Multi-Modal Citizen Science: From Disambiguation to Transcription of Classical Literature
This work addresses the problem of enhancing reading comprehension and language learning for readers of smart texts in Digital Humanities, though it is incremental by adding a new annotation mode to an existing corpus.
The study tackled the challenge of enriching Classical Literature corpora by introducing audio annotations to Hafiz's poetry, enabling both proficient and non-proficient Persian speakers to contribute through tasks with varying difficulty levels, which helps rate annotator accuracy for future stages without ground truth data.
The engagement of citizens in the research projects, including Digital Humanities projects, has risen in prominence in recent years. This type of engagement not only leads to incidental learning of participants but also indicates the added value of corpus enrichment via different types of annotations undertaken by users generating so-called smart texts. Our work focuses on the continuous task of adding new layers of annotation to Classical Literature. We aim to provide more extensive tools for readers of smart texts, enhancing their reading comprehension and at the same time empowering the language learning by introducing intellectual tasks, i.e., linking, tagging, and disambiguation. The current study adds a new mode of annotation-audio annotations-to the extensively annotated corpus of poetry by the Persian poet Hafiz. By proposing tasks with three different difficulty levels, we estimate the users' ability of providing correct annotations in order to rate their answers in further stages of the project, where no ground truth data is available. While proficiency in Persian is beneficial, annotators with no knowledge of Persian are also able to add annotations to the corpus.