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To automate business workflows, you need to search through the text to find relevant information. To address this need, our team performs NER, so you can train your model to recognize the specified entities, like “name,” “date,” or “contract number” and enhances information retrieval efficiency.
If you need to group texts according to their sentiment, use our sentiment analysis service. Your model will be able to recognize if the text is positive, negative, or neutral. The emotional spectrum can be broadened for a specific task and include other categories, depending on your project requirements.
Our intent analysis service helps the machine determine the intent of the text you provide. This way, your model will be able to search through the text for cues. We can help your business when looking through thousands of client emails to prioritize those that have, for example, a financial intent.
For your audio-to-text transcription tasks, we accurately transcribe spoken audio into written text. We use advanced technology and human expertise to ensure high-quality transcripts that capture every word and nuances of the audio file.
Text classification at Label Your Data helps your model group the texts according to their themes. A great example is text categorization of the news, such as “Politics”, “Art and culture”, “Sports”, etc. We help businesses extract insights from their text data for better decision-making.
With many text annotation companies vying for attention, Label Your Data stands out by offering exceptional benefits to our clients, making us a trusted partner for your NLP initiatives.
Label Your Data’s text annotation solutions are designed with flexibility in mind. We can work either with our own tool or seamlessly integrate with the client’s existing tool. Moreover, we provide text annotation services in 55 different languages, catering to the specific needs of our clients
Our text labeling services stand out due to the personalized approach of our tailored team. We can hire professionals with specialized backgrounds, such as legal, psychology, accounting, and native speakers to achieve high-quality and accurate results. This guarantees that your text annotation needs will be met with precision and attention to detail.
Our quality control methods are diverse, particularly when it comes to text, which is often a subjective annotation process involving sentiment analysis or semantic annotation. We offer a variety of QA approaches, including basic quality control with a specialized team or cross-reference QA for a more objective assessment.
The Label Your Data team adopts a nuanced methodology for a rigorous text annotation process:
Data collection usually happens on the client’s side. But if you don’t supply any data, our team performs data collection at your request. You determine the type of data to gather, the volume, and the method for acquiring it.
At this stage, we coordinate with you the key project details. Together, we decide on the process, policies, data labeling criteria, and annotation tools to create a complete textual dataset.
As we receive the first batch of data, our annotators run a small annotation sample to verify all the edge cases with the client. A free pilot helps decide whether our text annotation service can satisfy all your demands.
Once the pilot is done and the results are satisfactory, we proceed to full-scale annotation by assigning a dedicated team to the project. On request, we can set up on-site teams and provide the option of working in the office. We perform text annotation in batches, allowing you to track progress.
Before sending the completed annotations, we ensure their quality and validity. To ensure the number of mistakes is negligible, Label Your Data delivers a thorough QA.
Our 10+ years of experience in building remote teams allows us to expertly navigate 500+ data annotators and provide high-quality text annotation services in 55 languages. If you choose us as your text annotation outsourcing partner, you choose the winning mix of quality, speed, and security of your text data.
A varied corpus of templatic documents requiring precise text annotation.
Combining two types of text annotation, such as OCR and NER annotation for PDF documents.
A tech-insurance Client approached the Label Your Data team to automate the extraction of information from scanned documents (e.g., contracts, bills, receipts, and insurance quotes). The aim was to recognize text from typewritten PDF documents and label entities like dates, names and addresses, and monetary amounts. Our skilled annotators combined OCR and NER text annotations to apply them to both structured and tabular text.
Understanding the specifics of the text classification task.
Additional training conducted for the text annotation team.
A Client from the IT sector approached Label Your Data with a project that required training their live chatbot with the data annotated for text classification. The project involved a large volume of data, with 10 to 15 thousand texts to label each month. Our team underwent additional training to gain a deep understanding of the subject (i.e., the differences between parameters and tags for different types of text) and meet the project requirements.
Large volume of multilingual texts and a short timeframe.
Assembling a big, multilingual team of remote data annotators.
One of the text labeling projects our team has worked on was a translation task involving 15,000 weekly review texts in six languages, including French, German, Spanish, Portuguese, Italian, and Chinese, with the goal of automating analysis of hotel guest reviews. A large, remote, multilingual team of annotators was brought together to complete the project within the specified timeframe and provide the Client with high-quality and secure text annotation services.
Yes, text annotation can be used for training a deep learning model. Such algorithms function on the basis of neural networks that mimic the way a human brain works. Still, such a model requires well-annotated data that can be provided by a text annotation development company, like Label Your Data.
Text annotation solutions play a crucial role in improving the accuracy of NLP tasks by providing labeled data that enables ML models to recognize patterns in text elements and make precise predictions.
Text annotation development services refer to the process of adding tags, aka text tagging, to the pieces of the text data. These tags should be accurate, objective, and relevant to ensure that you have a high-quality training dataset.