Named Entity Recognition

Named Entity Recognition (NER) plays an integral role in enabling machines to understand the key text in NLP entity extraction for deep learning. We use NER expertise to train your machine learning 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 Named Entity Recognition Process

Data preprocessing is done through a process called NER which involves identification and categorization of textual information. It is done manually by a well-trained and highly experienced data annotation team.
It involves three key steps:-

Step 1

It is a manual process that’s carried out by a well-trained and highly experienced data annotation team.

Step 2

The labels are applied by workers to get specific words or phrases within a larger text.

Step 3

The labels are selected by using a mouse/pointer to highlight the part of the text that’s to be labeled.

Techniques Used in Named Entity Recognition

Named Entity Recognition Annotation

Named Entity Recognition Annotation

Annotations based on NER Annotation enrich natural language processing pipelines that identify relationships between entities. NER Annotation tools when used with manual labeling ensures the precision of NLP applications which rely 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 NER 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 including person names, locations, organizations, etc. Among the applications for Open NER are information retrieval, question answering, automatic summarization, and machine translation.

Open Named Entity Recognition
Supervised Named Entity Recognition

Supervised Named Entity Recognition

Supervised NER 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.

We 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

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 NER experts at Cogito use machine learning, natural language processing, information retrieval, and data mining to help you build a custom NER system that suits your specific needs.

Targeted Named Entity Recognition
Named Entity Recognition for NLP

Named Entity Recognition for NLP

NER utilizes the principles of NLP to identify and categorize named entities in text, such as individuals, organizations, locations, dates, etc. By using NER in NLP, information can be extracted from unstructured text, such as news articles, blogs, and social media posts.

We can offer you a comprehensive suite of NER solutions customized to meet your needs.

Named Entity Recognition Use Cases

The NLP-led NER 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

Publishing

Automated NER 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

Customer Care

NER makes it possible for businesses to categorize customer complaints into teams, departments, products, and company branches. The requests can be automatically routed to the relevant department using an automated notification system developed with NER.

Finance & Banking

Finance & Banking

NER is a time-consuming, tedious, and a process that’s prone to human error in the private financial markets and banking space. It can help banking & financial institutions evaluate profitability and credit risk more efficiently by tagging and classifying relevant data.

Outsource To Us

We use deep learning and neural networks to recognize entities in any language and across any domain. Our NLP technologies can be integrated with sentiment analysis and text classification to suit customer requirements.

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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.

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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.

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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.

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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 NER 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 NER.

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