Data Labeling Companies: 5 Reasons You Need Them

2023-11-24

In the age of large language models, data labeling companies are increasingly important. In fact, they play a crucial role during the preparation of the training data. Without them, the process can be unnecessarily tedious and costly.

But before we delve into their value, let’s clarify what we mean by data labeling.

What is data labeling?

Data labeling is the process of manually or semi-automatically processing and annotating raw data. The specific type of annotations will depend on the data, but they tend to consist of the following:

Labels
Categories
Attributions

When labelers annotate said data, they do so for a specific reason: to improve the training of an AI model. This improvement comes in two core ways: making it easier to train said AI model and improving the quality of its data and, thus, output.

In fact, it provides understandable and processable data. As a result, it can enable automated decision-making, classification, and recognition.

Data labeling is helpful for many AI models. From machine learning research to famous AI products like ChatGPT, Bard, and Claude. Below are some common data labeling application scenarios:

Applications	Computer Vision	Natural Language Processing	Speech Recognition	Data Mining & Recommender Systems
Scenarios	Image recognition, object detection, face recognition, image segmentation, etc.	Text classification, sentiment Analysis, Named entity recognition (NER), Machine translation, etc.	Speech-to-text (speech recognition), speech command recognition, voice interaction, etc.	User behavior analysis, personalized recommendations, targeted advertising, etc.
Data	Image dataset, including samples of different categories of images.	Text dataset, including annotated information on sentiment, entities, and relationships.	Speech recording dataset, including speech signals and corresponding text transcriptions.	User behavior dataset, including information on clicks, purchases, ratings, and more.

The data labeling market is growing fast because of new AI and machine learning tech. Experts think that by 2026, it will be worth billions of dollars. As more businesses use AI, data labeling becomes more important. But data labeling isn’t easy. Why? Because as its value increases, so does its complexity. The volumes of data needed are growing and are becoming more varied.

But the accuracy and efficiency needs don’t change. Data labelering companies must mix manual and automated labeling tactics to solve this problem. We also have to leverage modern labeling platforms and tools.

The 7 requirements data labeling companies have mastered

There are seven areas that data labelers must handle well to prepare data sets optimally.

1. Domain expertise

Labelers need to have domain-specific knowledge. For instance, understanding medical jargon is essential when dealing with medical data. Depending on the complexity of said data, it may need high-level experts.

2. Language proficiency

Text-based data labeling requires strong language skills. Labelers need to have good grammar, spelling, and semantic comprehension.

3. Data understanding

Every data set typically comes with its set of properties. For instance, video data does not have the same structure as text data. Financial data doesn’t have the same format as linguistic data.

Labelers need to learn the rules of each data set. That may include guidance documents or labeling specifications. Sometimes, said rules must be worked out from scratch. But either way, a deep understanding of the data is important.

4. Attention to detail

Data labeling requires high attention to detail to ensure accuracy and consistency. Labelers must be meticulous in handling data to avoid omissions or errors.

5. Learning ability

Labelers need to be open to learning new tools and platforms. Often, different data sets need to be labeled using different tools.

They must also stay updated on task changes and requirements and be prepared to adjust and learn accordingly.

6. Team collaboration

In big labeling projects, labelers work with others like team members and project managers. They need to be good communicators and teammates. This means they should know how to share ideas, plan together, and fix problems with the team.

7. Data confidentiality awareness

Data labeling often means handling data early in the app development stage. As such, labelers need to be very mindful of confidentiality rules in areas where information is sensitive. A leak can cause serious issues (lawsuits, fines, etc.).

The skills needed for data labeling can change based on the job and the type of work. That’s where data labeling companies come in. Good ones have a clear process put in place to ensure quality consistently.

The 5 technique categories used by data labeling companies

Data labeling companies deploy many (many!) techniques and tools, which we can categorize broadly by data type and tool reliance. The 5 categories are text, image, video, audio, and automated labeling techniques.

1. Text labeling techniques

The most common type of data is text data. For this type, the most common techniques are:

Named Entity Recognition (NER).
Part-of-speech tagging.
Syntax analysis.
Sentiment analysis.

We deploy NER to annotate entities (such as names of people, places, organizations, etc.) in the text. On the other hand, when we want to label grammatical categories of words, we rely on parts of speech tagging. For grammatical relationships between words in a sentence, we use syntax analysis. Finally, to annotate the sentiment polarity of the text, we rely on sentiment analysis.

There are many other methods. Click here for if you want to learn more.

Entity linking.

EL means identifying words that refer to specific entities and linking them to an identifier database. For example, relating Paris to France or Texas (yes, there is a Paris in Texas).

Relation extraction

It is identifying and labeling the relationships between entities in text. It can be used to identify many types of relationships. For instance:

Employment relations:
- Sentence: “Angela Merkel served as the Chancellor of Germany.”
- Extracted Relation: (Angela Merkel, served as, Chancellor of Germany)
Family relations:
- Sentence: “Elon Musk’s brother, Kimbal Musk, is also an entrepreneur.”
- Extracted Relation: (Elon Musk, brother of, Kimbal Musk)
Organizational affiliations:
- Sentence: “Susan Wojcicki is the CEO of YouTube.”
- Extracted Relation: (Susan Wojcicki, CEO of, YouTube)
Geographical locations:
- Sentence: “The Eiffel Tower is located in Paris.”
- Extracted Relation: (Eiffel Tower, located in, Paris)
Educational background:
- Sentence: “Stephen Hawking studied at the University of Cambridge.”
- Extracted Relation: (Stephen Hawking, studied at, University of Cambridge)
Product and producer:
- Sentence: “The iPhone was developed by Apple.”
- Extracted Relation: (iPhone, developed by, Apple)
Historical events:
- Sentence: “The Declaration of Independence was signed in 1776.”
- Extracted Relation: (Declaration of Independence, signed in, 1776).

Event extraction

Labeling text to identify events and their related components, such as the time, location, and participants. Here is an example:

“The World Economic Forum (WEF) will hold its annual meeting in Davos, Switzerland, from January 25 to 29, 2023.”

Extracted event details:

Event Type: Annual Meeting
Organizer: World Economic Forum (WEF)
Location: Davos, Switzerland
Start Date: January 25, 2023
End Date: January 29, 2023

Co-reference annotation

It determines when different words or phrases in a text refer to the same entity. For instance, if pronouns like “she” or “her” refer to the person mentioned before.

Textual entailment

It is labeling text to show if one piece of text logically follows from another. It’s useful when it comes to understanding language inference. If it’s confusing, check the example below:

Premise: “The astronaut fixed the satellite during a spacewalk.”

Hypothesis: “The satellite was repaired in space.”

In this example, we need to see if the hypothesis can be inferred from the premise. And in this case, it can be reasonably inferred. Thus, the entailment holds.

Textual entailment is crucial for tasks like question answering, summarization, and information extraction. Understanding the logical relationships between different pieces of text is important here.

Frame semantics

Labeling sentences to identify the semantic frames or concepts and their associated elements. Let’s look at a quick example:

“Jane bought a dress from the boutique for her birthday.”

Frame: Commercial Transaction

Elements:

Buyer: Jane
Item Purchased: Dress
Source: Boutique
Purpose: Her Birthday

In this sentence, we can identify the commercial frame. Jane is the buyer, the dress is the item purchased, the boutique is the source of the sale, and her birthday is the reason for the transaction.

Pragmatic Analysis

Labeling to understand a text’s intended message or implication, which may not be explicitly stated. For example:

“It’s getting pretty chilly in here, isn’t it?”

Analysis: The speaker might be implying that they want someone to close a window or turn up the heat, even though they didn’t directly say it.

2. Image labeling techniques

For images, data labeling companies can use the following techniques:

Bounding box labeling.

We use it to highlight regions of interest in a picture by marking it with rectangular boundaries as you can see on the right.

Segmentation labeling.

We use it to segment a given object from the image.

KeyPoint labeling.

As the name indicates, this one marks key points in an image. You can see an example on the right.

Keep in mind that these images are simple examples to give you a rough idea of what the technique looks like. In an actual project, there would be more granularity involved.

For more image labeling techniques, click here.

Polygonal segmentation

This method uses complex polygons to define the shape and location of objects. It’s a more precise annotation for non-rectangular forms.

Semantic segmentation

We assign each pixel a specific class (e.g., pedestrian, car, road). As such, each pixel carries semantic meaning.

3D cuboids

Another technique that is like bounding boxes, but with more information. As the name indicates, they highlight items with 3D representations. It allows for distinguishing features like volume and position in 3D space.

Lines and splines

Finally, we use this type of annotation using narrow lines and splines. It’s a standard method in data sets for autonomous vehicles. It’s used to highlight road lanes (single, broken, double, etc.).

3. Audio labeling techniques

For audio, common techniques include:

1. Speech recognition.

We used to transform this type of task into text labeling. To do so, we transcribe the audio into text and then treat it with any text techniques mentioned before.

2. Speaker recognition.

We use it to identify different speakers’ identities in the audio.

3. Emotion recognition.

We use it to annotate the emotional state of the speakers in the audio.

4. Video labeling techniques

For video data labeling, data labeling companies use the following:

Action recognition

As the name indicates, we use this technique to label human actions in the video. For example, you can imagine a clip of a basketball game where each player’s actions are labeled (jumping, throwing, dribbling, etc.).

Object tracking.

We use it to label the trajectory of target objects in the video. This technique is important for surveillance videos or sports analytics.

Let’s think of a common example—traffic. In this context, labelers may label the path of a specific car, tracking it frame by frame.

Event recognition.

Finally, we use this technique to annotate specific events in the video. For instance, labelers may label events like lion hunting or elephant bathing in a wildlife documentary. This technique goes beyond recognition. It is about understanding the significance of these actions as distinct events within the natural setting.

5. Automated labeling techniques

Unsupervised learning

This approach involves algorithms that learn patterns from unlabeled data. It helps discover hidden structures and inform future labeling tasks. Let’s think of a basic example.

Imagine a company that wants to segment its customers based on their purchasing behavior but doesn’t have pre-labeled data. They could use an algorithm like K-means clustering. As a result, customers could be grouped into distinct groups based on their buying habits.

Semi-supervised learning.

This technique combines a small amount of labeled data with lots of unlabeled data during training. The idea is to leverage the labeled data to guide the learning process and then apply the learned patterns to the unlabeled data. This approach is advantageous when getting labeled data is too expensive or time-consuming, but there is plenty of unlabeled data.

Active learning.

This technique is iterative. When the algorithm fails to label a data point, it can ask a user to help. This technique shines most when we need to minimize the labeled instances. In other words, when labeling is costly. The algorithm focuses on querying the most informative or ambiguous examples to learn efficiently.

For example, a prominent legal firm must classify thousands of long legal documents. But, they would rather not waste their talented staff’s time on this tedious task. Using this technique, they can use the model to classify the documents and then involve staff only for uncertain cases. This way, the model quickly improves with minimal human intervention.

Transfer learning.

This one requires starting with a pre-trained model. The goal is to fine-tune the base model to handle more specialized tasks. This technique is great if you have a decent base model but need more data for the target task.

5 reasons LSPs are the perfect data labeling companies

Granted, we may have a bias, but as you’ll see, language service providers have honed into a set of resources and processes ideally suited for data labeling.

1. Professional language knowledge

Obviously, LSPs have specialized linguistic knowledge. It allows them to handle textual labeling with high accuracy across various languages. It sets them apart from traditional data labeling companies that may have data expertise but lack it in other areas.

LSPs are equipped to manage data in many languages, including less common ones. Smaller LSPs handle several languages, while larger ones can work with several dozens. For example, our company often works with 80 languages.

2. High-Quality labeling outcomes

LSPs adhere to strict quality control standards, often backed by ISO certifications like ISO 9001. They follow precise labeling standards and guidelines, ensuring accurate and consistent results. Their broad range of services and expertise across many fields allows them to access a vast pool of subject matter experts, making them ideal for labeling diverse data sets. In our case, we have access to 2,000+ talented individuals from various backgrounds.

3. Rapid response and flexibility

LSPs are famous for their excellent project management and ability to optimize their use of resources. It means they can quickly meet the needs of their clients and adapt to different types of work.

In fact, translation projects tend to have a lot of variety. They can be simple or complex, small or large. As such, big LSPs are ready to easily manage small tasks and big projects. Their workflows are built to deal with volumes of all sizes, ensuring they can always provide the help their clients need.

4. Data confidentiality and security

Non-disclosure agreements are a standard practice in the industry. Most LSPs are very careful with their own data and their clients’ data. They follow strict rules to make sure everything stays confidential. For example, we strictly follow the ISO 27001 Information Security Standard.

5. Comprehensive services

Furthermore, unlike traditional data labeling companies, LSPs provide flexible and comprehensive services. They can help with all the modalities discussed: text, images, audio, and video. They handle various tasks and modalities (transcription, video production, copywriting, translation, annotations, etc.).

An extra advantage of their suite of services is the corpus of data they maintain. By working with such a diverse pool of clients in so many tasks, most sizeable LSPs maintain a usable and valuable corpus of labeled data.

With the swift evolution of AI technology, data labeling has become a pivotal factor in developing high-quality and accurate AI models. Its importance is magnified by the growing demand for AI applications across diverse sectors, leading to an ever-increasing need for precise and reliable data labeling.

As data labeling branches into various areas like image, text, speech recognition, NLP, and machine translation, the importance of choosing the right data labeling partner becomes paramount. The future of AI heavily relies on the quality of data labeling, making it crucial for companies to invest time and effort in selecting a data labeling provider that aligns with their specific needs and quality standards.

The rapid growth and application of AI are set to further amplify the demand for skilled data labeling, opening up numerous opportunities in the industry. This expansion not only promises more job opportunities but also drives continuous progress and innovation in AI technology.

In this dynamic landscape, taking the time to thoroughly research and select the right data labeling company is vital. It’s a decision that can significantly impact the effectiveness and accuracy of your AI models. If you’re unsure about where to start or what to look for, don’t hesitate to reach out. We’re here to guide you through this critical process, ensuring that you make a well-informed decision that will benefit your AI initiatives in the long run.

FAQ

What tools do data labeling companies use?

There are many tools that data labeling companies can rely on. For instance, Labelbox, Supervisely, Dataturks, Annotator, Brat, Amazon Mechanical Turk (MT Turk), Scale AI, Prodi.gy, V7 Darwin, Figure Eight, Label Studio, Clarifai.

Of course, these tools provide an incredible range of functionalities. From ML workflows to data visualization. As for which one is used and when, that will largely depend on the data, the project, and the company.

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