Worldwide data production has been increasing rapidly every year, generating over 2.5 quintillion bytes of data per day. As a result, all data-intensive domains, such as natural language processing (NLP), are growing substantially. In fact, a report by Verified Market Research estimates that the global NLP market will reach $65.38 billion by 2030, compared to $13.17 billion in 2021.
A significant amount of NLP data is in unstructured text format, such as emails, business and technical documents, social media posts, messages, etc. Extracting valuable information from this huge data is a challenging task that demands modern NLP-based techniques. One such technique is known as Named Entity Recognition (NER).
What is Named Entity Recognition?
Named Entity Recognition is an NLP technique that scans whole textual data to identify and extract fundamental keywords belonging to specific semantic types (known as entities), such as person, place, company, etc., based on noun or verb phrases.
A few commonly used entity types are mentioned below:
- Names of people and products
- Figures, etc
For instance, have a look at the following Wikipedia text regarding Lionel Messi:
Different Components of a Standard Named Entity Recognition Model
Mainly a standard Named Entity Recognition model consists of three blocks:
- Noun Phrase Identification: This step involves extracting all noun phrases from a text using dependency parsing (identifying semantic relations among words in a sentence) & part of speech tagging (POS). In the above example, we identified noun phrases (all the names) like Lionel Messi, Argentina, Paris Saint-German, etc.
- Phrase Classification: This step classifies all extracted noun phrases into their respective categories. In the above example, we classified different types of noun phrases used, including person name (Lionel Messi), place name (Argentina), team name (Paris Saint-Germain), date (24th June 1987), and quantity (7).
- Entity Disambiguation: Entities can get misclassified, e.g., Paris refers to the city of France in the context of the passage mentioned above, NOT Paris Hilton or any other entity named ‘Paris.’ This can be avoided by adding a link with the named entity, e.g., the Wikipedia page (https://en.wikipedia.org/wiki/Paris) of Paris city.
How Named Entity Recognition Works?
For humans, it is easy to identify entities from a text passage, but it is a challenging task for computers. First, they have to recognize the entities & then classify them, which requires modern NLP techniques. Let’s briefly discuss them below.
A Quick Overview of NLP Workflow
NLP provides the structure and rules of languages to develop a mechanism where it is possible to extract meaning from the words in a sentence. The following NLP techniques are typically applied to language or text-based data to extract meaningful context:
- Tokenization: Each sentence is divided into words to understand each of them individually.
- POS Tagging: Each word is assigned a part of speech that it represents, e.g., noun, verb, etc.
- Stop Word Removal: Words that are not important in understanding the context are known as stop words, e.g., a the, of, etc. They are removed in this step.
- Stemming: A technique used to extract the base form of a word by chopping off the end letters, e.g., words like ‘goes,’ ‘going,’ and ‘gone’ reduces to the base word ‘go.’
- Lemmatization: Lemmatization generates better ‘root’ words as per context in the text. For example, lemmatizing the word ‘caring’ will generate ‘care’ while stemming might generate ‘car.’
Various toolkits are available that help develop a named entity recognizer, such as Python-based NLTK & SpaCy libraries. Moreover, machine learning or neural network algorithms are used to train the NER models and improve the results over time.
In a nutshell, first, entity categories are created, like name, location, organization, date, etc., using NLP workflow. Then, relevant training data is fed to the NER model, which learns to detect and categorize the entities by mapping them with their categories. The model is tested using various evaluation metrics (f1-score, precision, recall, etc.). Moreover, hyperparameter tuning is also performed to tune the NER system and maximize the evaluation metrics.
3 Prominent Use Cases of Named Entity Recognition
NER extracts key values from a large corpus that helps in performing many essential tasks in enterprise products, such as recommendation systems, effective customer support, and efficient searching on web browsers, to name a few. Let’s discuss them below:
Effective Customer Support
Suppose you are managing the customer support department in an electronic store with many branches worldwide. You will receive a ton of customer feedback or complaints through tweets, Facebook reviews, or any other social media platform. NER can analyze each feedback/complaint and identify the location of the relevant store, product type, and issue. Then, you can forward this complaint to the concerned department or respond to the customer with a quick resolution. This makes it easy for you to manage complaints and provide effective customer support.
Efficient Search Algorithms
Suppose you're creating an internal search engine for a large online publisher with tons of articles and news pieces. If the classifier tries to look at all the words in this corpus for each search query, the search process will take an eternity to return results. Instead, if Named Entity Recognition is applied once on all of the articles and the relevant entities (tags) linked with each of the pieces are stored separately, the search process could simplify and increase the velocity of search execution significantly.
In today's world, a robust recommender system influences how we find new content, products, and ideas. The example of Netflix demonstrates how creating a successful recommendation system can increase competitive advantage over other platforms. NER can suggest similar articles or videos aligned with customer interests to capture their attention faster. Many news agencies, such as BBC and Daily Mail, use NER for content recommendations to their readers.
Building Wisecube’s Knowledge Graph for Entity-Named Recognition
Wisecube’s Knowledge Graph Engine uses advanced NLP and cutting-edge AI algorithms to deliver state-of-the-art analytics and insights on biomedical data. It identifies hidden relationships in the knowledge graph generated via open and extensible NLP pipelines. It infers and predicts correlations between entities, such as diseases, proteins, compounds, and a wide range of biomedical concepts.
Wisecube’s comprehensive knowledge graph provides the following benefits to the biomedical industry:
- Fusing disjointed datasets and providing a unified view of all information
- Surfacing hidden patterns
- Answering research questions
By quickly uncovering important insights, biomedical researchers can use Wisecube's knowledge graph to make ground-breaking drug discoveries that can save many lives. Wisecube’s NLP helps organizations develop an integrated graph of concepts and evidence from millions of documents and databases for uncovering explicit and undiscovered links.
If you wish to get innovative with your data analysis, schedule a call with us today and discover hidden gems in your data.