Retrieve, Read and Link: Fast and Accurate Entity Linking and Relation Extraction

September 14, 2020

Are you ready to dive into the world of Information Extraction (IE) with the blazing fast model ReLiK? With its capabilities for Entity Linking (EL) and Relation Extraction (RE), you can efficiently analyze large datasets on an academic budget! This guide will walk you through the installation process, usage, troubleshooting tips, and much more. Let’s get started!

Installation

First things first, you’ll need to install ReLiK. Here’s how you can go about it:

Using PyPI

pip install relik

Install with Optional Dependencies

Install with all optional dependencies:
```
pip install relik[all]
```
Install optional dependencies for training and evaluation:
```
pip install relik[train]
```
Install FAISS (a necessary indexing package):
```
pip install relik[faiss]
```

Installation from Source

git clone https://github.com/SapienzaNLP/relik.git
cd relik
pip install -e .[all]

Models Overview

ReLiK comes with various models for different tasks. Here’s a brief synopsis:

Quick Start

Let’s get our hands dirty with some code! The ReLiK model consists of two components: the retriever and the reader. The retriever gathers relevant documents, while the reader extracts entities and relations.

Using ReLiK for Entity Linking


from relik import Relik
from relik.inference.data.objects import RelikOutput

relik = Relik.from_pretrained("sapienzanlprelik-entity-linking-large")
relik_out: RelikOutput = relik("Michael Jordan was one of the best players in the NBA.")

The above code will return the identified entities like “Michael Jordan” and “NBA” correctly from the input text.

Using ReLiK for Relation Extraction


relik = Relik.from_pretrained("sapienzanlprelik-relation-extraction-nyt-large")
relik_out: RelikOutput = relik("Michael Jordan was one of the best players in the NBA.")

Understanding the Components: Analogy Time!

Think of the ReLiK model as a library system.

Retriever: The librarian who quickly identifies the shelves where the relevant books (documents) can be found based on your request.
Reader: The actual book that provides you the information you need about the characters and events (entities and relations) within its pages.

Together, they enable you to gather and digest information effectively and seamlessly! In this analogy, the librarian doesn’t just find you a book; it retrieves contextually relevant books to address your query, while the book itself answers your questions.

Troubleshooting Tips

If you encounter issues, consider the following:

Ensure you are using compatible versions of Python and the necessary libraries.
Check the installation commands for typos or missing dependencies.
If models fail to load or do not perform as expected, consider rechecking the input formats or revisiting the installation steps.
For additional clarity or project collaboration opportunities, check out fxis.ai.

Conclusion

ReLiK makes Entity Linking and Relation Extraction easy and fast without breaking the bank. Dive into the code snippets and adjust them according to your project needs. The library’s flexibility allows you to tailor the model for your specific requirements! You’re all set to test the system and expand your AI capabilities.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.