Search

Named Entity Recognition (NER), the automated identification and tagging of entities in text, is a popular natural language processing task, and has the power to transform restricted data into open datasets of entities for further research. This project benchmarks four NER models–Stanford NER, BookNLP, spaCy‐trf and RoBERTa–to identify the most accurate approach and generate an open‐access, gold‐standard dataset of human annotated entities. To meet a real‐world use case, we benchmark these models on a sample dataset of sentences from Native American authored literature, identifying edge cases and areas of improvement for future NER work. [ABSTRACT FROM AUTHOR]

Given the size of digital library collections and the inconsistencies in their genre‐related bibliographic metadata, as digital libraries grow and their contents are opened for computational analysis, finding materials of interest becomes a major challenge. This challenge increases for sub‐genres and other categories of text data that are less distinct from the whole. This project pilots machine learning methods and word feature analysis for identifying Black Fantastic genre texts within the HathiTrust Digital Library. These texts are sometimes referred to as "Afrofuturism" but more commonly today described as "Black Fantastic," in which African Diaspora artists and creators engage with the intersections of race and technology in their works with a primary focus on world‐building. Black Fantastic texts pose a challenge to genre classification, as they incorporate aspects of science fiction and fantasy with typical characteristics of African Diaspora‐produced literature. This paper presents and reports on results from a pilot predictive modeling process to computationally identify Black Fantastic texts using curated word feature sets for each class of data: general English‐language fiction, Black‐authored fiction, and Black Fantastic fiction. [ABSTRACT FROM AUTHOR]