TNNT: The Named Entity Recognition Toolkit
This toolkit addresses the complexity of applying diverse NER models to unstructured documents for researchers and practitioners in NLP, though it is incremental as it combines existing tools.
The paper introduces TNNT, a toolkit that automates the extraction of categorized named entities from unstructured documents using 21 different NER models, integrating them into a Knowledge Graph Construction Pipeline to output results with summaries for enhanced data analysis.
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.