How named entity recognition identifies and categorises proper names

Named entity recognition (NER) is a sub-discipline of computational linguistics that’s used to identify named entities (proper names) in a text and catalogue them based on certain parameters. The technique plays a particularly important role in the field of machine learning.

What is named entity recognition (NER)?

Named entity recognition (NER for short) is a discipline of computational linguistics that identifies proper names in texts and automatically assigns them to specific categories. The method is therefore also referred to as proper name recognition. Proper names or named entities are individual words or sequences of several words that describe a real-life entity. This can be, for example, a person, a company, an authority, an event, a place, a specific product or even a date.

The discipline is also used in the field of machine learning and artificial intelligence and originates from the field of Natural Language Processing (NLP), in which natural language is categorised and processed using algorithms, computers and fixed rules. Thanks to continuous further development, named entity recognition can now demonstrate convincing success rates in many languages and can barely be distinguished from identification by a human being.

How does named entity recognition work?

There are various methods for named entity recognition, which we’ll discuss in more detail later in this article. However, there are basically two important steps for each method that are particularly relevant to the success of the action.

Identification of proper names

This first involves the actual identification of one or more named entities. These are not just typical people’s names such as ‘Emily Williams’. Proper nouns such as ‘Lake Tahoe’, ‘Second World War’, ‘Porsche’, ‘Adirondack Mountains’, ‘Jurassic Park’ or ‘October 12, 1986’ are also considered named entities and can therefore be captured by named entity recognition. Once these proper nouns have been identified as such, their beginning and end are marked. This enables a system to recognise them within a natural text.

Categorisation of named entities

After identification, the marked proper names are assigned to defined categories. These include personal names, places, historical events, companies, authorities, products, dates or certain media titles and works of art. It’s important that named entity recognition recognises variants of an entity and that the previously established start and end points are correct.

What NER procedures are there?

While the two steps in named entity recognition must always be carried out, there are various procedures and methods for achieving the desired results. We’ll show you the four most common and, therefore, most successful approaches.

Analysis with dictionaries

In what’s probably the simplest method, the entities are compared with different dictionaries. As soon as there’s a match between a word or word sequence and a proper name in a dictionary, the entity is marked as a named entity and then assigned to the corresponding category.

Rule-based named entity recognition

Defined rules can also be used as a basis for named entity recognition. For this purpose, patterns are developed, which are compared with the existing texts. If there are matches, the entities are identified and categorised. The rule-based method is particularly suitable for certain specialist texts and not for general use.

Machine learning and AI

The best results are achieved with methods that use machine learning or AI as a basis. Data sets are used to train the corresponding systems. The recognition of statistical correlations plays a particularly important role here. Once the training is complete, the AI can search through unknown texts, recognise proper names and assign them to a category. The rule here is: the more comprehensive and balanced the training data, the better the subsequent results.

Hybrid of rule-based and AI-supported NER

A hybrid approach of rule-based and AI-supported named entity recognition can also provide very good results. Simple proper names are identified by the rule catalogue and more complex entities can be found and catalogued by artificial intelligence.

What applications does NER have?

There are numerous actual or conceivable future areas of application for named entity recognition. Here are some of the most important:

• Sentiment analysis: Named entity recognition is already being used to evaluate customer feedback and trends. For example, the AI identifies brand names, opinions on products or other reactions.
• Business intelligence: NER is used to convert unstructured texts into structured data. This can be used in the area of information retrieval and helps with the analysis of financial documents.
• Data annotation: Data annotation can be used to develop and train improved models for text translation, classification and analysis. named entity recognition plays an important role in this.
• Digital assistance: Named entity recognition is suitable for services such as chatbots or other digital assistants. It evaluates requests from users and can provide customised response options on that basis.
• Keywording: This method is used, for example, to filter people or places from different articles and then store them as meta information.
• Search engines: The method is used to evaluate and improve search algorithms. This enables search engines to provide even more relevant results.
• Neural networks: NER is also used in the field of long short-term memory (LSTM) and in comparable techniques.

What are the problems with named entity recognition?

Even though named entity recognition is developing rapidly and can already achieve impressive results, there are still some challenges with regard to the technology. In particular, the adaptation of trained models to specialist texts does not always lead to the desired results. This is especially true if the data for transfer learning is not sufficient or specific enough. Due to new entities, models often have to access insufficient amounts of data. Zero-Shot or Few-Shot approaches, which can also work with a smaller volume of data, offer a possible solution.