@inproceedings {pub4142,
	title = {MyFixit: An Annotated Corpus and Annotation Tool for Extracting Information from Instructional Text},
	author = {Nima Nabizadeh AND Martin Ernst Heckmann AND Dorothea Kolossa},
	year = {2020},
	abstract = {Text instructions are the most common way of learning and teaching various tasks for humans. For having a system capable of supporting humans in such tasks, a vital step is to extract a machine-understandable knowledge from the instructions. In this paper, we focus on the complex task of repairing devices. For this purpose, we interduce a semi-structured corpus of repair manuals, annotated with the information that a repair assistant can use to help a human, including the required tool and the disassembled objects at each step of the repair progress. Then we propose baseline methods for the extraction of such information automatically. The baseline methods are integrated into a semi-automatic web-based annotator tool that is also available along with the dataset.},
	publisher = {LREC},
	booktitle = {LREC 2020: 12th Conference on Language Resources and Evaluation}
}