My research project aims to extract toponyms from the British India colonial corpus to create a historical gazetteer. The primary challenge in this work revolves around the toponyms themselves, as they exhibit a high degree of fuzziness and inconsistency, particularly in their spellings. Historically, mapping, documenting, and surveying have been recognized as essential tools employed by colonial powers to demarcate, expand, and exert control over their colonial subjects. These activities enabled the colonial administration to establish governance over land and streamline revenue collection during the British colonial period. As time progressed, surveys expanded beyond their initial military and geographical purposes, evolving into comprehensive sources of information encompassing geography, political economy, and natural history. The British colonial India corpus is, therefore, intricate, marked by non-standard formatting, and plagued by inconsistencies in the spelling of Indian toponyms. This intricacy adds an extra layer of complexity to the task of extracting and organizing these toponyms for the creation of a historical gazetteer. The recognition of these challenges underscores the importance of using advanced techniques and tools to handle the uncertainty inherent in this historical data.

Digital Humanities methods and tools

The first and foremost challenge is the absence of a trained dataset of Indian place names. I need to focus on creating a trained dataset using Named Entity Recognition and other external open-access resources, such as Wikipedia. The second challenge pertains to the advanced programming techniques that I am experimenting with. The initial experiment with BERT NER for identifying toponym entities demonstrates that the algorithm performs well compared to other NER libraries. However, it also identified a few words that are not toponyms as place names and did not identify the broken toponym words as place names. Therefore, the extracted place name entities will require manual verification to confirm their accuracy. I anticipate encountering additional challenges when I begin exploring DeezyMatch, as I am currently in the initial stages of my research.

Digital Academy workshop on uncertainty 

The Digital Academy workshop presented a fantastic opportunity for scholars like myself to convene and discuss a wide array of challenges, approaches, methods, and tools for addressing uncertainty. The inclusion of experts in the field of uncertainty was a valuable aspect of this workshop, enabling attendees to solicit advice and feedback on the challenges they face in their research. Although I was not able to attend the entire workshop, the workshop's theme serves as a motivating factor for me to persist in my research endeavors despite the numerous challenges I've encountered. I believe that ongoing discussions and collaboration within the academic community will be instrumental in finding effective solutions to these challenges and further advancing the field. 

Questions remain open

The open questions revolve around the ideal size of the corpus required for applying the aforementioned advanced techniques and the expected effectiveness of the trained dataset. However, I am hopeful that I will be able to find answers to these questions in the near future. 

References

Devlin, Jacob, Ming-Wei Chang, Kenton Lee and Kristina Toutanova. “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.” North American Chapter of the Association for Computational Linguistics (2019). Accessed October 5, 2023. https://arxiv.org/pdf/1810.04805v2.

Hosseini, Kasra, Federico Nanni, and Mariona Coll Ardanuy. “DeezyMatch: A Flexible Deep Learning Approach to Fuzzy String Matching.” Paper presented at the Empirical Methods in Natural Language Processing: System Demonstrations, Online, October 2020. https://aclanthology.org/2020.emnlp-demos.9. Accessed October 5, 2023.  

Biographical note