Named Entity Recognition: Identifying Entities in Text Data
In computer science and natural language processing, named entity recognition (NER) is concerned with the recognition and categorization of named entities in text. The objective of NER is to automatically extract data insights from unstructured text, such as names of individuals, companies, places, etc.
A natural language processing technique called name entity recognition (NER) is used to extract data from text. A key component of NER is the identification and classification of named entities—important textual information. A text’s main subjects, such as individuals, places, businesses, events, and goods, as well as themes, topics, times, monetary amounts, and percentages, are all examples of named entities.
In addition to entity extraction and identification, NER is also known as e-chunking. Machine learning (ML), deep learning, and neural networks are just a few of the applications for it in the field of artificial intelligence (AI). In NLP systems, including chatbots, sentiment analysis tools, and search engines, NER is a crucial component.
Every circumstance where a high-level overview of a huge text is required is best served by named entity recognition algorithms. With the help of NER, you may quickly scan a big body of literature and comprehend its topic or theme. Several scenarios have use cases for it. Below are some examples:
- Customer support
NER reduces response times by classifying user requests, grievances, and queries filtered by particular keywords.
It reduces workloads and improves the quality of patient care by assisting doctors in swiftly understanding reports by extracting key information.
- Search Engines
Search queries and other materials are analyzed to increase the speed and relevance of search results.
- Human Resources
Grouping employee grievances and inquiries enhances internal workflows. Also, condensing resumes hastens the hiring process.
The easiest NER technique is this one. The vocabulary utilized in this method is taken from a dictionary. Simple string-matching algorithms compare the given text to the vocabulary items to determine whether the entity is present. Because the dictionary must be updated and maintained regularly, this method is typically not used.
This approach uses a pre-established collection of pattern- and context-based information extraction rules. In contrast to context-based rules, which use the word’s context in the text document, pattern-based rules employ the morphological patterns of the words.
- Machine Learning-based
A lot of the problems with the first two methods are resolved by this one. It is a statistically grounded model that seeks to produce a feature-based representation of the observed data. Even with subtle spelling differences, it can identify an existing entity name.
For doing NER, the machine learning-based technique requires two parts. On annotated documents, the ML model is first trained. The trained model is then used to annotate the unprocessed documents in the following stage. The procedure resembles a typical machine learning model workflow.
- Using Deep Learning
In-depth learning Because NER can put words together, it can better understand the semantic and syntactic relationships between different words, making it more accurate than the ML-based approach. Additionally, it has the ability to automatically analyze high-level and topic-specific terms.
So, this was all you needed to know about NER. Being a subsidiary of Sambodhi Research and Communications Pvt. Ltd., Education Nest is a global knowledge exchange platform that empowers learners with data-driven decision making skills.
Enroll in our comprehensive courses to dig deep into the vast field of NLP. Connect with us to explore more about our services today!