more of the following methods:
- Labeler consensus helps to counteract individual labeler errors and unconscious biases . Errors may include labeling errors or duplication of labeling data. Furthermore, one of the challenges of machine learning is when the data does not fully represent all possible potential labels, resulting in bias in the training data itself.
- Label audits keep labels updated and ensure their accuracy. Typically, when building a machine learning database, it is regularly updated with new data that needs to be labeled before we can store and use it. Auditing data ensures that new data is properly labeled and old data is relabeled to be consistent with these new labels.
- Active learning uses another machine learning approach to decide which small amounts of data need to be labeled or inspected by human labelers. In active learning, a human labeler first labels a small amount of data and then uses these labels to train a model on how to label future data.
8. Examples of data labeling tools
You can use many online tools and packages to label data using any of the methods we mentioned above.
- LabelMe is an open-source online tool that helps users build image databases for computer vision applications and research.
- Sloth is a free tool for tagging image and video files. One of its well-known use cases is facial recognition.
- Bella is a tool for labeling textual data.
- Tagtog is a startup that provides a web tool of the same name for automatic text classification.
- Praat is a free software for tagging audio files.