TNNT: The Named Entity Recognition Toolkit

TL;DR

TNNT is a comprehensive toolkit that automates the extraction of categorized named entities from unstructured documents using multiple NLP models, facilitating improved data analysis and knowledge graph construction.

Contribution

The paper introduces TNNT, a toolkit integrating 21 NER models into a pipeline for automated entity extraction and summarization from diverse document formats.

Findings

Supports 21 NER models within a unified pipeline

Enables automated extraction and summarization of entities

Facilitates knowledge graph construction and NLP tasks

Abstract

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.