Named Entity Recognition (NER) in Natural Language Processing

What is Named Entity Recognition (NER)?

Named entity recognition (NER) is a vital sub-task of natural language processing (NLP). The objective of NER is to identify and classify named entities in text data. NER is classified into predefined categories such as person, organization, location, dates, percentages, etc. NER helps with information extraction, text understanding, and document summarization. NER models empower organizations to extract valuable insights, automate information retrieval, improve search functionality, etc.

NER Categorization

Following are the primary categories of NER.

• Persons: Names of people
• Organizations: companies, government bodies, political groups
• Locations: names of places, including cities, countries, monuments, etc.
• Dates: Specific dates such as years, months, and dates
• Numbers: numerical values such as percentages, currencies, measurements, etc.
• Miscellaneous: miscellaneous named entities such as product names, event titles, skills, etc.

Importance of NER

• Information Extraction: Extracting names of people, organizations, locations, etc.
• Question Answering: Chatbots identify entities mentioned in user queries and retrieve relevant information.
• Document Summarization: Helpful in identifying and highlighting key named entities
• Sentiment Analysis: By understanding the organization and which products are discussed in customer reviews

Techniques in NER

• Rule-based NER: These systems rely on predefined patterns, regular expressions, or dictionaries to identify named entities.
• Statistical NER: These models use ML algorithms such as conditional random fields (CRF) and hidden Markov models (HMM). This model requires labelled training data for learning.
• Deep learning-based RNNs and transformers have gained popularity for their ability to capture contextual information and achieve state-of-the-art results.

Challenges in NER

• Ambiguity: Text data often contains ambiguous references to entities, which makes it difficult to define the correct category.
• Named Entity Variability: Various forms of entities can exist, such as abbreviations, misspellings, synonyms, etc.
• Domain Specificity: NER models perform differently based on domains with unique vocabularies and contexts.

Applications of NER

• Healthcare: NER is used to extract medical entities like patient names, diseases, and treatment information from electronic health records.
• Finance: NER is used to identify entities such as company names, stock symbols, and financial metrics from reports and news articles.
• Legal: NER assists in recognizing legal entities, case names, and references to legal documents in legal texts.