Dr Ignatius Ezeani

Hello and thanks for stopping by. I am Ignatius, a computer scientist, an AI and NLP researcher and a Research Fellow with the School of Computing and Communications at Lancaster University, UK as well as a visiting Senior Lecturer at Nnamdi Azikiwe University, Nigeria.

I have a Bachelor (1st) in Computer Science, a Masters in Advanced Software Engineering, and a PhD in Natural Language Processing (NLP). I am interested in the application of NLP techniques in building resources for low-resource languages especially African languages, but my interests span other related areas like corpus linguistics, distributional semantics, information retrieval and extraction, machine learning, data science, deep neural models and general AI.

My current research work focuses on the efficient adaption of existing natural language processing tools and techniques to dealing with the challenges of integrating majority of the low-resource languages in a globalized world for task-oriented systems. I have contributed in building tools that support languages like Igbo and Welsh and I am currently working on other low-resource languages. You can reach me via any of the platforms shown here for a quick chat. Alternatively, you can check out the other pages on this site for my works and creative outputs.

Currently, I am the lead software developer on the £814k SBE-UKRI project trying to understand imprecise space and time in textual narratives through qualitative representations, reasoning, and visualization. Before that, I recently worked on the ongoing £80k FreeTxt project and completed the £90k Cardiff-Lancaster collaboration Welsh Text Creator project. I led the creation of the Igbo-English MT benchmark dataset, a $23k Facebook-funded project. I am part of Masakhane Initiative, the most vibrant AfricanNLP research community where I am also leading a Google-backed Question Answering dataset creation project for Igbo. For the 2nd year, I am on the Technical Advisory Panel of the Lacuna Fund granting over $1m in 2021 to fund the creation of African language datasets.