How Text Annotation Plays an Important Role for 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
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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
How to Annotate Text Data Accurately Cost
Effectively?

• Based on the complexity of your project decide on
  the approach.

In-house
May not be cost-effective if you dont 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
Name Entity Reorganization Classification
Comments Feedback Annotation
Social Media Post Annotation
Semantic Annotation
Reach out to HabileData to fuel your ML models
with accurately annotated text
www.habiledata.com info_at_habiledata.com