About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
IEEE Transactions on Audio, Speech and Language Processing
Paper
A Cascaded Approach to Mention Detection and Chaining in Arabic
Abstract
This paper presents a fully statistical approach to Arabic mention detection and chaining system, built around the maximum entropy principle. The presented system takes a cascade approach to processing an input document, by first detecting mentions in the document and then chaining the identified mentions into entities. Both system components use a common maximum entropy framework, which allows the integration of a large array of feature types, including lexical, morphological, syntactic, and semantic features. Arabic offers additional challenges for this task (when compared with English, for example), as segmentation is a needed processing step, so one can correctly identify and resolve enclitic pronouns. The system presented has obtained very competitive performance in the automatic content extraction (ACE) evaluation program. © 2009 IEEE