What is Text Annotation?
Annotating text is a method of adding information to text to make it more clear, more helpful, or more actionable. Annotated text is used to teach computers to spot patterns and make predictions in natural language processing (NLP) and machine learning applications.
Types of Text Annotation
Annotating texts in various ways fulfills various needs in natural language processing and machine learning. There are many different kinds of annotation, but some of the most frequent include:
- Named Entity Recognition (NER) is the process of extracting and organizing information about the persons, places, things, and times mentioned in a document. Applications like sentiment analysis and information extraction rely heavily on this style of annotation.
- Sentiment Analysis – Determine if a piece of text is good, negative, or neutral based on an examination of the emotions conveyed by the words within it using sentiment analysis. It is common practice to utilize this kind of annotation when analyzing client comments or keeping tabs on social media.
- Part of Speech tagging – Labeling words as nouns, verbs, adjectives, or adverbs is called part-of-speech (POS) tagging. Text categorization and information retrieval are only two examples of popular uses for this sort of annotation.
- Text Classification – Classifying a text according to its content, such as whether it is news, sports, or entertainment, is called text classification. Content filtering and recommendation systems are two common uses for this kind of annotation.
Manual vs Automatic Text Annotation
Annotating texts may be done either by human annotators or automatically by machine learning techniques.
Data that has been manually annotated by experts who are able to grasp the subtleties and intricacies of the text is generally more accurate and exact. Manual annotating takes human work, which increases both the time and cost involved.
On the other hand, automated annotation is more effective since it can be performed rapidly and on a large scale for more difficult jobs. However, the quality of the annotations may be poorer than with hand annotation.
- It is common to use a hybrid approach, combining human and automatic text annotation to maximize both precision and speed.
An example use case is training a machine learning model to automatically annotate data using a subset of data annotated by humans.
In general, the work at hand and the available resources will determine whether the human or auto annotate text is the better option. Annotating data manually may be the best option when precision is paramount, but automating the process may be more efficient when dealing with vast amounts of data.
Text Annotation NLP
With the rise of annotation NLP (natural language processing) and machine learning (ML), there are a variety of platforms and tools available for annotating text. Below are some of the most often-used resources:
- Amazon Mechanical Turk – it’s a crowdsourcing platform that lets customers hire human employees to do activities like annotation for a small fee.
- Brat– you can manually annotate text with features like named entity recognition (NER), POS tagging, and more.
- Prodigy is an expensive tool for semi-automatic text annotating that employs active learning to provide label suggestions for text depending on the user’s prior annotations.
- The Natural Language Toolkit (NLTK) is a Python package for natural language processing that facilitates many different text annotating jobs and methods.
Key Takeaways
Annotating text with extra information to improve its use for analysis and prediction is a crucial step in natural language processing (NLP) and machine learning (ML) applications. Named entity identification, part-of-speech tagging, sentiment analysis, and text categorization are just some of the many forms of text annotation that may be performed, either manually or automatically. Amazon Mechanical Turk, Brat, Prodigy, and NLTK are just some of the tools and platforms you may utilize to simplify and automate the annotation process.
