Multi-Modal Citizen Science: From Disambiguation to Transcription of Classical Literature
| Autor: | Maryam Foradi, Johannes Pein, Gregory Crane, Jan Kaßel |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Computer science Computer Science - Human-Computer Interaction 02 engineering and technology computer.software_genre Human-Computer Interaction (cs.HC) Annotation Computer Science - Computers and Society User experience design Transcription (linguistics) Computers and Society (cs.CY) 0202 electrical engineering electronic engineering information engineering Citizen science Added value 0501 psychology and cognitive sciences 050107 human factors Persian Computer Science - Computation and Language business.industry 05 social sciences 020207 software engineering Language acquisition language.human_language Reading comprehension language Artificial intelligence business Computation and Language (cs.CL) computer Natural language processing |
| Zdroj: | HT |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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