Automatic Natural LanguageProcessing( NLP ) is a branch of Artificial Intelligence (AI) that focuses on the understanding and analysis of human language by computers. NER or Named Entity Recognition, a technique based on NLP, is a solution that automatically extracts information from textual, audio or video documents.
In concrete terms, this means that computers can understand natural language, such as emails, tweets and newspaper articles, and extract information from them. Thanks to NLP, textual data can be analyzed on a massive scale and valuable information extracted. A key application of NLP is Named Entity Recognition (NER), which focuses on the recognition and labeling of different types of entities (entities) such as names, places, dates, emails, etc., making it possible to automatically extract specific information from textual, audio and 🔗 video. Implementing NER involves writing code that follows specific documentation and examples, particularly in contexts such as using 🔗 Azure AI Language. To process natural language, NLP uses statistical models and deep neural networks ("Deep Learning"). These models are trained on large linguistic datasets to develop an understanding of language and its structures.
NLP has many applications in everyday life, including voice assistants, machine translation systems, chatbots, information retrieval, social network analysis and automatic document classification. A concrete example of a project carried out with the help of 🔗 Innovatiana involved labeling thousands of real estate ads to train an NLP model. Information such as property size, number of bedrooms, available facilities and much more could be extracted automatically from unstructured data.
Here are 5 key points to ensure the success of your multilingual NLP labeling projects!
1. Define clear guidelines (labeling instructions for your text documents)
When 🔗 data labeling for NLP, it is essential to establish clear guidelines for 🔗 Data Labelersincluding for the application of Named Entity Recognition (NER) in various projects. These guidelines should cover the various aspects to be annotated, such as named entities, relationships, sentiments, etc., and explain how to effectively integrate NER into the user's application. Entity recognition plays a key role in identifying and classifying entities in unstructured text. It is fundamental, for example, to the pseudonymization of personal data in documents and the analysis of unstructured text, facilitating the protection of privacy and the extraction of relevant information.
In addition, the use of entity recognition in Azure AI Language to identify and classify entities, the process of labeling entities in text using NER in Amazon SageMaker Ground Truth, and the creation of labeling tasks for entity recognition using the API 🔗 SageMaker are examples of its practical application. Examples and detailed instructions should be provided to help 🔗 annotators understand the expectations and practical applications of NER, such as document indexing, information organization, question answering systems and other NLP tasks.
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2. Train annotators in IA labeling techniques
Data labelers need to be trained in the specific tasks involved in data labeling. They need to be familiar with the guidelines, objectives and quality criteria. Hands-on training and regular review sessions can help improve the consistency and quality of annotations.
3. Maintain dataset consistency
Consistency is critical when labeling. It is imperative that all annotators, or "Data Labelers", consistently apply the same criteria and follow the same guidelines to ensure consistent annotations. To achieve this, the use of a detailed guide or specific glossary is strongly recommended. These tools provide clear references on annotation terminology and methodology, reducing individual variations and ensuring greater data accuracy.
4. Check and validate annotations
The annotation verification and validation stage is essential to maintain the quality and reliability of an annotated dataset. This rigorous procedure should include internal quality control, where, for example, a Labeling Manager within the Innovatiana team supervises and reviews annotations to ensure their accuracy. During this phase, a specialized team reviews the annotations to detect and correct errors, ambiguities and inconsistencies. Thisoptimizes data quality and ensures reliability for future applications.
5. Iterate and improve
NLP labeling is an iterative process, for both entity recognition and named entity recognition. Organizations face considerable challenges in managing large volumes of documents, and the use of Named Entity Recognition (NER) can help overcome these challenges by automatically extracting information from text, audio and video documents.
It is important to gather feedback from Data Labelers and end-users to constantly improve annotation quality and refine word and noun recognition and categorization tasks in NLP projects. Errors and difficulties encountered can serve as the basis for new guidelines or adjustments to the labeling process, or even a change of tool during the project if the difficulties encountered with the platform are numerous and have a negative impact on data quality!
By following these best practices, it is possible to guarantee high-quality data for training natural language processing (NLP) models, and obtain reliable, accurate results.
