I am a Doctoral Student in Library and Information Science. My project is titled Within the Dates: Predicting and Categorizing Time Periods in Historic Science Fiction Literature.

With the rapid expansion of digitized literature, there is an increasing urgency to develop effective organizational structures. Traditional categorizations based on authorship, titles, and genres often lack depth in capturing temporal nuances. This study delves into the complex task of categorizing literary texts according to specific time periods. It investigates the multifaceted challenge of time period categorization, mainly focusing on historical context and temporal cues within historical narratives. While traditional approaches that rely on manual cataloging face challenges in managing the increasing volume of literary content, the combination of Machine Learning techniques and the capabilities of large language models presents promising opportunities for developing efficient and scalable solutions.

This study focuses on several key aspects: a) conduct a literature review to map the current state of knowledge and identify a gap, b) evaluate and compare feature engineering techniques to predict a single time period category per text, c) examine the language models capability to predict multiple time periods d) To investigate the search behaviors of library patrons, specifically examining why they prioritize temporal or historical cues in their literature searches. Additionally, to explore how catalogers currently tag literature by time periods, and assess their perceptions of the potential benefits and challenges of implementing large-scale time period categorization. 

Researcher's publications in DiVA (Digitala Vetenskapliga Arkivet)