Named Entity Recognition (NER)

What is Named Entity Recognition

Named entity recognition (NER) is a classification technology for use in artificial intelligence models that work with written language. NER goes beyond general classification by identifying individual terms within the passage and applying appropriate labels.

For example, take the sentence “Gailileo studied medicine at the University of Pisa in 1581.” A general text classification algorithm would recognize it as a historical fact. A named entity recognition model would parse “Galileo” as a person, “medicine” as a field of study, “University of Pisa” as a location, and “1581” as a date.

Simple NER implementations can look up terms in a dictionary or attempt pattern or context-based parsing. However, text passages in the real world often contain slang, typos, and spelling variations that require companies to update their list of terms regularly. And often the same word can take on multiple labels depending on the context.

A machine learning-based NER model is considerably more productive in practice and solves many of the weaknesses of dictionary-based approaches. Developers train the model with a pre-labelled document and then allow it to apply its predictions to new documents.

Use Cases For NER Models

In practical terms, an NER model is invaluable in language parsing as it extracts more meaning from written language. Businesses use it to retrieve details, summarize passages, and find relationships between similar terms. For instance,

  • Search engines can find more relevant results more quickly by parsing the search query with NER.
  • Customer service teams can categorize incoming support tickets by keyword and pick up details from written complaints and reviews.
  • Human resources can work with NER to process job applications and cover letters much more quickly.
  • Academic institutions use NER to help students search for more relevant research materials by parsing through key terms.
  • Social networks like YouTube can recommend new content to users according to their interests.

A few open-source NER APIs have arisen to meet this demand, including:

  • Stanford Named Entity Recognizer (SNER), a Java tool with pre-trained models for recognizing most people, places, businesses, and other entities.
  • Natural Language Toolkit (NLTK), a Python library for natural language processing.
  • SpaCy, a Python framework well-known for its ease of use and implementation.

Any situation where an automated system can extract meaning from natural text is an opportunity to apply NER models.

Current Challenges Facing the Development of NER Models

Because named entity recognition requires generating labels for multiple terms, it has a higher computational cost and takes more time to develop. Labeling entities correctly often requires manual intervention as well and the expertise from subject matter experts.

Some common issues facing named entity recognition in natural language processing (NLP) include the following.

  • Setting up data sets– Preparing the input data to train an NER model can be time-consuming. Sample passages must ideally be diverse and contain varying sentence structures and terms to ensure comprehensive training. And not everyone can agree on the correct label for a specific term sometimes.
  • Potential for errors– Language is complex, and a variety of errors are possible when parsing it. A model might misidentify a label or even attempt to label a word that does not require one. And because of the black-box nature of machine learning models, fixing errors in the algorithm isn’t always straightforward, requiring careful retraining and data collection.
  • Computational cost– The complexities of machine learning-powered NER increases the computational power required to converge a model.
  • Support for multiple languages– Larger organizations working in multiple regions might have to invest in multilingual named entity recognition to support various languages.

As discourse becomes more digital, NER models have access to an immense amount of raw data. Machine learning will play a crucial role in parsing all that input at scale.