What is named-entity recognition (NER) in NLP?

Named-entity recognition (NER) is the subtask of natural language processing. We, as humans, are able to comprehend language i.e. figure out the nouns, pronouns, and adverbs. This allows us to capture the semantic syntactic meaning of phrases. But how do we pass this information onto machines?

Natural language processing is the process of making machines understand human language. NER is a subtask of NLP that aims to identify and classify named entities in text into predefined categories such as person names, organization names, locations, dates, and more.

NER basics

At its heart, NER is just a two-step process, as mentioned below.

1. Detecting entities.

2. Classifying them into different categories.

Some important categories in the NER architecture are :

• Person.

• Organization.

• Place/ location.

• Other common categories include date/time, Numeral measurement (money, percent, weights).

let's explore NER with an example.

Example

Here's an example to illustrate NER in action.

In the example, the NER system identifies and classifies the named entities in the text as follows:

• Named entity: "Apple Inc." | Entity type: Organization

• Named entity: "Steve Jobs" | Entity type: Person

• Named entity: "April 1976" | Entity type: Date

• Named entity: "Cupertino, California" | Entity type: Location

NER techniques

NER techniques can be broadly categorized into rule-based approaches, dictionary-based models, and neural network-based models.

1. Rule-based approaches: Rule-based techniques rely on handcrafted rules and patterns to identify named entities in text. These rules can be based on regular expressions, syntactic patterns, or specific domain knowledge. Rule-based approaches are often used for simple and well-defined entity types but can be limited in handling complex and diverse text.

2. Dictionary-based systems: They use a dictionary with an extensive vocabulary and synonym collection to cross-check and identify named entities. This method may face difficulty when classifying named entities with variations in spellings.

3. Supervised machine learning-based systems: It employs ML models trained on texts pre-labeled with named entity categories by humans. Supervised machine learning approaches employ complex statistical language models such as conditional random fields and maximum entropy.

Note: spaCy and NLTK (Natural Language Toolkit) are popular libraries in Python for natural language processing (NLP) tasks, including named entity recognition (NER).

NER applications

Named entity recognition (NER) finds valuable applications in various domains, including customer service, human resources, recommendation systems, and chatbots. Here's how NER can be utilized in each of these areas:

1. Recommendation systems: NER can identify named entities from user profiles or historical interactions to generate personalized recommendations. By understanding user preferences, such as favorite genres, authors, or brands, NER enables recommendation systems to suggest relevant items or content tailored to individual users.

2. Customer service: NER can extract important entities from customer queries or support tickets, such as product names, service categories, or specific issues. This helps in understanding customer needs and routing inquiries to the appropriate department or support team.

3. Chatbots and virtual assistants: NER is used in conversational AI systems like chatbots and virtual assistants to understand user queries, extract important entities, and provide appropriate responses. By recognizing named entities, these systems can offer personalized and relevant information.

4. Human resource (HR): NER can extract relevant information from resumes or CVs, such as candidate names, contact details, education history, work experience, and skills. This streamlines the recruitment process by automatically populating candidate profiles or matching candidate skills with job requirements.

Note: NER may face difficulty in analyzing lexical ambiguities, semantics, and evolving usages of language in text. It can run into problems with spelling variations too.

Remarks

In conclusion, NER plays a crucial role in various NLP applications, including information extraction, document classification, question answering, chatbots, sentiment analysis, and recommendation systems. However, NER also presents several challenges, such as entity ambiguity, scalability, handling rare entities, entity boundary detection, context awareness, and domain-specific entities.