Developing effective ML models require text annotation. High-quality text annotation is the need for the machines to catch the finer nuances of the language and respond better to user queries. Here is the complete overview of why text annotation is important in developing ML models.

1. How Text Annotation is
Important in Developing
ML Models
Annotated data is critical for accurate understanding and detection by AI and ML models.

2. Why is Text Annotation Important for
Developing ML Models?
 Text annotation helps machine learning models accurately
understand contextual conversations, situations, sentiments, etc. by:
• Highlighting parts of speech in a sentence, grammar syntax,
keywords, phrases, and more
• By better mimicking human conversations
 Accurate and fast paced text annotation helps build scalable and high
performing ML models

3. Techniques of Text Annotation
Named Entity Recognition
Assigns labels to words or phrases
within a text from predefined
categories.
Entity Linking
Assigns a unique identity to entities
such as locations, companies or
famous individuals mentioned in text.
Sentiment Annotation
Evaluates attitudes and emotions
behind a text by labeling that text as
positive, negative, or neutral.
Intent Annotation
Analyzes the intent behind a text,
classifying it into categories, like request,
command, or confirmation.
Semantic Annotation
Attaches additional information to words and
phrases that further explain user intent or
domain-specific definitions.

4. Applications of Text Annotation
Customer
service
Used in chatbots
and other
automated
processes ensuring
machine
understands the
queries, comments,
complaints etc.
Screening
processes
Helps in recruitment
process by
identifying
keywords, skills and
experience within
user profiles
Medical
Records
Used in processing
patient records such
as classifying
documents, filing
patient records and
amplifying medical
research

5. Applications of Text Annotation
Brand Social
Listening
Social media posts
is analyzed to help
brands understand
customer opinion
and strategize
accordingly
Customer
Insights
Companies
understand sentiment
behind customer
interactions, including
reviews, emails and
other comments
Brand Social
Listening
Social media posts
is analyzed to help
brands understand
customer opinion
and strategize
accordingly

6. Based on the complexity of your project decide on the approach.
How to Annotate Text Data Accurately & Cost Effectively?
In-house
May not be cost-effective if you don’t have infrastructure & experts in place
Crowdsourcing
Gives you access to experts from across the globe to
work on a particular task
Outsourcing
A great option where you hire experts for your labeling project. You have
better control over your project as you build a team that works as per your
specifications providing technology-enabled text annotation solutions.
Outsource In-house Crowdsource
Time required Low High Low
Price Low Expensive Cheap
Quality of annotation High Average Low
Security High Average Low

7. IF YOU ARE LOOKING FOR
Text
Classification &
Categorization
Text Annotation
for Sentiment
Analysis
Text Annotation
for NLP Machine
Learning
Comments &
Feedback
Annotation
Social Media
Post Annotation
Name Entity
Reorganization
& Classification
Semantic
Annotation
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Reach out to HabileData to fuel your ML models with accurately annotated text