If you have used or heard of the wonders of ChatGPT, you are already aware of the importance of text annotation because it is behind the marvels of ChatGPT as well as other similar types of generative machine learning tools. Natural language processing, precisely referred to as NLP, is a machine learning model to imitates the human brain in learning, deciding, and responding to text input in the form of natural language we use in our daily communication without any explicit computer programming or human intervention to instruct.
To make the NLP model learn from the text input, text annotation services by professional text annotators are extremely critical. It trains the NLP model based on the tagging of different parameters of the text. The accuracy, reliability, and efficiency of NLP are heavily dependent on text annotation. Low-quality text annotation results in misleading and hallucinated outputs.
Fundamentals of Text Annotation
Text annotation is a process of tagging, marking, naming, meaning, or providing any other features and characteristics of text in such a way that NLP models can learn from the annotated text easily and build accurate responses to the queries. Traditionally, a data annotation specialist is hired to perform this task manually, but numerous automated processes have also been developed to perform this activity through AI applications.
Main Components of Text Annotation
Text annotation is a larger process that consists of numerous parts or components used to mark attributes within a text dataset. A few of them include:
- Text segmentation – In this component of text annotation NLP service, the annotators segregate the text into meaningful parts such as paragraphs, sentences, and words to make it very understandable datasets ingested to the machine learning (ML) models. This is the fundamental part of building datasets for the supervised machine-learning process.
- Parts of speech tagging – Another significant component of text annotation services for NLP in the machine learning field is the parts of speech tagging. It is a sub-process of text annotation in which the text is marked with related parts of speech such as verbs, nouns, pronouns, prepositions, interjections, conjunctions, proverbs, and interjections.
- Entity recognition – As the name indicates, this component deals with the tagging of different entities such as locations, places, names, products, themes, times, values, and other attributes in text data. The main purpose of entity recognition is to extract the information contained in the text for easy understanding of ML models. In general terminology of NLP or data annotation services, entity recognition is known as named entity recognition (NER).
- Sentiment analysis – The sentiment analysis component is also referred to as the opinion mining process in NLP technology. The sentiments expressed in the text are tagged by negative or positive values to make ML models understand the tone, emotion, or attitude of the subject through positive or negative values.
NLP pipeline is a comprehensive machine-learning process consisting of numerous automated steps to process raw or tagged data and convert it into machine-understandable output in the form of a summary or response. Without an NLP pipeline, no data can be processed for the machine to learn and produce a desired response.
Types of Text Annotation
Text annotation can be accomplished through two main categories consisting of different methods as mentioned here.
Manual Text Annotation
- Hiring human annotators – The traditional method of performing text annotation activity is hiring a data annotator to work on building text datasets. Hiring an on-premises data annotator is a costly option.
- Using crowdsourcing Annotation – Using the services of common people online to accomplish a text annotation manually is another cheaper option but the quality and management of multiple contributors are two significant concerns of it.
Automated Text Annotation
- Rule-based annotation – A computer tool based on rule-based instructions to tag predefined items or parameters is used in this automated text annotation.
- Machine learning-based annotation – ML-based annotation is done by the advanced NLP models, which use their respective training to annotate the text. It is one of the advanced methods of text annotation with a few drawbacks of complexity and upfront cost.
- Hybrid approach – The combination of rule-based and ML-based methods is used to annotate text for building training datasets in this approach.
Using the services of a virtual or part time CTO for comprehensive management of text annotation and ML training process is one of the best options in modern NLP models to save cost and enhance the quality of the projects.
Application of NLP Models Powered by Text Annotation
Text annotation is used to build text datasets for training the NLP models. The main applications of text annotation in those models are mentioned here:
- Named entity recognition (NER) – Text annotation helps NLP projects to differentiate between multiple disciplines of entities such as locations, products, places, values, times, and many other broader categories of items.
- Sentiment analysis – By using text annotation from professional text annotation companies ensures the detection of sentiments, tones, attitudes, and other emotive components contained in a text.
- Machine translation – NLP models are extensively used in the translation of almost all types of languages spoken in the world.
- Question answering – You can ask any kind of question to get an answer from those NLP models perfectly.
- Chatbots and virtual assistants – A massive ratio of online virtual assistants and chatbots on websites are NLP-powered machines.
Text Annotation Best Practices with the Help of Part-Time CTO
A part-time CTO to manage and train the entire NLP project offers you many benefits in the modern competitive market by implementing the best practices such as:
- Quality control – Remote CTOs provide professional support to you to achieve higher-quality text annotation by using the latest QC tools.
- Avoiding overfitting – Overfitting is a major problem in the text annotation process, which can be avoided by using a part-time CTO.
- Training annotators – Always, keep your data annotators updated through training in modern trends and techniques.
- Inter-annotator agreements (IAA) – Use an automated quality measuring method known as inter-annotator agreement for measuring quality.
- Continual iteration and feedback – Make sure continual feedback, rechecking, and iterative tracking are done during the text annotation process. A remote CTO is effective in maintaining this perfectly.
Text annotation is an extremely critical part of building a reliable NLP generative model. Without professional-grade text annotation services, it is not possible for machine learning projects to complete the most fundamental processes and functions of NLP models. Using specialized text annotators remotely enhances the quality and reliability of your project and reduces the cost significantly.