Every time we hear a word or read a text, we have the natural ability to identify and categorize the word into people, place, location, values, and more. Humans can quickly recognize a word, categorize it and understand the context. For example, when you hear the word ‘Steve Jobs,’ you can immediately think of at least three to four attributes and segregate the entity into categories,
- Person: Steve Jobs
- Company: Apple
- Location: California
Since computers don’t have this natural ability, they require our help to identify words or text and categorize them. It is where Named Entity Recognition (NER) comes into play.
Let’s get a brief understanding of NER and its relation to NLP.
What is Named Entity Recognition?
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.
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 meaning of words, text, and speech and identifying their relationships – Semantics
- Identifying and recognizing spoken words and transforming them into text – Speech
NER helps in the semantic part of NLP, extracting the meaning of words, identifying and locating them based on their relationships.
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,
Other categories include Numerical values, Expression, E-Mail Addresses, and Facility.
Ambiguity in Named Entity Recognition
The category a term belongs to is intuitively quite clear for human beings. However, that’s not the case with computers – they encounter classification problems. For example:
Manchester City (Organization) won the Premier League Trophy whereas in the following sentence the organization is used differently. Manchester City (Location) was a Textile and industrial Powerhouse.
Your NER model needs training data to conduct accurate entity extraction and classification. If you are training your model on Shakespearean English, needless to say, it won’t be able to decipher Instagram.
Different NER Approaches
The primary goal of a NER model is to label entities in text documents and categorize them. The following three approaches are generally used for this purpose. However, you can choose to combine one or more methods as well.
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The different approaches to creating NER systems are:
The dictionary-based system 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.
One drawback of using this approach is there is a need for constantly upgrading the vocabulary dataset for the effective functioning of the NER model.
In this approach, information is extracted based on a set of pre-set rules. There are two primary sets of rules used,
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.
Use Cases and Examples of Named Entity Recognition?
Unveiling the Versatility of Named Entity Recognition (NER):
- Chatbots: NER aids chatbots like OpenAI’s ChatGPT in understanding user queries by identifying key entities.
- Customer Support: It organizes customer feedback by product names, speeding up response times.
- Finance: NER extracts crucial data from financial reports, aiding in trend analysis and risk assessment.
- Healthcare: It pulls essential information from clinical records, promoting quicker data analysis.
- HR: It streamlines recruitment by summarizing applicant profiles and channeling employee feedback.
- News Providers: NER categorizes content into relevant information and trends, speeding up reporting.
- Recommendation Engines: Companies like Netflix employ NER to personalize recommendations based on user behavior.
- Search Engines: By categorizing web content, NER enhances search result accuracy.
- Sentiment Analysis: NER extracts brand mentions from reviews, fueling sentiment analysis tools.
Applications of NER
NER has several use cases in many fields related to Natural Language Processing and creating training datasets for machine learning and deep learning solutions. Some of the applications of NER are:
Streamlined Customer Support
A NER system can easily spot relevant customer complaints, queries, and feedback based on crucial information such as product names, specifications, branch locations, and more. The complaint or feedback is aptly classified and diverted to the correct department by filtering priority keywords.
Efficient Human Resources
NER helps Human Resource teams improve their hiring process and reduce the timelines by quickly summarizing applicants’ resumes. The NER tools can scan the resume and extract relevant information – name, age, address, qualification, college, and so forth.
Additionally, the HR department can also use NER tools to streamline the internal workflows by filtering employee complaints and forwarding them to the concerned departmental heads.
Simplified Content Classification
Content classification is a humongous task for news providers. Classifying the content into different categories makes it easier to discover, gain insights, identify trends, and understand the subjects. A Named Entity Recognition tool can come in handy for news providers. It can scan many articles, identify priority keywords, and extract information based on the persons, organization, location, and more.
Optimizing Search Engines
NER helps in simplifying and improving the speed and relevance of search results. Instead of running the search query for thousands of articles, a NER model can run the query once and save the results. So, based on the tags in the search query, the articles associated with the query can be quickly picked up.
Accurate Content recommendation
Several modern applications depend on NER tools to deliver an optimized and customized customer experience. For example, Netflix provides personalized recommendations based on user’s search and view history using named entity recognition.
Named Entity Recognition makes your machine learning models more efficient and reliable. However, you need quality training datasets for your models to work at their optimum level and achieve intended goals. All you need is an experienced service partner who can provide you with quality datasets ready to use. If that’s the case, Shaip is your best bet yet. Reach out to us for comprehensive NER datasets to help you develop efficient and advanced ML solutions for your AI models.
How Does Named-entity Recognition Work?
Delving into the realm of Named Entity Recognition (NER) unveils a systematic journey comprising several phases:
Initially, the textual data is dissected into smaller units, termed tokens, which can range from words to sentences. For example, the statement “Barack Obama was the president of the USA” is segmented into tokens like “Barack”, “Obama”, “was”, “the”, “president”, “of”, “the”, and “USA”.
Utilizing a concoction of linguistic guidelines and statistical methodologies, potential named entities are spotlighted. Recognizing patterns like capitalization in names (“Barack Obama”) or distinct formats (like dates) is crucial in this stage.
Post detection, entities are sorted into predefined categories such as “Person”, “Organization”, or “Location”. Machine learning models, nurtured on labeled datasets, often drive this classification. Here, “Barack Obama” is tagged as a “Person” and “USA” as a “Location”.
The prowess of NER systems is often amplified by evaluating the surrounding context. For instance, in the phrase “Washington witnessed a historic event”, the context helps discern “Washington” as a location rather than a person’s name.
Following the initial identification and classification, a post-evaluation refinement may ensue to hone the results. This stage could tackle ambiguities, fuse multi-token entities, or utilize knowledge bases to augment the entity data.
This delineated approach not only demystifies the core of NER but also optimizes the content for search engines, enhancing the visibility of the intricate process that NER embodies.
NER Benefits & Challenges?
- Information Extraction: NER identifies key data, aiding information retrieval.
- Content Organization: It helps categorize content, useful for databases and search engines.
- Enhanced User Experience: NER refines search outcomes and personalizes recommendations.
- Insightful Analysis: It facilitates sentiment analysis and trend detection.
- Automated Workflow: NER promotes automation, saving time and resources.
- Ambiguity Resolution: Struggles with distinguishing similar entities.
- Domain-Specific Adaptation: Resource-intensive across diverse domains.
- Language Dependency: Effectiveness varies with languages.
- Scarcity of Labeled Data: Needs large labeled datasets for training.
- Handling Unstructured Data: Requires advanced techniques.
- Performance Measurement: Accurate evaluation is complex.
- Real-Time Processing: Balancing speed with accuracy is challenging.