Named Entity Recognition Annotation Experts
Unlock critical information in unstructured data with entity extraction in NLP
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Empowering teams to build world-leading AI products.
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 Case
- 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?
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.
Want to build your own NER training data?
Contact us now to learn how we can collect a custom NER dataset for your unique AI/ML solution
Frequently Asked Questions (FAQ)
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.
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
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,
The different approaches to creating NER systems are:
Dictionary-based systems
Rule-based systems
Machine learning-based systems
Streamlined Customer Support
Efficient Human Resources
Simplified Content Classification
Optimizing Search Engines
Accurate Content recommendation
