Have you ever used Google Translate to decipher writing in a language other than the one you speak? Or have you ever started a visual search for text printed on any object using Google Lens? If so, you have already experienced the advantages of text annotation, a crucial AI technique.
In, objects—whether they be found in papers, photographs, or other digital files—are tagged or labeled. Once these entities are tagged/labeled, they become comprehensible and can be deployed by AI/ML algorithms to train autonomous applications to perfection. It plays a key role in training computer vision AI/ML and Natural Language Processing (NLP) models by making a large volume of data usable and understandable to an algorithm.
Read along to know how that works.
| Table Of Content IntroductionText Annotation: Definition Text Annotation: Importance In Machine Learning Text Annotated: Process Text Annotation: Types Text Annotation: Use Cases The Bottom Line |
Text Annotation: Definition
Human language is complicated for machines. It consists of meanings, i.e., phrasal and text-based elements, sentiments, and emotions, with positive, negative, and neutral tones.
Unlike humans, machines cannot comprehend languages, read, speak, or understand contexts. At least not in the formative phase when the predictive model for autonomous application hasn’t been developed.
This is where text annotation comes into the picture.
Text annotation is a process of identifying and labeling text with relevant training data to define the characteristics of sentences. It ensures that computer vision AI/ML models get relevant training data to learn from.
Importance of Text Annotation In Machine Learning
Voice assistants, chatbots, and translators have made room for themselves in the modern tech-savvy world. Every enterprise wants to have its in-built AI voice assistant or similar autonomous application.
But with such massive competition, developing autonomous resources isn’t easy for enterprises. For accurate, responsive, and proactive automotive resources, they require-
- Ultra-modern Concepts
- Text datasets.
However, autonomous applications require more than text datasets. Text datasets, even when available in massive volumes, don’t do any good to autonomous applications as they don’t comprehend the context, style, meaning, and overtone in the first place. Text annotation, therefore, emerged as a revolutionary technology on this subject, where expert human annotators label/tag entities with training data.
Text annotation robust models let the machine understand the nuances of the speech/language and respond right to user queries. Moreover, text annotation is use-case-specific, allowing developers to create individual project-specific models (based on business requirements) with relevant training data.
The Process to Annotate Text
Language is very subjective!
The majority of companies choose to use human annotators to tag and label text data. Particularly in subjective speech/language, highly qualified, competent, and seasoned professional annotators offer considerable value. They also know about current speech patterns, which helps them comprehend slang, humor, sarcasm, and informal communication styles.
Here is a brief procedure:
Step 1: A collection of text or data is delivered to the human annotator with pre-set tags/ labels and detailed instructions for text annotation from the customer.
Step 2: Skilled human annotators match the appropriate tags and labels to the texts.
Step 3: The annotated texts are then sent into AI/ML algorithms to teach the computers how and when to label and tag texts, enabling them to make accurate predictions.
An accurate text annotation model can assist you in automating repetitive procedures in real time.
Types of Text Annotation Techniques
#1 Sentiment Annotation
A text annotation type- sentiment annotation is the assessment and tagging/labeling of feeling, emotion, belief, point of view, or sentiment inside a given text. The most difficult field of computer vision AI/ML- even for humans- is emotional intelligence (EI), the capacity to comprehend, perceive, utilize, manage, and handle emotions.
Machines have trouble understanding humor, sarcasm, or casual language. For instance, a human will comprehend the context and meaning of a sentence like “You nailed it!” by reading it (you did well). However, without human assistance, the machine will only comprehend the literal meaning.
Businesses can benefit from a sentiment annotation model’s ability to automatically identify the mood and emotions of-
- Clients’ Feedback
- Social Media Posts
- Messages
- Emails
- Reviews
#2 Text Classification
A text annotation type- text classification categorizes text body based on predetermined brackets. Text classification, also referred to as text tagging or text categorization, guarantees accurate text organization in cogent groups.
There are two types-
- Product Categorization
The product listing services for ease of research and better user experience. It is imperative in industries like eCommerce, where each product is classified based on the departments the eStore has provided.
For example, a woman dresses in the “Ladies Section” and in “Clothing & Apparels.
- Document Classification
The classification of documents based on predetermined tags for better organization, management, and documentation recall. For example, a banking department wants to classify its documents into separate groups: loans, members, prospects, FDs, RDs, etc.
#3 Entity Annotation
A text annotation type- Entity Annotation is finding, extracting, and labeling/tagging particular entities inside a text body. It is a crucial technique for obtaining pertinent information from text-based documentation. For example, in NLP, entity extraction for deep learning assigns labels or tags to entities like name, place, time, and organization so that machines can comprehend the important text.
Below are its three types-
⇒ Named Entity Recognition
Using name tags to annotate entities (e.g., name, place, location, organization, etc.). Usually, this entity type is used to develop a specific system (such as Named-entity recognition) to help locate particular terms in documents.
⇒ Language Filters
Using linguistic filters to verify the accuracy of the speech. For instance, an organization would seek to classify/label vulgar language as an expletive. This way, companies will find it simpler to identify by whom, when, and where the offensive language was used.
⇒ Part-of-speech Tagging
Text annotation of elements of speech (e.g., adjectives, subject, noun, pronoun, etc.)
#4 Entity Linking
A text annotation type: Entity linking is a method of connecting entities to build a large and more comprehensive repository. Entity linking is further partied as entity disambiguation- combining names and entities with subsist data, and uninterrupted linking, entity analysis, and disambiguation combined.
Use Cases for Text Annotation
Text Annotation is beneficial in-
#1 Healthcare
Text annotation has emerged as a game-changer in the healthcare industry. It has replaced old file systems and hand-operated processes with high-achieving models. In particular, it impacts the following proceedings-
- Automatic Data Extraction From Manual Records (File System)
- Medical Data Classification For Better Access
- Data Categorization For Ease Of Research
- Analyzing Patient Records
- Improved Medical Condition Detection
- Identification of Medically Insured Patients
#2 Banking
The text annotation benefits in the banking industry are manifolds. Increased personalization, higher mechanization, decreased error percentage, and appropriate resource use are all possible with text annotation through-
- Recognition of Tax Evasion And Fraud Patterns
- Extraction And Management of Custom-built Data
- Extraction of Banking Data: Loan Rates, Credit Scores, & Other Elements
#3 Insurance
Insurance is one of the industries best placed to reap the benefits of new advancements- automating a wide range of processes and services, creating significant cost and time efficiencies.
- Data Extraction From Contextual Records
- Risk Evaluation
- Recognition of Parties Involved
- Documents Monitoring
- Identification of Dubious Claims
- Claims Fraud Detection
#4 Media & Communication
In the media and communication industry, text annotation automates substantial manually operated work, especially in the following areas-
- Precise Issue Prediction
- Network Performance Optimization
- Automotive Responses (Chatbots)
- In-depth Analysis of Network Interactions
- Understanding Client Intent & Sentiments
- Detection of Malicious Practices
- Personalized Promotion
- Analysis of Customer Behavior
The Bottom Line
Undoubtedly, text annotation has a significant role in machine learning. Enterprises looking to create autonomous applications with built-in natural language generation and understanding must, however, outsource data entry services to a reputable business where professional data annotators can assist you in creating project-specific training data in real-time while overcoming data bias.
