What is natural language processing or NLP?
🧐 Natural language processing (or NLP For”Natural Language Processing“) is a branch of artificial intelligence (AI) that focuses on computers understanding and analyzing human language. 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, we can analyze textual data on a large scale and extract valuable information from it. A key application of NLP is Named Entity Recognition (NER), which focuses on the recognition and labeling of various types of entities (entities) such as names, locations, dates, emails, etc., allowing specific information to be automatically extracted from textual, audio, and video. Implementing NER involves writing code that follows specific documentation and examples, especially 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 vast sets of linguistic data in order to develop an understanding of language and its structures.
The NLP finds numerous applications in daily life, including voice assistants, machine translation systems, chatbots, information retrieval, social media analysis, and automatic document classification. A concrete example of a project carried out with the help of Innovatiana consisted in the certification of thousands of real estate ads to train an NLP model. Information such as property size, number of rooms, available amenities, and more could be automatically extracted from unstructured data.
💡 Discover 5 key points below for the success of your multilingual NLP certification projects!
1. Define clear guidelines (instructions for labelling your textual documents)
During the data labelling for NLP, it is essential to establish clear guidelines for Data Labelers, including 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, feelings, 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 texts. For example, it is fundamental for the pseudonymization of personal data in documents and the analysis of unstructured texts, thus facilitating the protection of privacy and the extraction of relevant information.
Additionally, using 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 creating labeling tasks for entity recognition using the API SageMaker are examples of its practical application. Detailed examples and instructions should be provided to assist annotators to understand the expectations and practical applications of NER, such as document indexing, information organization, question response systems, and other NLP tasks.
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2. Train annotators in AI labelling techniques
It is necessary to train Data Labelers on specific labelling tasks. They should be familiar with the guidelines, goals, and quality criteria. Practical training and regular review sessions can help improve the consistency and quality of annotations.
3. Maintain the consistency of the dataset
Consistency is critical during labelling. 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 a specific glossary is highly recommended. These tools provide clear references to terminology and annotation methodology, thus reducing individual variations and ensuring greater data accuracy.
4. Review and validate annotations
The step of verifying and validating annotations is essential to maintain the quality and reliability of an annotated data set. This rigorous procedure should include internal quality control, where, for example, a Labeling Manager on the Innovatiana team oversees and reviews the annotations to ensure their accuracy. During this phase, a specialized team reviews the annotations to detect and correct errors, ambiguities, and inconsistencies. This approach makes it possible to optimize data quality and to ensure their reliability for future applications.
5. Iterate and improve
NLP certification is an iterative process for entity recognition and named entity recognition. Organizations face significant 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 the quality of annotations and refine the tasks of recognizing and categorizing words and names in NLP projects. The errors and difficulties encountered can serve as a basis for new guidelines or adjustments to the labelling process, or even for 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 Ensuring high quality data to train natural language processing models (NLP or Natural Language Processing) and obtain reliable and accurate results.
