- Behrang Mohit, Nathan Schneider, Kemal Oflazer, Noah A. Smith. 2011. Annotating a Multi-Topic Corpus for Arabic Natural Language Processing. Abstract: Abstract Human-annotated data is an important resource for most natural language processing (NLP) systems. Most linguistically annotated text for Arabic NLP is in the news domain, but systems that rely on this data do not generalize well to other domains. We describe ongoing efforts to compile a dataset of 28 Arabic Wikipedia articles spanning four topical domains—sports, history, technology, and science. Each article in the dataset is annotated with three types of linguistic structure: named entities, syntax and lexical semantics. We adapted traditional approaches to linguistic annotation in order to make them accessible to our annotators (undergraduate native speakers of Arabic) and to better represent the important characteristics of the chosen domains. For the named entity (NE) annotation, we start with the task of marking boundaries of expressions in the traditional Person, Location and Organization classes. However, these categories do not fully capture the important entities discussed in domains li...
