Year: 2006
Type: conference-paper
Abstract: This paper describes the development of CICEROARABIC, the first wide coverage named entity recognition (NER) system for Modern Standard Arabic.Capable of classifying 18 different named entity classes with over 85% F, CICEROARABIC utilizes a new 800,000word annotated Arabic newswire corpus in order to achieve high performance without the need for hand-crafted rules or morphological information.In addition to describing results from our system, we show that accurate named entity annotation for a large number of semantic classes is feasible, even for very large corpora, and we discuss new techniques designed to boost agreement and consistency among annotators over a long-term annotation effort.
Authors: Luke Nezda, Andrew Hickl, John Lehmann, Sarmad Fayyaz
Institution: Language Computer (United States)
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2756800091