A Comprehensive Guide to Creating Effective Labels for Text Annotation

Introduction

Text annotation is the process of adding metadata, labels or notes to unstructured text data, in order to provide context and meaning to the data. This extra information on the text helps in various NLP and ML tasks, such as sentiment analysis and text classification.

Annotated data can be used for a variety of different tasks, such as model training, testing, and improvements, as well as for refining different NLP algorithms to make them better and more effective.

For example, if you need to analyze your customer feedback, you’ll need to annotate a dataset of customer reviews with the labels you want to analyze for, in order to train a solution on that data. The trained solution will then be able to analyze new, unseen data.

In order to annotate text, you need to use annotation labels. Labels are used to identify the type of information that is contained in a piece of text.

Moreover, the quality and end result of your annotation will largely depend on the labels that you define for your annotation. Without good quality labels, a number of issues can arise in your annotation:

• Poorly labeled data can lead to incorrect predictions or inconsistent performance of your NLP solution.
• Good labels help your annotators be more clear and consistent in their work, which ensures that data is consistently labeled across the dataset. Consistency helps ML models learn patterns effectively.
• Effective labels are unambiguous and precise, and this makes it easier for annotators to understand the exact meaning and scope of each label, helping reduce errors and inconsistencies.
• Good labels help make the model’s predictions and results more interpretable and understandable. Some models, for example, zero-shot classification models, are quite sensitive to label names since the models rely a lot on what they learned during pre-training.
• Good annotation labels make the annotation process faster and more efficient because they reduce the effort spent on understanding the labeling criteria.
• Effective labels help get a more accurate evaluation of the performance of different models on the same task. This means that it’s easier to compare and benchmark different models, which is necessary for arriving at the best performing model, and improving your solutions.

It’s quite clear that creating good quality labels for annotation is quite necessary to be able to have a high quality NLP solution.

But how do you define high quality labels that are effective and clear? That’s what we will uncover in this article.

1. Define Your Text Annotation Objective

Having a clear objective for your text annotation project is very important since it directly affects the specific labels that are used in the annotation process. This is because with a clear objective, the labels will be then designed to better capture information relevant to the goals of the project.

When you have a clear objective for your project, it becomes easier to know what type of information needs to be extracted from the text, which will then guide the choice of labels much better.

Here are a few reasons why:

Relevance

When the objective is clear, it becomes easy to know which labels are relevant the project’s goals and which ones are irrelevant. This way you can exclude the irrelevant labels and make the annotation process more focused and efficient, thereby saving time and resources.

Granularity

When you know the objective of the project, you’ll better know what is the level of detail that’s required for the project. This makes a difference to the annotation labels, since you can either then create labels that are highly specific or broad.

Customization

Different project objectives might require different labels that are tailored to the problem that the project is trying to solve.

Examples

Customer Support Ticket Classification: the objective here is to classify customer support tickets based on the issues that the tickets describe. Hence, the labels can be: “billing issue”, “technical problem”, “account management”, etc. If you want to learn more about customer feedback analysis you can read our article here.

Social Media Moderation: here, the objective is to identify and categorize content that might violate community guidelines. Labels can be: “hate speech”, “graphic violence”, “spam”, etc.

Medical Test Annotation: the objective here is to extract relevant information from medical texts. Hence, labels can be: “medical condition” (”diabetes”, “hypertension”, “asthma”), “treatments” (”medication”, “surgery”, “therapy”), etc.

2. Identify Relevant Labels & Label Groups for Annotation

Once you have decided and agreed on the objective of the text annotation project, the next big step is to identify relevant labels and label groups for annotation. This is a crucial step because the choice of your labels directly impacts the quality as well as the usefulness of annotated data. Here’s how to select suitable labels based on your objective:

Define the objective

This shouldn’t be too hard since you already did it in the previous step. You want to define what is the purpose of your project, what are the desired outcomes, and what is your target audience.

Identify Key Concepts

Now that the objective has been clearly defined, it is necessary to identify the key labels and label groups that you want to capture in the annotation. They should be essential aspects of the problem’s domain.

Create an Initial Label Set

Create a set of labels that can represent the key concepts that you identified above. You want to create labels that are specific, unambiguous, and mutually exclusive, so as to reduce potential confusion.

3. Make Sure Labels are Specific, Relevant, and Comprehensive

When you’re defining your labels, you want to make sure that they are specific, relevant and comprehensive.

Specific labels

These are important because they minimize any confusions that might arise during the annotation process. When your labels are specific, annotators can be confident in their work, and result in a high quality dataset.

Example:

1. In Customer Support Ticket Classification, a non-specific label might be “issue”. This doesn’t provide enough information about the nature of the issue. “Billing Issue”, “Technical Problem”, or “Account Management” might be better, more specific labels.
2. In Social Media Content Moderation, a non-specific label might be “Inappropriate Content”. A more specific label set is “Hate Speech”, “Graphic Violence”, or “Spam”.

Relevant labels

Labels that are relevant capture information that is directly related to the objective of your project. When you focus on labels and label groups that are relevant, your annotation process becomes more efficient.

Example:

1. In Customer Support Ticket Classification, labels like “positive”, “negative”, “neutral” are not relevant, since they don’t have anything to do with customer support, and solving the problem at hand.
2. In Social Media Content Moderation, labels like “Fiction”, “Non-fiction”, “News Article” are not relevant since they don’t help with moderating content.

Comprehensive labels

Comprehensive label sets ensure that all of the concepts, labels and label groups that a project involves are covered by your labels. This way, you can be sure that your dataset is complete and balanced, and it accurately represents the problem domain.

4. Ensure Labels are Mutually Exclusive

It is quite important that the labels that you create are non-overlapping. This means that each text segment must be able to be assigned to only one category, preventing ambiguity and inconsistency during the annotation process.

Clarity: when labels non-overlapping, they provide a clear and unambiguous framework for annotators, and it helps make it easier for them to understand the distinctions between labels and label groups, and hence apply labels accurately.

Consistency: when you ensure that each text segment can only be assigned to one category, this promotes consistency in the text annotation process. As we read before, consistent labeling is essential for creating a high quality annotated dataset.

Improved model performance: when a dataset is annotated with labels that are mutually exclusive, non-overlapping, and collectively exhaustive, it is more likely to lead to a better performing machine learning model.

How to create mutually exclusive labels

1. Define clear boundaries: it is necessary to define clear and distinct boundaries between labels and label groups. You need to ensure that each label represents a unique concept. This helps prevent overlaps between labels and label groups and makes it easier for annotators to apply labels accurately.
2. Use specific and unambiguous terms: you should choose label names that are specific and unambiguous, because this minimizes the potential for confusion between similar or related concepts.

Examples

1. In Customer Support Ticket Classification, the following labels are not mutually exclusive: “Issue”, “Request”, “Complaint”. This is because they can all represent the same text. These labels are mutually exclusive: “Billing Issue”, “Technical Problem”, “Feature Request” – because they represent different issues and types of text.
2. In Social Media Content Moderation, the following are not mutually exclusive: “Inappropriate content”, “Violence”, “Hate Speech”. This is because they can overlap in what they represent. These are mutually exclusive: “Hate Speech”, “Graphic Violence”, “Spam”, “Misinformation”.
3. In Medical Text Annotation, the following are not mutually exclusive: “Medical Condition”, “Symptom”, “Treatment”. These, however are mutually exclusive: “Diagnosis”, “Symptom Description”, “Medication Name”.

5. Organize Labels Hierarchically

When there are large numbers of labels to manage, the annotation process can be a lot more complex and challenging.

This is where it becomes important to organize your labels hierarchically — this helps streamline this process, and make it much easier for annotators to find the relevant labels for the text they’re labeling.

Hierarchical organization of labels involves grouping related labels together into label groups, and creating a multi-level structure that shows the relationships between the different labels in the set.

This way, when someone is annotating a piece of text, they can first select the top-level category, and then go narrower and narrower till they find the right label.

This reduces the cognitive load for annotators, makes it easier for them to find and apply the correct labels, and in turn, improves the quality of annotation.

By organizing labels into a hierarchical structure, it also makes it easier for project managers to maintain the label set, since it then becomes easy to add, modify or remove labels without disrupting the overall structure of the label set.

Example

For Customer Support Ticket Classification, here’s a suggested hierarchical structure for labels:

This way, the 20 different labels are now organized into 4 different top-level label groups. It has now become much easier to annotate — annotators can first choose the top-level category, and then choose a more specific subcategory.

6. Create Clear Definitions and Guidelines

When you’re creating labels for annotation, it’s very important to provide clear and concise definitions for each class, for top level label groups as well as lower level label groups. This will ensure that annotators will understand the differences between the labels, and this will improve the accuracy and consistency of the annotations.

You should also ideally provide examples, so as to differentiate between similar looking labels, especially when the definitions alone might not be sufficient to show the differences. These can help annotators better understand the context in which each label should be applied.

They can help with clarifying the nuances of each label, and the contexts in which they should be used. They can also reinforce the definitions and guidelines, and ensure that annotators have a good understanding of what the distinctions between similar labels are.

7. Keep the Number of Labels Manageable

While it might be tempting to define as many labels as possible in the pursuit of comprehensiveness, there are some risks involved with having too many labels:

1. Increased complexity: more labels makes the annotation process more complex, and this can lead to confusion and increased cognitive load for annotators.
2. Reduced accuracy: more labels makes it more challenging for annotators to choose the right label, and this reduces accuracy.
3. Longer annotation time: when there are more labels, it will naturally take more time for annotators to find the right label.
4. Overfitting: when you have too many labels in a dataset, you risk overfitting in machine learning models, where the model becomes highly specialized in training data but might perform poorly on real world, new, unseen data. Not good.

How to keep the number of labels manageable

1. Focus on the objective: make sure each label is properly aligned with the project’s objective. This is one more reason to have a well-defined objective.
2. Merge similar labels: if two or more labels capture similar information, consider merging them.
3. Use hierarchy: as mentioned before, a hierarchical organization can make things much simpler. When you see hierarchy, you might learn about labels that are redundant.
4. Limit granularity: try to maintain a balance between granularity and generalization, allowing to capture relevant information without overwhelming annotators. Use your judgement.
5. Test and refine: as we’ll mention in the next section, it’s important to keep testing and refining, since you’re only likely to arrive at a final solid set of labels after iteration.

8. Test, Refine, and Iterate

Once you’ve created your initial set of labels, the next important step is to test and refine this initial set of labels. This will help ensure that the label set is well-defined, useful, and appropriate for the project’s objectives. This testing phase can also help identify any issues or challenges with the label set, so that you can fix them before you go full-scale on your annotation, when it might be much harder and expensive to fix errors.

1. Early Error Detection: When you test the labels on a small sample, you can detect potential issues such as ambiguous definitions or overlapping labels and label groups.
2. Annotator Feedback: Annotators can provide valuable feedback regarding the clarity and usefulness of the labels, as well as any challenges that they might have faced during annotation.
3. Validation of Label Relevance: When you test on a small sample, you can make sure that labels are relevant and appropriate for your data and objectives.

How to iterate based on feedback and results

1. Clarify ambiguous definitions
2. Merge or separate similar or overlapping labels
3. Add new labels to address missing information, labels and label groups
4. Remove irrelevant or redundant labels
5. Adjust the hierarchical organization of the labels

9. Train and Support Annotators

If your annotators are properly trained, your annotation project is more likely to be successful. This is because their ability to understand and apply labels correctly will directly impact the quality of the annotated dataset, which in turn will influence the performance of the model that is being trained on that dataset.

How to train annotators

1. Comprehensive Guidelines: Develop clear and detailed guidelines that cover the project’s objectives, label definitions, the annotation process, and any other specific rules or requirements. Make sure these guidelines are easily accessible.
2. Training Materials: Offer materials such as slides, videos or documents that explain the annotation process and provide thorough examples of how to apply the labels correctly.
3. Hands-on Practice: Provide annotators with practice exercises or sample data to annotate. This helps them gain experience and apply their understanding of the labels in a practical context.
4. Feedback and Iteration: Regularly review annotators’ work and provide feedback on their performance, addressing any issues or concerns and offering guidance for improvement.
5. Ongoing Support: Maintain open lines of communication with annotators throughout the project, offering assistance and guidance as necessary. Communicate any changes or updates to the project scope, objectives or requirements.

10. Monitor and Evaluate Annotation Quality

Finally, ongoing monitoring and evaluation of the annotations as well as the labels is important for the success of a text annotation project. By assessing your annotations regularly, you can identify any inconsistencies, inaccuracies or issues that may arise, and hence fix them in a timely manner.

1. Quality Assurance: By regularly analyzing your annotations and evaluating them, you can ensure that the dataset’s quality remains high and meets the project’s requirements.
2. Annotator Performance: Monitoring annotator performance allows you to provide targeted feedback and support, helping annotators improve their skills and maintain consistency in their work.
3. Adaptability: Regular evaluation enables you to identify and address any changes in the project requirements, label definitions, or guidelines, ensuring that the annotation process remains aligned with the project's objectives.

Tips on Giving Feedback to Annotators

To give feedback to annotators and update label definitions and guidelines as needed, consider the following tips:

1. Constructive Feedback: When providing feedback to annotators, focus on offering constructive and actionable suggestions for improvement. Be specific about the issues you've identified and provide clear guidance on how to address them.
2. Positive Reinforcement: Recognize and acknowledge the good work and progress made by annotators. Positive reinforcement helps boost morale, motivation, and confidence in their abilities.
3. Regular Check-ins: Schedule regular check-ins with annotators to discuss their performance, address any concerns or questions, and provide feedback. This ongoing communication helps ensure that any issues are promptly addressed and that annotators feel supported throughout the project.
4. Collaborative Environment: Encourage an open and collaborative environment where annotators can ask questions, seek clarification, and share their experiences with each other. This approach promotes peer learning and fosters a sense of teamwork and shared responsibility for the project's success.
5. Updating Label Definitions and Guidelines: Based on the feedback and evaluation results, update the label definitions and guidelines as needed to address any ambiguities, inconsistencies, or gaps. Ensure that these updated materials are promptly shared with annotators and that they have access to the latest information.
6. Monitor Progress: Track the progress of the annotation project and make adjustments as needed, such as reassigning resources, providing additional training, or updating guidelines to better align with the project's objectives.

11. Annotate in Lettria

Get Started with Annotating Your Data in Lettria

One of the easiest ways to create a set of labels and annotate your data is to use Lettria. Our no-code platform allows you to not only define and manage labels, and annotate your data, but also to train your solution on the data and start using it for your classification projects, all in one place.

It’s truly the easiest way to get started with implementing NLP in your organization.

Conclusion

In conclusion, creating effective text annotation labels is crucial for ensuring the success of any natural language processing or machine learning project. By adhering to best practices and carefully considering each step of the process, you can achieve high-quality and consistent results in your own projects.

The key steps and best practices for creating effective text annotation labels include:

1. Establishing a clear objective for your project to guide the selection of appropriate labels.
2. Choosing specific, relevant, and comprehensive labels that directly align with your project's goals.
3. Ensuring that labels are mutually exclusive and non-overlapping to reduce annotator confusion and maintain consistency.
4. Organizing labels hierarchically, if necessary, to make it easier for annotators to navigate and apply them.
5. Providing clear and concise definitions for each label, along with examples to distinguish between similar labels and label groups.
6. Limiting the number of labels to capture the necessary information without overwhelming annotators or introducing complexity.
7. Testing the labels on a small sample of text data to ensure their effectiveness and refining them based on feedback and results.
8. Properly training annotators and providing them with comprehensive guidelines, training materials, and ongoing support.
9. Monitoring and evaluating the annotation process regularly to maintain quality assurance and provide feedback to annotators.

By following these guidelines, you can create a strong foundation for your text annotation project and ensure that your dataset is accurate, consistent, and valuable for your intended application. Remember to keep the lines of communication open with your annotators and continually refine and adjust your label set as needed. By investing in the quality of your text annotation labels and the training of your annotators, you can achieve the high-quality results needed to drive the success of your natural language processing or machine learning projects.