CNER: A tool Classifier of Named-Entity Relationships
This is an incremental tool for Spanish NLP education and development, primarily benefiting researchers and students at Universidad del Valle.
The authors tackled the problem of extracting semantic relationships between named entities in Spanish by developing CNER, an ensemble tool with a user-friendly interface, and preliminary results show its promising potential for advancing NLP tools in Spanish contexts.
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.