Named Entity Recognition Annotation Experts

Human Powered Entity Extraction / Recognition to train NLP models

Unlock critical information in unstructured data with entity extraction in NLP

Featured Clients

Empowering teams to build world-leading AI products.

There’s an increasing demand to analyze unstructured data to uncover undiscovered insights.

Looking at the speed at which the data is generated; of which 80% is unstructured, there is a need on ground to use next-gen technologies to analyze the data effectively and gain meaningful insights for making better decisions. Named Entity Recognition (NER) in NLP primarily focuses on processing unstructured data and classifying these named entities into predefined categories.

IDC, Analyst Firm:

The worldwide installed base of storage capacity will reach 11.7 zettabytes in 2023

IBM, Gartner & IDC:

80% of the data around the world is unstructured, making it obsolete and unusable.

What is NER

Analyze data to discover meaningful insights

Named Entity Recognition (NER), identifies and classifies entities such as people, organizations, and locations within unstructured text. NER enhances data extraction, simplifies information retrieval, and powers advanced AI applications, making it a vital tool for businesses to leverage. With NER, organizations can gain valuable insights, improve customer experiences, and streamline processes.

Shaip NER is designed to allow organizations to unlock critical information in unstructured data & lets you discover relationships among entities from financial statements, insurance documents, reviews, physician notes, etc. With rich experience in NLP & linguistics, we are well equipped to deliver domain-specific insights to handle annotation projects of any scale

NER Approaches

The primary goal of a NER model is to label or tag entities in text documents and categorize them for deep learning. The following three approaches are generally used for this purpose. However, you can choose to combine one or more methods as well. The different approaches to creating NER systems are:

Dictionary-based
systems

This is perhaps the most simple and fundamental NER approach. It will use a dictionary with many words, synonyms, and vocabulary collection. The system will check whether a particular entity present in the text is also available in the vocabulary. By using a string-matching algorithm, a cross-checking of entities is performed. There is a need for constantly upgrading the vocabulary dataset for the effective functioning of NER model.

Rule-based
systems

Information extraction based on a set of pre-set rules, which are

Pattern-based rules – As the name suggests, a pattern-based rule follows a morphological pattern or string of words used in the document.

Context-based rules – Context-based rules depend on the meaning or the context of the word in the document.

Machine learning-based systems

In Machine learning-based systems, statistical modeling is used to detect entities. A feature-based representation of the text document is used in this approach. You can overcome several drawbacks of the first two approaches since the model can recognize entity types despite slight variations in their spellings for deep learning.

How we can help

General NER
Medical NER
PII Annotation
PHI Annotation
Key Phrase Annotation
Incident Annotation

Applications of NER

Streamlined Customer Support
Efficient Human Resources
Simplified Content Classification
Improve patient care
Optimizing Search Engines
Accurate Content recommendation

Use Cases

Information Extraction & Recognition Systems
Question-Answer Systems
Machine Translation Systems
Automatic Summarizing Systems
Semantic Annotation

NER Annotation Process

NER annotation process generally differs to a client’s requirement but it majorly involves:

Phase 1: Technical domain expertise (Understanding project scope & annotation guidelines)

Phase 2: Training appropriate resources for the project

Phase 3: Feedback cycle and QA of the annotated documents

Our Expertise

1. Named Entity Recognition (NER)

Named Entity Recognition in Machine Learning is a part of Natural Language Processing. The primary objective of NER is to process structured and unstructured data and classify these named entities into predefined categories. Some common categories include name, location, company, time, monetary values, events, and more.

1.1 General Domain

Identification of people, place, organization etc. in the general domain

1.2 Insurance Domain

It involves extraction of entities in insurance documents such as

Sums insured
Limits of Indemnity/policy limits
Estimates such as wage roll, turnover, fee income, exports/imports
Vehicle schedules
Policy extensions and inner limits

1.3 Clinical Domain / Medical NER

Identification of problem, anatomical structure, medicine, procedure from medical records such as EHRs; are usually unstructured in nature and require additional processing to extract structured information. This is often complex and requires domain experts from healthcare to extract relevant entities.

2. Key phrase Annotation (KP)

It identifies a discrete noun phrase in a text. A noun phrase may be either simple (e.g. single head word like noun, proper noun or pronoun) or complex (e.g. a noun phrase that has a head word along with its associated modifiers)

3. PII Annotation

PII refers to Personally Identifiable Information. This task involves annotation of any key identifiers which can relate back to a person’s identity.

4. PHI Annotation

PHI refers to Protected Health Information. This task involves annotation of 18 key patient identifiers as identified under HIPAA, in order to de-identify a patient record/identity.

5. Incident Annotation

Identification of information like who, what, when, where about an event e.g. Attack, kidnapping, Investment etc. This annotation process has following steps:

5.1. Entity Identification (e.g. Person, place, organization, etc.)

5.2. Identification of word denoting the main incident (i.e. trigger word)

5.3. Identification of relation between a trigger and entity types

Why Shaip?

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Dedicate Team

It is estimated that data scientists spend over 80% of their time in data preparation. With outsourcing, your team can focus on the development of robust algorithms, leaving the tedious part of collecting the named entity recognition datasets to us.

Scalability

An average ML model would require collection and tagging large chunks of named datasets, which requires companies to pull in resources from other teams. With partners like us, we offer domain experts which can be easily scaled as your business grows.

Better Quality

Dedicated domain experts, who annotate day-in and day-out will – any day – do a superior job when compared to a team, that needs to accommodate annotation tasks in their busy schedules. Needless to say, it results in better output.

Operational Excellence

Our proven data quality assurance process, technology validations, and multiple stages of QA, helps us deliver best-in-class quality that ofen exceeds expectations.

Security with Privacy

We are certified for maintaining the highest standards of data security with privacy while working with our clients to ensure confidentiality

Competitive Pricing

As experts in curating, training, and managing teams of skilled workers, we can ensure projects are delivered within budget.

Availability & Delivery

High network up-time & on-time delivery of data, services & solutions.

Global Workforce

With a pool of onshore & offshore resources, we can build and scale teams as required for various use cases.

People, Process & Platform

With the combination of a global workforce, robust platform, & operational processes designed by 6 sigma black-belts, Shaip helps launch the most challenging AI initiatives.

Recommended Resources

Blog

Named Entity Recognition (NER) – The Concept, Types

Named Entity Recognition (NER) helps you develop top-notch machine learning & NLP models. Learn NER use-cases, examples, & a lot more in this super-informative post.

Solutions

Human-Powered Medical Data Annotation

80% of data in the healthcare domain is unstructured, making it inaccessible. Accessing the data requires significant manual intervention, which limits the quantity of usable data.

Blog

Text Annotation in Machine Learning: A Comprehensive Guide

Text annotation in machine learning refers to adding metadata or labels to raw textual data to create structured datasets for training, evaluating, and improving machine learning models.

Creating clinical NLP is a critical task that requires tremendous domain expertise to solve. I can clearly see that you are several years ahead of Google in this area. I want to work with you and scale you.

Google, Inc. Director

My engineering team worked with Shaip’s team for 2+ years during the development of healthcare speech APIs. We have been impressed with their work done in healthcare-specific NLP and what they are able to achieve with complex datasets.

Google, Inc. Head of Engineering

Want to build your own NER training data?

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Frequently Asked Questions (FAQ)

1. What is Named Entity Recognition in NLP?

Named Entity Recognition is a part of Natural Language Processing. The primary objective of NER is to process structured and unstructured data and classify these named entities into predefined categories. Some common categories include name, location, company, time, monetary values, events, and more.

In a nutshell, NER deals with:

Named entity recognition/detection – Identifying a word or series of words in a document.

Named entity classification – Classifying every detected entity into predefined categories.

2. But how is NER related to NLP?

Natural Language processing helps develop intelligent machines capable of extracting meaning from speech and text. Machine Learning helps these intelligent systems continue learning by training on large amounts of natural language data sets. Generally, NLP consists of three major categories:

Understanding the structure and rules of the language – Syntax

Deriving the meaning of words, text, and speech and identifying their relationships – Semantics

Identifying and recognizing spoken words and transforming them into text – Speech

3. Common Examples of NER?

Some of the common examples of a predetermined entity categorization are:

Person: Michael Jackson, Oprah Winfrey, Barack Obama, Susan Sarandon

Location: Canada, Honolulu, Bangkok, Brazil, Cambridge

Organization: Samsung, Disney, Yale University, Google

Time: 15.35, 12 PM,

4. Different NER Approaches?

The different approaches to creating NER systems are:

Dictionary-based systems

Rule-based systems

Machine learning-based systems

5. What are the different applications of NER?

Streamlined Customer Support

Efficient Human Resources

Simplified Content Classification

Optimizing Search Engines

Accurate Content recommendation

Named Entity Recognition Annotation Experts

Featured Clients

IDC, Analyst Firm:

IBM, Gartner & IDC:

What is NER

Analyze data to discover meaningful insights

NER Approaches

Dictionary-basedsystems

Rule-basedsystems

Machine learning-based systems

How we can help

Applications of NER

Use Cases

NER Annotation Process

Our Expertise

1. Named Entity Recognition (NER)

2. Key phrase Annotation (KP)

3. PII Annotation

4. PHI Annotation

5. Incident Annotation

Why Shaip?

Dedicate Team

Scalability​

Better Quality

Operational Excellence

Security with Privacy

Competitive Pricing

Availability & Delivery

Global Workforce

People, Process & Platform

Recommended Resources

Blog

Named Entity Recognition (NER) – The Concept, Types

Solutions

Human-Powered Medical Data Annotation

Blog

Text Annotation in Machine Learning: A Comprehensive Guide

Want to build your own NER training data?

Frequently Asked Questions (FAQ)

AI Data Services

Speciality

Industry

Products

Company

Resources

Contact Us

Dictionary-based
systems

Rule-based
systems

Scalability