Named Entity Disambiguation for Resource-Poor Languages

Abstract

Named entity disambiguation (NED) is the task of linking ambiguous names in natural language text to canonical entities like people, organizations or places, registered in a knowledge base. The problem is well-studied for English text, but few systems have considered resource-poor languages that lack comprehensive name-entity dictionaries, entity descriptions, and large annotated training corpora. In this paper we address the NED problem for languages with limited amount of annotated corpora as well as structured resource such as Arabic. We present a method that leverages structured English resources to enrich the components of a language-agnostic NED system and enable effective NED for other languages. We achieve this by fusing data from several multilingual resources and the output of automatic translation/transliteration systems. We show the viability and quality of our approach by synthesizing NED systems for Arabic, Spanish and Italian.