Named Entity Recognition

What is Named Entity Recognition (NER)

Named Entity Recognition (NER) - also referred to as named entity extraction or identification - is an extraction technique that automatically identifies key information from text data and classifies them into predefined categories. NER is a form of Natural Language Processing that finds uses in analyzing unstructured text data such as emails, social media posts, product reviews, online surveys, research data, contractual documentation etc. Using Skyl.ai AI Platform for NLP you can quickly build and deploy custom Named entity recognition (NER) ML models in hours.

NER Industry use cases:

RETAIL

Product Trends in Social Media

With the advent of Social Media, economic demand for products has turned into a dynamic landscape. Traditional methods of product demand forecasting and trend analysis become unable to cope with the nature of social media content. Retail businesses engage AI to identify popular trends, using Named Entity Recognition to identify them, and promote similar products in their stores in order to stay ahead of the curve.

REAL ESTATE

Contract Content Analysis

Contract Negotiation and Contractual Interpretation are some of the more important parts in establishing a real estate deal. Implementing AI software can help real estate professionals review contracts more rapidly and organize and locate large amounts of contract data by extracting key entities like termination dates, agreed on payments, legislation references, contracting parties etc.

CUSTOMER SERVICE

Chat Analytics

Improving customer support strategy and efficiency is a high priority for every company. Using NLP, companies can get a solution where they see the chat graphs, comparing the chat with the time trends on an hourly, daily, weekly or monthly basis. Analyzing the peak hours, waiting times, response time, and chat rating helps companies to retain their customer base by optimal utilization of resources.

HEALTHCARE

Biomedical Named Entity Recognition

Bio-entity extraction is a core task of information extraction from medical literature. Examples of such entities include names of genes, proteins, location of activity etc. With the increase in biomedical record content, human annotators find this extraction task tedious. AI and Deep Learning can automate identifying and classifying bio-medical terms into predefined categories, from blocks of unstructured text data.

MANUFACTURING

Service Reports Interpretation

Quality Engineers periodically analyze service reports of machinery that have broken down to assess the cause of failure. The large volume of reports make this process very tedious and create bottlenecks. AI can be used to optimize this task. The text reports provided by the service team are fed into the ML model, and the AI automatically assigns a failure label to it to analyse the reliability statistics. AI & ML reduces the cost and resources required for the process