Labeling text data is a crucial step in preparing datasets for Natural Language Processing (NLP) applications, as it helps machines understand and interpret human language. With Labelo, users can easily annotate text for various NLP tasks, including sentiment analysis, named entity recognition, text classification, and more. Labelo’s intuitive interface simplifies the labeling process, allowing users to tag words, phrases, or sentences based on specific project requirements. The platform’s customizable features enable teams to adapt the workflow for different NLP needs, ensuring high-quality annotations that improve model accuracy. Additionally, Labelo’s integration capabilities make it easy to incorporate annotated datasets into existing NLP pipelines, empowering data science teams to develop and refine language models more efficiently.

Templates in NLP

Text Classification:- Text classification is the process of assigning a category or label to a piece of text based on its content. This can be done using machine learning models that have been trained on labeled datasets. 

Named Entity Recognition:- Named Entity Recognition (NER) is the process of identifying and classifying entities mentioned in a text into predefined categories, such as the names of people, organizations, locations, dates, etc.

Text Summarization:- Text summarization is the process of condensing a long piece of text into a shorter version while retaining the essential information and meaning. 

Question Answering:- Question Answering (QA) is the task of building systems that automatically answer questions posed by humans in natural language. These systems can either extract answers from a given text (closed-domain) or generate answers based on a broader understanding of a topic (open-domain).

Taxonomy:- Taxonomy refers to a hierarchical classification system that organizes concepts or items into categories and subcategories based on their relationships. In data science and NLP, a taxonomy is used to classify and organize information in a structured manner.

Relation Extraction:- Relation Extraction is the task of identifying and classifying relationships between entities in a text. This process involves detecting pairs of entities and the type of relationship that exists between them.

Text Labeling Workflow

Labelo offers an organized, user-friendly workflow for data annotation, enabling users to produce high-quality, precise annotations with ease. By guiding users through each step of the annotation process, Labelo ensures that tasks are both efficient and accurate, reducing the chance of errors and promoting consistency across the dataset.

1. First, navigate to the project page and create a new project over there. A complete guide on creating and configuring the project can be found in Create and Configure Projects in Labelo

2. The next step is to import a text file into the project that we have created. Importing data can be seen in the attached blog How to Import and Export Datasets in Labelo

3. Once data is imported, you can now choose the labeling setup corresponding to Natural Language Processing which could be available in Configuring Laeling Interface in Labelo . Here I am choosing the template Named Entity Recognition for your better understanding. 

Named Entity Recognition (NER) is a Natural Language Processing (NLP) technique used to identify and classify key pieces of information (entities) within text. This process tags specific words or phrases in the text as belonging to predefined categories, such as names of people, organizations, locations, dates, and other important items.

4. Now select any of the important text from the data and choose the corresponding label shown above so that the particular text will be highlighted. For eg, suppose you are trying to highlight the name of a person, select the corresponding name and click on the label PER, hence it will be labeled.

You can add labels of your choice and label any kind of text.

5. After completing an annotation, click the Submit button to finalize it and move on to the next task. This approach guarantees that each data segment is accurately labeled and systematically documented.

Now if you want to update the annotation that was already submitted, you can simply open the corresponding task and change the label. Once you made changes, you can click ‘Update’ in order to apply your changes.