Data annotation and Labeling are at the root of creating not only AI models that are adaptable but also ML ones that can perform these tasks by themselves to improve their functionalities. These characteristics have made them a mainstay of businesses already, with over US$ 629.5 Million going into annotation and labeling tools in 2021. That investment is expected to grow at a staggering rate of 26.6% between 2022 to 2030. This makes them an inevitable choice for businesses across scales and industries.
If you’re wondering how you can adopt them for your business, and what benefits they can bring to your AI/ML usage, then continue to read this blog.
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What Is Data Annotation and Labeling?
Data annotation is the process of adding metadata to data. The purpose of this is to provide information on how and why a particular piece of information was collected so that it can be used by machine learning algorithms in more intelligent ways. Data annotation will not just help you understand your data better but also make it easier for other people who might have access to it.
Data labeling is not much different from data annotation; it consists of adding tags to objects in raw data (like images and videos) to help ML models identify them to make predictions and estimations. An example is security AI recognizing the object a person is carrying to discern if they are a threat or not.
Data Annotation Types And How They Help Create Smart Projects
Annotation can be broadly classified into Manual and Automated types. Annotation experts carefully study the data and apply techniques suitable to the project’s goals. They produce two kinds of annotated data sets based on purpose training and test.
Several annotation techniques are in use depending on the type of data and the requirement. The three main types of data are image (and by extension, video), audio, and text.
These are techniques applied to visual data to train and test ML and AI models to recognize the same and identify various objects in their environment (called computer vision).
Consists of tagging images/frames and grouping them under various classes to help algorithms recognize the entirety of the image or frame. It’s the most basic form of classification, helping the AI detect similar images/frames in a data set and with data abstraction.
- Object Detection and Recognition
It’s similar to classification but goes more in-depth to discover more information about the target subject like location, size, etc. It uses boundaries for the purpose and can detect multiple classes of objects in an image.
Creates multiple segments in an image or frame by defining the target objects on a pixel level. It’s the most complex and accurate of the three types and has the following sub-types:
Helps algorithms develop context by aiding in distinguishing similar objects in an image/frame. Also gives further information about them like presence, shape, etc.
Recognizes the presence of objects and their location in the image beside the information from the previous sub-type. Helps filter out unwanted information.
A hybridized version of the above two subtypes. Helps algorithms identify both the target subjects/objects and the background.
- Boundary Identification
Usually used in the other types of annotation, it helps create boundaries to separate various items in an image for identification purposes. It is also used to automate annotation where algorithms recognize linear objects using lines and curves.
The annotation technique is used to handle speech and voice similarly to humans. Various audio annotation approaches can be used depending on the objectives of a project.
- Sound Labeling
It involves specialists selecting the necessary sounds from an audio data set and labeling them. It’s a method for finding and extracting words and phrases from samples of audio data.
- Event Tracking
It aids in assessing the system’s effectiveness in multi-source audio data settings that closely mirror real-world circumstances with overlapping sounds.
- Speech-to-Text Transcription
Key elements of speech including words, sounds, and punctuation are carefully documented, and pertinent terms are annotated.
- Audio Classification
It involves listening to and analyzing audio data using an algorithm to distinguish noises and spoken instructions. It is fundamental to develop programs for text-to-speech, automatic voice recognition, and virtual assistants. It is available in the following types:
- Acoustic Data Classification
Helps pinpoint the location of the recording like halls, stone corridors, rooms, the outdoors, etc. It serves a purpose in sound library upkeep and system monitoring.
- Classification of Music
Various musical genres, instrumentation, ensembles, etc. are sorted into their appropriate categories to enhance suggestions and organize music libraries.
- Classification of Natural Spoken Language
Enables chatbots, virtual assistants, and related technology to comprehend human speech more accurately by putting dialect, semantics, inflections, and other such features into categories.
Trains AI/ML models to identify textual data targets in a data set. It may occasionally be used in conjunction with a Voice/Audio Annotation tool, like with Natural Language Processing (NLP). Several text annotation approaches are in use:
- Entity Annotation
Locates, extracts and labels target entities in text for chatbots that employ NLP models to assist them in recognizing speech components, notably named entities and keywords/phrases. Entity linking is paired with entity annotation to improve the results.
Three different types exist:
- Named Entity Recognition (NER): This technique involves giving proper names to entities.
- Keyphrase Tagging: Identifying and labeling keyphrases or keywords in collections of text.
- Part-of-Speech (POS) Tagging: Adjectives, nouns, adverbs, and other functional speech elements are identified and annotated.
- Entities Linking
This procedure links the entities found and annotated during entity annotation to sizable data repositories. It helps search engine algorithms enhance their search capabilities and deliver more precise results. Labeled entities are linked to URLs that provide extra information about them. It comes in two varieties:
- Disambiguation: involves connecting them to databases that have information about them.
- End-to-end: Entities are analyzed, annotated, and engaged within a textual data collection (also known as entity recognition) along with entity disambiguation.
- Text Classification
Also known as document classification and text categorization. Annotators analyze a body of text or a few lines of text to establish its subject, intent, and sentiment before categorizing it according to a set of predetermined categories. Used when a text body needs single label annotation.
The following are its subcategories:
- Document classification: is used to sort documents and recall textual material from them.
- Product categorization: Most useful for eCommerce platforms. Aids the organization of goods and services into categories and intuitive classes.
- Sentiment Annotation: Labels the selected segment based on the sentiment, emotion, or opinion contained in the text data when classifying it.
- Annotation in Linguistics
Also called corpus annotation. Used to tag grammatical, phonetic, or semantic components of the text or audio. Utilized in NLP solutions such as chatbots, voice-activated search engines, virtual assistants, machine translation, etc. It comes in four different forms:
- Discourse annotation: Connects anaphors and metaphors with their appropriate antecedent and postcedent subjects.
- Semantic annotation: Word definition annotation.
- Phonetic annotation: Labels a speech’s natural pauses, intonation, and stress components.
- POS Tagging: The annotation of various functional terms in texts.
Practices That Give The Best Data Annotation and Labeling Results
It is important to pay attention to how a complex process like data annotation and labeling is being implemented. Otherwise, the outcomes won’t match what was anticipated. Here are some of the top ways to stop it:
- Gather a variety of data. Images should, for instance, show the same thing from several perspectives and lighting conditions. It assists in overcoming bias and keeps the algorithm from being perplexed.
- Additionally, the information gathered should be precise—that is, it should be about the intended subject and nothing else that resembles it. It enhances precision.
- To guarantee that the annotation/labeling quality is high, a thorough QA procedure should be implemented. Numerous techniques may be used, such as task auditing, targeting, and random QA.
- Adhere to a well-thought-out annotation guideline that outlines annotation and tool usage guidelines in detail. If required, provide examples when creating labels.
- Reduce expenses and delivery times by creating a smooth, effective pipeline for your project.
- Maintain open lines of communication through a variety of activities and channels with all parties involved.
- To test how well your annotation setup works, run a pilot. Examine the results and take into account any criticism you receive to keep refining the procedure until you achieve the best outcomes.
Data annotation and labeling are, thus, invaluable investments that form the base of today’s business progress. Using the above-mentioned best practices and project-applicable techniques, you can gain the smart AI/ML system you need to up your profits and brand value by improving efficiency throughout the organization.