CNER: A tool Classifier of Named-Entity Relationships

TL;DR

CNER is an ensemble tool designed for extracting semantic relationships between named entities in Spanish, combining multiple NLP tools within a user-friendly interface to facilitate research and education.

Contribution

It introduces a container-based ensemble architecture for Spanish NLP tasks, integrating various tools for entity recognition and relation extraction in a practical, educational prototype.

Findings

Preliminary results show promising potential for CNER in Spanish NLP.

CNER effectively combines multiple tools for improved relation extraction.

The tool demonstrates usability and educational value in NLP research.

Abstract

We introduce CNER, an ensemble of capable tools for extraction of semantic relationships between named entities in Spanish language. Built upon a container-based architecture, CNER integrates different Named entity recognition and relation extraction tools with a user-friendly interface that allows users to input free text or files effortlessly, facilitating streamlined analysis. Developed as a prototype version for the Natural Language Processing (NLP) Group at Universidad del Valle, CNER serves as a practical educational resource, illustrating how machine learning techniques can effectively tackle diverse NLP tasks in Spanish. Our preliminary results reveal the promising potential of CNER in advancing the understanding and development of NLP tools, particularly within Spanish-language contexts.