What are some tools and techniques for automating content annotation using AI or machine learning?

1 Natural Language Processing

One of the most common techniques for automating content annotation of text-based resources is natural language processing (NLP). NLP is a branch of AI that deals with the analysis and generation of natural language. NLP can perform tasks such as named entity recognition, sentiment analysis, topic modeling, summarization, and keyword extraction. These tasks can help identify and extract relevant information from text and assign metadata or tags accordingly. For example, NLP can help annotate news articles with their author, date, location, genre, sentiment, and main keywords.

2 Computer Vision

Another technique for automating content annotation of visual resources, such as images or video, is computer vision. Computer vision is a branch of AI that deals with the understanding and manipulation of visual data. Computer vision can perform tasks such as face detection, object recognition, scene classification, optical character recognition, and image captioning. These tasks can help detect and describe the content and context of visual resources and assign metadata or tags accordingly. For example, computer vision can help annotate photos with their location, date, people, objects, and captions.

3 Audio Processing

A third technique for automating content annotation of audio resources, such as podcasts or music, is audio processing. Audio processing is a branch of AI that deals with the analysis and synthesis of sound signals. Audio processing can perform tasks such as speech recognition, speaker identification, emotion recognition, music genre classification, and audio summarization. These tasks can help recognize and extract meaningful information from audio and assign metadata or tags accordingly. For example, audio processing can help annotate podcasts with their title, speaker, duration, genre, and summary.

4 Annotation Tools

In order to automate content annotation, digital libraries must utilize annotation tools that can incorporate AI or machine learning models and algorithms. Annotation tools are software applications that help create, edit, manage, and export annotations for digital resources. TagTog is a web-based tool that supports manual and automatic annotation of text, images, and PDF documents using NLP and computer vision models. Labelbox is a cloud-based tool that supports manual and automatic annotation of text, images, video, and audio using NLP, computer vision, and audio processing models. Prodigy is a scriptable tool that supports manual and automatic annotation of text using NLP models. All of these tools enable users to customize their own annotation schemas and formats as well as create their own data pipelines and workflows for annotation projects. Additionally, Prodigy allows users to train and improve their own NLP models using active learning and feedback.

5 Annotation Standards

To guarantee the quality and interoperability of automated content annotation, digital libraries must adhere to annotation standards. These standards provide rules and guidelines for creating and using annotations, and are used to harmonize the structure, syntax, semantics, and exchange of annotations across different platforms and domains. Examples of annotation standards include Dublin Core, which defines a set of 15 basic metadata elements for describing digital resources; TEI, which defines a set of XML-based guidelines for encoding and annotating text-based resources; and MPEG-7, which defines a set of XML-based schemas for describing and annotating multimedia resources.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?