Looking for language data annotation to train your NLP models? Label Your Data can make the linguistic elements in data deliver the meaning to AI.contact us
NER, or entity extraction, is the task used to find and classify specific entities (words and phrases of high value, like names, dates, etc.).
We use text classification to group text based on context similarities (e.g., for automated spam filters or topic tagging).
The chosen keywords and key phrases of the text are labeled by our team as relevant within the scope of this linguistic annotation task.
This type of labeling we use for linking all relevant entities throughout the text and to bridge the relations between them.
OCR and IDC are on the verge between NLP and computer vision. They require a machine to read and understand a scan or a photo of some text with the goal of turning it into an editable digital copy.
Transforming spoken speech to editable text, this task can go as little as phonetic features or as big as discourse structures.
Here we deal with audio and video data as it researches the intonation and tone, stress, and natural pauses in spoken language.
Our annotators interpret the definition of the words to extract the subjective meanings (including opinions, emotions, and attitudes towards certain entities).
Machines require labeled data to not only analyze the grammatical structure of the text, but also the semantic linguistic elements that convey meaning and context. Unlike other linguistic annotation service companies, Label Your Data offers valuable extras to help you achieve this.
Label Your Data offers expert multilingual support in 55 languages. Our linguistic annotation services will help you reach a wider audience and enter new markets with ease.
Depending on your project needs, we can hire data labeling experts with specialized backgrounds, such as legal or psychology, and native speakers to achieve high-quality results.
Security is our top priority at Label Your Data. We boast compliance with GDPR and CCPA, and the ISO/IEC 27001:2013 certification ensures the security of even the most sensitive data.
Our team has developed a field-proven strategy that we use to deliver the most optimal linguistic annotation solutions for our clients.
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, data labeling criteria, implement linguistic rules, and tools to create a complete 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 linguistic 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 annotations in batches, allowing you to track progress.
Before sending the completed annotations, we ensure their quality and validity by conducting a thorough QA.
Our 10+ years of experience in building remote teams allows us to expertly navigate 500+ data annotators and provide expert linguistic annotation services in 55 languages. If you choose us as your linguistic annotation services provider, you choose the winning mix of quality, speed, and security.
Insufficient quality of scanned documents with multiple languages involved
Hiring and training an annotation team with a multilingual background.
The Client from real estate asked us to convert paper documents into the digital format. To process 7,000 to 15,000 documents a week, our annotators applied OCR to transcribe the text in the scanned documents, followed by NER to extract the relevant information. Yet, the quality of certain photocopies was poor and included extensive multilingual lexicons. We created a multilingual team of annotators who completed the work within the set timeframe.
Main Challenge: Training annotators to handle an extensive volume of diverse text data.
Combination of several linguistic annotation types.
A business intelligence enterprise was designing an ML model that could separate fake news from the real ones. They looked for an expert linguistic annotation company to label and assess 10,000 social media posts, forums, blogs, and news articles. The Label Your Data team had to combine several linguistic annotation types, including sentiment and intent analysis, as well as text classification annotation.
Sensitive health-related information
Additional data protection training for the linguistic annotation team.
An EHS company asked us to process 27,000 incident reports using NER annotation. However, the health-related information is highly sensitive and requires additional security measures. Label Your Data is compliant with GDPR and CCPA, yet we trained our annotators to ensure there could be no mistreatment of this data during the labeling process. Then, we used NER to extract the relevant information from the incident reports.
A linguistic annotation company usually adds relevant tags (linguistic metadata) to the data that can be separate characters, words, or phrases. This computer-readable data is used to train your ML algorithm to recognize patterns in a language.
The main challenges arise when the meaning of the text is not literate, there are several languages included, or there are subjective issues like the analysis of humor or sentiment.
Any type of data that contains the elements of natural languages can be used for annotation by our linguistic annotation service company. Most commonly, it is text and audio, as well as the video data that has speech elements.