Extraction of categorised named entities from text is a complex task given the availability of a variety of Named Entity Recognition (NER) models and the unstructured information encoded in different source document formats. Processing the documents to extract text, identifying suitable NER models for a task, and obtaining statistical information is important in data analysis to make informed decisions. This paper presents TNNT, a toolkit that automates the extraction of categorised named entities from unstructured information encoded in source documents, using diverse state-of-the-art Natural Language Processing (NLP) tools and NER models. TNNT integrates 21 different NER models as part of a Knowledge Graph Construction Pipeline (KGCP) that takes a document set as input and processes it based on the defined settings, applying the selected blocks of NER models to output the results. The toolkit generates all results with an integrated summary of the extracted entities, enabling enhanced data analysis to support the KGCP, and also, to aid further NLP tasks.
@article{arxiv.2108.13700,
title = {TNNT: The Named Entity Recognition Toolkit},
author = {Sandaru Seneviratne and Sergio J. Rodríguez Méndez and Xuecheng Zhang and Pouya G. Omran and Kerry Taylor and Armin Haller},
journal= {arXiv preprint arXiv:2108.13700},
year = {2021}
}