Named Entity Recognition (NER) - Voice of the Customer Platform

Named Entity Recognition (NER)

Artificial Intelligence is the technology that aims to make machines have intelligence like human beings. As an Artificial Intelligence task, Natural Language Processing (NLP) aims to process, understand, and analyze natural languages by machines. A natural language is any language that has evolved naturally in humans through use and repetition without conscious planning or premeditation. Named Entity Recognition is a study of NLP, and we try to clarify it in this blog post. Hope you will like it!

Named Entity Recognition is the process of determining and categorizing entities in texts by machines. Entities fall into categories that are specially defined according to the task and maybe words or groups of words representing person, location, time, etc.

The NER model detects the entities in the text and classifies them into related categories.

As the NER model notices the entities, the system gets more information about the meaning of the text. Because being aware of what is talked about helps to understand the told. Systems have both intelligence and information. Therefore, they have the competence to give results.

A text-understanding system only can be in progress by Natural Language Processing. It is a system that learns the intelligence of understanding a natural language.

NER helps extract information in the texts as a study of NLP, and that study calls “Information Extraction”. Information Extraction aims to extract structured information from unstructured texts.

NER extracts information in two steps;

1- Identifying the Entities

Artificial Intelligence works by two important complementary: Programming and Training Data. NER models need to learn “which words are relevant and how to categorize them” by familiarizing many different texts to notice entities in any text. In this way, the NER model learns what the entities’ points are in texts.

2- Categorizing the Entities

To categorize the entities, we have to specify the categories first. Categories can be;

Person: E.g., Mustafa Kemal Atatürk, Nikola Tesla

Organization: E.g., Artiwise, Google

Location: E.g., İstanbul, Times Square

Time: E.g., Spring, 02.00 p.m.

Institution: E.g., the Institute of Architects, University of Oxford

It is important to specify the categories in view of your data and the information you want to extract. Considering that a NER model that only knows banking sector entities probably can not be familiar with healthcare.

After specifying the categories, related data should be marked up, and an algorithm should be trained. In this way, the NER model will be able to give results in the field of your wants.

As taking outputs from the NER model, you can;

  • Classify data without reading
  • See any entity told in data in the field of predefined categories
  • Find related data easily


Challenges of NER

Natural languages evolve naturally and unconsciously. Therefore, there may be complexities in texts to detect entities.

  • Detecting a certain entity in a text which may contain another entity

“Istanbul Technical University” specifies a single entity, disregarding that “Istanbul” is another entity. NER model can understand the discourses in texts thanks to NLP and can handle nested entities, as well.

  • Detecting correct types of entities that have a common lexicon with multiple entity types

The word which is marked as Institution is the University of Kocaeli, not Kocaeli as a city. NER models can extract types of entities from sentence structure.

  • Detecting correct types of entities which include homonym phrases

“The band” in these sentences are not from the same entity type. NER models use Relation Extraction to detect entities from the overall meaning of the text.


Fields of Usage


NER can detect organizations, famous people, individuals, currency, etc., in the news. Search engines may not be functional, so if you need to find an entity in the news, you probably have to read them first, in case of homonyms, etc. NER finds the entities according to related categories; this means you can easily reach and list related news.


Chatbots generally work with rules and cannot satisfy needs at a high level. Chatbots with NER can understand what users talk about and generate answers accordingly. As you see what users talk about, you can shape your priorities and manage your processes efficiently.

Chatbots are treasury for extracting information about users because all data from chatbots are directly from users themselves. The conversation contains golden information about them; for this reason, it is vital to see and sense the content correctly. NER helps to extract information from chatbots and is enable to give that result.

Machine Translation

Detecting entities helps translate correctly because knowing a word’s meaning in another language helps complete translation. NER is used for not only proper names or specific entities but also synonyms, etc. For example, when a NER model notices a proper name, it should detect it and not translate it.

The NER model perceived that “Mustafa Kemal Atatürk” is a proper name and did not translate it.

To detect entities correctly, a NER model should notice all the words of the entity. Therefore, it should specify the beginning and ending words, as well. In this example, the BILOU format is viewed, but there are different data labeling formats.

B — Indicates the first word of the entity.

I — Indicates that the name of the entity continues.

L — Indicates the last word of the entity.

O — Indicates asset words that don’t belong to any category.

U — Indicates one-word entity names.

Google Translate is a good example of machine translation; it can be used whenever wanted, and one can get instant results. Machine translation can also be seen on YouTube, like subtitles in different languages: At first, AI senses the spoken language and what is spoken and translates it to needed language.

Health Care

By using test results from the laboratory as train data, AI can make sense of the results. NER models are facilitators for speeding up the identification, analysis, and healing of health problems. We can say that NER usage for tagging entities and extracting relations is necessary for biomedical knowledge extraction.


The ability to detect entities told in data to process texts in real-time helps speed up and shape processes efficiently. NER models can analyze customers’ or employees’ comments and give results according to data and defined entities. By this means, managers can take action faster according to the needs.

Chatbots with NER have an important ability to notice the entities in messages. In this way, they signalize what is told in messages, answer questions in accordance with the real questions and bring managers to a conclusion in the field of needs and wants.

Academia / Literature

There can be hundreds of papers on a single topic, and as a human being, it isn’t easy to find certain information in that much data. NER is the best option to categorize and structure data because classifying papers according to included entities makes it easy to find certain information in the literature.


NER with Artiwise


Artiwise can detect entities in the news such as famous people, football teams, cities, etc. In this way, you can collate related news, which includes the entity you concern with, and list them easily.

Traditional search engines can not find a certain word as you need because a certain word may have different meanings, therefore, may refer to different entities. Artiwise helps you to reach the one you need by artificial intelligence.


Chatbots are digital assistants that communicate with real users in texts. Artiwise Analytics can instantly classify each comment from chatbot data and tag the related sentiment. As you can quickly identify the messages, you can shape your priorities without losing time.

Thanks to NER, it can be ensured that;

  • Detecting the parameters that can be requested from the users
  • Asking questions if necessary
  • Extracting needed parameters from the natural language input entered by the users

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