NLP For Relation Extraction

What is NLP Relation Extraction?

The concept of relation extraction entails the use of technology to help machines understand the relationship between various entities in a sentence. Let’s say you come across a sentence, ‘Paris is in France.’ The sentence reflects a relationship between Paris and France.
That is easy for humans to understand, but how do machines understand the relation between entities in a sentence? This is where relation extraction using NLP techniques comes into play. At AnnotationBox, we have experts who are well-versed in the processes to help organizations establish relations between words in a sentence.
Get in touch with us for the best relation extraction services!

What Are the Applications of Relation Extraction in Businesses?

We understand how relation extraction works and what its applications are. Here are a few applications of relation extraction: 

A. Knowledge Graph Construction

Knowledge graph is one of the major applications of natural language processing relation extraction. We work on the raw data to establish entity pairs or semantic relationships and help populate graphs automatically from raw texts.

C. Information Retrieval and Semantic Search

Information retrieval and semantic search go beyond simple searches using keywords. Here, the user’s intent matters, and chatbots or search engines understand the meaning of queries to provide the results. 

E. Entity Linking and Disambiguation

Relationship extraction helps link one entity to another using the right term or word. For example, you come across a sentence, ‘Apple is investing in some company.’  Here, apple can be the fruit as well, but the word ‘investing’ helps connect it to the company ‘Apple.’

What Do You Get When You Avail Our Natural Language Processing Services?

We are your one-stop solution for information extraction. You can be assured of getting everything in one place. Here’s what we bring to the table:

A. Text Annotation

Labeling data makes the process of relationship extraction easy. Our experts have the experience and knowledge base to label data and use them in the information extraction process. 

B. Open Relationship Extraction

Extracting relationships that are not explicitly stated in the input text is what open relationship extraction is all about. Our experts understand the processes and have helped numerous organizations with the open relation extraction process. 

C. Supervised Relation Extraction

The relation extraction dataset must be trained to understand and recognize the relationships. Our experts know how to create such models that can recognize two entities in a text. 

D. Targeted Relationship Extraction

There are cases where machines need to recognize specific relationships between entities in a sentence. Our services aim to solve the problem. The experts can identify the specific relationships for NLP models. 

E. Entity Relationship for NLP

This involves extracting structured information from unstructured text using appropriate methods. We help identify the relationships between the different entities for fast and accurate results.

What Are the Different Relation Extraction Techniques?

The relation extraction model evolved over the years. To understand more about it, we have listed a few NLP techniques used for relation classification: 

A. Rule-Based Approaches

Earlier, relationship extraction was rule-based. It used pre-defined patterns or syntactic rules. The process was easy, but it was not flexible, and struggled to identify relationships when presented with complex data. 

B. Supervised Machine Learning

The supervised machine learning technique relies heavily on labeled data. Part-of-speech tags, dependency parses, and word embeddings are used to train the classifiers for relationship recognition. The need for extensive labeled data limits the use of this technique. 

C. Deep Learning and Neural Network Approaches

Deep learning and neural networks are much advanced relationship extraction processes. This arranges texts sequentially or in parallel. It helps in getting quick and accurate results for relation classification. 

D. Distant Supervision

This technique is used to align existing structured data with text. It assumes that entities that are present in both are related to each other. The technique can help get a large amount of data, but assumptions might not be right every time. 

E. Dependency Parsing

The technique identifies grammatical structure in sentences. It helps build a relationship between words by understanding how they are dependent on each other. Using this technique, the NLP systems can determine relationships between entities accurately, even in complex sentences. 

F. End-to-End Neural Models

One of the recent developments in the field, end-to-end neural models, is designed to perform entity recognition and relationship extraction together. As a result, it improves the efficiency of the method by focusing on two tasks simultaneously.