Named Entity Recognition — Extracting Entities That Matters
Bring our Named Entity Recognition (NER) expertise to your service to train your ML models and AI algorithms to identify the named entities in a text document with categorizations such as individuals, dates, places, people, company names, locations, medical terms, and product terminologies — quickly and accurately.
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Our Approach to Named Entity Recognition Process
Preprocessing of data involves named entity recognition (NER), i.e., the identification and categorization of textual information. Named entities often have varying lengths and formats, making NER difficult and requiring large amounts of hand-annotated data. The NER & NLP experts at Cogito are capable of accurately tagging and categorizing entities in text for machine learning.
Our Named Entity Recognition Service Involves:
Named Entity Recognition Annotation
Annotations based on Named Entity Recognition Annotation (NERA) enrich natural language processing pipelines that identify relationships between entities. Using named entity recognition annotation tools in conjunction with manual labeling ensures the precision of NLP applications that depend on a deep understanding of word meaning.
The annotations are then used to build systems that can recognize and classify named entities in a text.
Open Named Entity Recognition
Open Named Entity Recognition involves searching the text for meaningful words or phrases that can be identified as named entities. Following the identification of words, a list of categories is created, such as person names, locations, organizations, etc.
Among the applications for Open Named Entity Recognition are information retrieval, question answering, automatic summarization, and machine translation.
Supervised Named Entity Recognition
Supervised Named Entity Recognition is the process of extracting meaningful information from unstructured text data and converting it into structured form. A model is trained using labeled data and then used to recognize and classify entities in unseen texts.
Cogito can help you with datasets of accurately labeled entities required for training an NLP model — a model that learns from labeled data to recognize entities in unseen data.
Targeted Named Entity Recognition
Named Entity Recognition (NER) targets specific entities within a text. The use of NLP techniques allows for a more focused NER system to retrieve and identify entities that are of interest to the user.
The Named Entity Recognition experts at Cogito use machine learning, natural language processing, information retrieval, and data mining to help you build a custom Named Entity Recognition system that suits your specific needs.
Named Entity Recognition (NER) for NLP
NER utilizes the principles of natural language processing in order to identify and categorize named entities in text, such as individuals, organizations, locations, dates, etc. Using Named Entity Recognition in NLP, information can be extracted from unstructured text, such as news articles, blogs, and social media posts.
At Cogito, our team of experts can offer you a comprehensive suite of NER solutions customized to meet your needs.
Named Entity Recognition Use Cases
The NER system can answer questions, retrieve information, and translate text. In addition to improving part-of-speech tagging and parsing accuracy, NER can also be applied to other NLP tasks to enhance their accuracy. NLP-led named entity recognition can be used in a wide variety of use cases, including but not limited to text summarization, query processing, information extraction, and sentiment analysis.
Publishing
Automated Named Entity Recognition can provide publishers with a way of identifying prominent individuals, organizations, and locations mentioned in articles. It further helps build relevant tags for each article to automatically categorize articles according to their defined tags.
Customer Care
NER makes it possible for businesses to categorize customer complaints into teams, departments, products, and company branches. Customer support requests can be automatically routed to the relevant department using an automated notification system developed with NER.
Finance & Banking
It’s time-consuming, tedious, and prone to human error to extract data from PDFs and websites in the private financial markets and banking space. NER can help banking & financial institutions evaluate profitability and credit risk more efficiently by tagging and classifying relevant data.
Outsource To Us
Using deep learning and neural networks, experts at Cogito are capable of recognizing entities in any language and across any domain. Our NLP technologies can be integrated with sentiment analysis and text classification according to customer requirements. If you are in need of an accurate and reliable partner to handle NLP-based named entity recognition tasks, Cogito is a trusted name to partner with.
Quality on a Promise
Our team is committed to delivering high-quality Text Annotations. Our training data is therefore tailored for the applications of our clients.
Uncompromised Data Security
Data security and confidentiality are of utmost importance to us. At all points in the annotation process, our team ensures that no data breaches occur.
Scalable with Quick Turnaround Time
We at Cogito claim to have the necessary resources and infrastructure to provide Text Annotation services on any scale while promising quality and timeliness.
Flexible Pricing
Besides offering flexible pricing, we can tailor our services to suit your budget and training data requirements with our pay-as-you-go pricing model.
Get Us On Board
A wide range of industries can use our Named Entity Recognition expertise to extract data from digital documents, websites, pdf, etc. Bringing together over 1500 data experts, Cogito boasts a wealth of industry exposure to help you develop successful NLP models that utilize Named Entity Recognition.