Named Entity Recognition (NER) is the process of extracting the crucial information for natural language processing (NLP). And in the NER, the entities like person names, organizations, locations, quantities, monetary values, numerical values, percentage and other types of legal entities mentioned in the document to make it recognizable and understandable to machines through NLP annotation algorithms.
How Named Entity Recognition Works?
Depending on the process has been used, named entity recognition works accordingly but the main motive is to extract the crucial information of all the entities mentioned in the document. Actually, NER processes the structured and unstructured texts by identifying and locating the entities.
Let’s take an example, instead of recognizing “Larry” and “Page” as different entities, NER understands that “Larry Page” as a single entity. And more advanced version of NER processes can even classify identified entities as well. Here, NER not only identifies, but can also classify “Larry Page” as a person.
So, basically, it works like first identifying the entities and then also classifies them to allocate in a particular category like person, organization or location. To understand how named entity recognition works read the process discussed below with various components.
Extracting the Information
The first step in NER is extracting the information by detecting and preparing the named entities mentioned in the document, paragraph, sentence and texts. The entire extraction phase includes the tagging the speech, detecting the boundaries of sentence, capitalization rules and co-reference in the documents that more important to use and find more specific terms in the search.
Searching the Entities
The next process in NER is searching the entity candidates to mention in the document. Names, multiple pages, and informative web pages pseudonyms are also considered to catch the synonyms. The searcher maintains the balance with accuracy and recall the right entity while keeping a small set of entities in order to reduce the calculation needed for recognizing such entities.
NAMED ENTITY RECOGNITION METHODS
There are basically two methods for named entity recognition machine learning, ontology and deep learning based NER. In first one ontology is knowledge based recognition process, in which collection of data sets containing words, terms, and their interrelations.
And depending on the level of details of Ontology the result of NER can be very comprehensive or specific to a particular topic. Compare to a free encyclopedia, where a very high level Ontology to capture and structure all their data is required.
A company operating in medical science needs a very far more detailed ontology due to the complexities of various medical terminologies. NER based on ontology is like machines learning approach that can at identifying known terms and concepts in unstructured or semi-structured texts, but at the same time also relying on updates.
Deep Learning NER
While on the other hand, deep learning NER is much more accurate than ontology, as it is capable to assemble words. And this is owing to a method called word embedding, that is capable of understanding the semantic and syntactic relationship between various words.
While another advantage of NER is deep learning enabled which can recognize terms and concepts that are not present in ontology because it is trained on the way various concepts used in the written life science language.
It is also able to learn analyzes topic-specific as well as high level words automatically. This makes deep learning NER applicable for performing multiple tasks. Deep learning can do most of the repetitive work itself, hence researchers for example can use their time more efficiently.
However, presently there are numerous deep learning methods available for NER. But owing to high competition and novelty of developments it is hard to identify the best one in the market. So, if you are interested or looking for named entity recognition service you can get in touch with Cogito for named entity recognition services NLP in machine learning and AI with best quality.