Using NLP for Relation Extraction
Make use of our NLP expertise to help AI extract relationships between entities in unstructured sources, such as raw text. Cogito provides businesses working on NLP prototypes with supervised relation extraction from text. At Cogito, we perform relation extraction using NLP techniques that allow us to identify entity relationships in the text.
Contact Us Now
Our Approach to the Relation Extraction Process
An NLP algorithm may not learn correctly if incorrectly extracted data is used for training. At Cogito, experts use text mining and pattern matching to accurately detect the entities and their relationships within the text. Once the relationships have been extracted, they are verified by semantic analysis and rule-based approaches. As the process involves several steps, experts must be involved in accurate relation extraction.
Our Relation Extraction Service Involves:
Text Annotation
The process of annotation for relation extraction in NLP involves assigning labels to phrases or words in a sentence or document that describe their relationship. Typically, annotations are done manually by an annotator, but there are also automated methods for recognizing patterns and relationships in texts.
Our text annotation experts can help build information extraction systems, or to create knowledge bases out of the text.
Open Relationship Extraction
Open relationship extraction involves extracting relationships that are not explicitly stated in the text, such as those between people, places, and organizations. It has many practical applications in the fields of natural language processing and knowledge discovery.
The relation extraction in machine learning can generate knowledge graphs for accurate information presentation, service recommendation, and question answering.
Supervised Relation Extraction
Training the model on labeled data constitutes supervised learning, which means that the relation extraction dataset used to train the model must contain examples of the relationships it is expected to recognize.
With considerable expertise in NLP models and relation extraction, Cogito’s experts know what it takes to develop a model that recognizes relationships between two entities in a text.
Targeted relationship extraction
A targeted relationship extraction method in Natural Language Processing (NLP) identifies specific relationships among entities in a text. We offer relation extraction services for that very purpose — our experts can identify entities and their relationships within the text using automated systems and manual methods.
It is possible to further utilize the extracted data as input for NLP models and to construct knowledge databases.
Entity Relationship Extraction for NLP
Entity Relationship Extraction (ERE), which is a type of Natural Language Processing, involves extracting structured information from unstructured text. The extraction method is used to identify relationships among entities such as people, products, and places.
ERE uses a combination of rule-based methods and machine-learning techniques to analyze sentiments, understand knowledge, and summarize information.
Relation Extraction Use Cases
With the ability of a machine to comprehend how entities link up and communicate, entity extraction has reached a new level. Various tasks can be accomplished using relationship extraction, such as identifying relationships between entities, such as products to brands to people. Embracing our relationship extraction expertise can enable AI enterprises to develop NLP algorithms for recognizing entities and their relationships automatically.
Medical
In order to build a medical database, pharmaceutical companies can use relationship extraction-based AI to find interactions/relationships among drugs and medicines.
Social Media
With relationship extraction, social media companies can design AI that recommends relevant pages and communities to users based on relationships between people, places, and organizations.
Information Technology
Search engines can also utilize relationship extraction to build a searchable knowledge base and develop AI to recommend user pages with content similar to their search terminologies.
Outsource To Us
Having robust, specially trained teams makes it easy to build and scale relation extractions. Outsourcing relation extraction to Cogito can be a cost-effective method of quickly gaining insights from unstructured data. As part of our work at Cogito, we use a number of techniques, including natural language processing and machine learning, in order to develop algorithms that identify relationships between entities.
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
Wide range of other industries that require Relation Extraction services. Bringing together over 1500 data experts, Cogito boasts a wealth of industry exposure to help you develop successful NLP models that utilize relationship extraction.