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Annotation in its simplest definition is the process of making comments, notes or explanations to text, image or any form of data. In addition, it is applicable across numerous domains like literature, big data, education, and abstraction/research to increase the audience’s comprehension and add value and perspective to the process. In this guide, you will discover the various categories of annotations and their uses.
1. Textual Annotation services
Highlighting: Hitting essential passages in the text to
provide the readers with colours, bold scripts or italics. This technique is
applied to call the attention of readers towards particular aspects within a
document such as points, themes, and concepts.
Application: Typically applied in academic reading, research
papers or studying since sometimes while reading, there are important quotes or
terms that one would wish to highlight.
Marginalia: Using notes placed beside a text, in the
margins. Such notes also contain comments, quick notes, questions, or notes
that refer to other works or books.
Application: Usually found in works that entail references
and citations such as historical analysis, novels, and textbooks, especially to
give the readers extra information or questions to ponder on.
Inline Annotation: Incorporating remarks, notes and
implications right into the content of the text through the use of brackets or
parentheses.
Application: Aids in technical writing especially in legal
writing and computer programming where an explanation or supplementary
information is required within the body of the text.
Footnotes and Endnotes: Inserting extra information,
sources, or remarks on the site at the bottom of the concerned page (footnotes)
or at the bottom of the chapter or document (endnotes).
Application: Special to the academic genres such as research
papers, articles, and historical investigation to offer bibliographical
references, sources, or additional information to the reader without interrupting
the flow of the text.
Bounding Boxes: Enclosing the objects in the image with
rectangles, to recognize and sort them. Often the boxes
contain the object that belongs to a certain category and the boxes themselves
are labeled accordingly.
Application: In computer vision and machine learning are used to
train the algorithms for object detection and recognition.
Semantic Segmentation: Labeling the recorded video frames
into different classes, meaning that each pixel is provided with a class label
and the image is thus divided into regions based on the content of each such
region.
Application: Desirable for self-driving cars, medical
diagnosis and imaging, air- and water-quality measurement, or any other task,
for which the precise delineation of object boundaries is required.
Polygon Annotation: Using polygons to better enclose objects
than what is done with rectangular boxes commonly known as bounding boxes.
Application: Used in cases when the studied objects have complex shapes, for example, in satellite imagery analysis when the
buildings, the roads and other objects should be accurately outlined.
Keypoint Annotation: Identifying certain points in an image
such as the faces, limbs’ joints or the corners of objects of interest.
Application: Applicable in facial recognition, pose
estimation and gesture recognition applications.
Transcription: The process of transcribing what was said in
a particular audio and sometimes, we add time stamps to show where in the audio
a particular word or phrase was said.
Application: Useful in making subtitles, making transcripts
of the interviews and analyzing the speech data for linguistic purposes.
Speaker Diarization: This involves the process of
partitioning an audio recording into segments of different speakers and
thereafter associating each segment with a unique speaker.
Application: Applied in capturing, by speech-to-text
service, all activities in the form of a meeting, interview, trial, etc., for
the different speakers’ differentiation.
Emotion Annotation: Assigning a description of a certain
emotion or feelings in the content of the audio such as happy, sad or angry
tones.
Application: Critic for developing of sentiment analysis
systems, customer service chatbots and Interactive voice response systems.
Frame-by-Frame Annotation: To watch and then pause the video
and examine the frame by frame to observe the objects, actions or events in
different frames.
Application: Incredibly important for tasks such as video
surveillance, sports analysis, and training artificial intelligence for video analysis.
Object Tracking: Keeping track of objects’ position and
dynamics in the subsequent frames of the video.
Application: In applications such as AD, video processing
and Augmented Reality to ensure that the object feature remains
invariant across frames in a video stream.
Action Annotation: Identifying what is going on at the time
when certain frames in a video are being shot, including running, jumping, or
using objects.
Application: Vital in the sporting performance analysis,
behavioural tracking, and video coaches’ training tools.
Labeling: Adding labels or tags to the data points in a
given dataset where for example you classify emails as spam or not spam.
Application: Critical in training of the machine learning
models for the purpose of classification including sentiment analysis, image
recognition and natural language processing among others.
Normalization: Making all the different observations
similar, that is scaling or transforming all the data into either a ratio or
interval format.
Application: Crucial in data preprocessing, most importantly
during data preparation for machine learning algorithms because uniform data
formats increase the model’s performance.
Categorization: A classification technique of data wherein
data is divided in categories with prior specifications of the characteristics
the categories should possess before compiling them.
Application: Applied in operation research for categorization
of customers, recognizing pattern infographics and enumeration of recommended
items.
6. Linguistic Annotation
Part-of-Speech Tagging: Categorizing all the words in a
given text according to their grammatical features ( e. g. noun, verbs,
adjectives).
Application: Essential in natural language processing (NLP)
application areas including parsing, machine translation and conversion of
written text to speech.
Named Entity Recognition (NER): Tagging of text in which
information such as names of people, places or organizations, dates, and so on
are identified.
Application: Employed in IE, Web and document search
queries, document sorting and filtering to identify and classify essential
information.
Syntactic Parsing: Markup of the grammar of a sentence, that
is, the way words are arranged in a sentence and the links between them.
Application: It is the key in computational linguistics,
language modelling and in the design of text generation systems.
7. Collaborative Annotation
Crowdsourcing: Data annotation involves the task of getting
a group of people to perform a task of annotation often on the internet.
Application: Employed by giants such as Google and Amazon
for complex data annotation tasks like image classification, translation
services, and analysing the customers’ attitudes.
Peer Review: The annotators, who reviewed the texts, would
check each other’s work to eliminate possible mistakes and variations.
Application: Quite popular in the studies proving different
theories, legal documents’ research, and various quality assurance checks.
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