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Deep Learning Named Entity Recognition

Find relevant information and exploit all the potential of your content

While companies all over the world generate an ever-growing amount of data, this makes it difficult for teams to find relevant information and exploit all the potential of their content.

Using deep learning named entity recognition, companies can automate text classification and facilitate indexing, saving a precious amount of time and boosting efficiency.

Named Entity Recognition for information extraction

Deep learning named entity recognition allows to automate information extraction thanks to powerful machine learning techniques. This helps process huge amounts of text data in an automated way, where manually analyzing all of it would be too time-consuming. This feature works thanks to natural language processing, which analyzes word structure and semantics to extract meaningful information from any form of content.

Quick definition

Named entity recognition (NER) is one of several tasks performed by natural language processing (NLP) tools, which allows to extract and categorize any named entity in a text content. A named entity can be many different things: a person, place, time, event, product, organization, phone number, etc. In order words, this functionality helps recognize all of the subjects discussed in a text, by extracting key instances from its content, then automatically labeling and classifying them.

How does it work?

Named Entity Recognition works with powerful neural networks to analyze very large amounts of unstructured text data (under the form of emails, news articles, transcribed text from audio or video content, social media posts…) and extract key entities from it.

This process works through machine learning and natural language processing, in order to examinate the structure and meaning of words, while training the machines to recognize entities and classify them into pre-determined categories.

Before the machine automatically performs entity extraction and label prediction, it has to be trained by human by defining these categories and associating various named entities with each one of them.

NER is used within most text analysis tools to understand which instances are mentioned in a content, the meaning of each entity and what kind of relationships exist between them.

The deep learning algorithm makes decisions in case it ever runs into an ambiguous term, to generate the most relevant tagging with a high level of reliability. Although, the user always has the final say concerning the meaning of a word, by manually pre-entering or post-moderating the instance.

What are the benefits of NER?

Powered by artificial intelligence, named entity recognition allows its users to get meaningful, structured information from a great quantity of unstructured data, in a totally automated way. NER tools are able to quickly analyze a text document to understand what is talks about and extract key information from it.

Text content then goes through sentiment analysis to determine whether the tone of a message is positive or negative. This allows companies to get useful customer feedback directly extracted from online reviews or social media posts, to name a few.

This helps save a lot of time from browsing through content for hours, trying to identify specific names or entities.

How is NER useful?

There are multiple ways companies can use NER in text analytics to work more efficiently.

First, NER tools allow to generate automated tagging and entity categorization, in order to facilitate retrieval of the relevant parts in a content.

The tagging system allows to automatically generate metadata, turning unstructured information into enriched, annotated content. It helps make this content accessible, easily shared and exploited by all teams within the company.

Therefore, named entity recognition is useful to any company who needs to manage a great amount of content and to industrialize their content description.

NER applications & use cases

NER allows to process any type of textual content and can efficiently work with other content analysis features, for the most precise description and enrichment.

For example, the NER feature takes over from speech to text to automatically recognize entities within the transcribed textual content and put them into context.

This process finds many applications in various domains. Below are some use-cases of how it served our clients:

For Eurovision, our NER tool extracted the names of people present during the events from the client’s sources. It then automatically compared the faces appearing in the image with this list of names, to recognize which person was showing or talking at what precise moment.

For sports club Paris Saint-Germain, our NER platform ingested and extracted data from match logs to identify all of the key entities in the content: players, actions, time and place, etc. This allowed to index all of these entities so that any coworker or partner could easily retrieve the information they were looking for.

How does Perfect Memory help you achieve your objectives with NER?

Named entity recognition powered by Perfect Memory helps automate low-value tasks like tagging and classifying content, to save you time and help you optimize your content management strategy.

Perfect Memory’s NER tool is a SaaS (software-as-a-service) solution that is easily deployed and can be implemented with many different software. Contrary to open-source NER solutions, it requires no previous coding or machine learning experience.

Our solution

Deep learning named entity recognition is only one of the many features available with our Content Operations Ecosystem.

Perfect Memory’s solution uses computational-linguistics to train machine learning algorithms so it processes large quantities and types of content: image, text, audio, video, etc. Using natural language processing, it analyzes and extracts metadata from content in multiple languages.

The NER feature associates with video analysis and goes beyond face recognition to identify people speaking, objects appearing in the image, etc. It works with semantic segmentation annotation to generate incredibly accurate description of all the elements within a content.

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