Paper: Named Entity Recognition using Cross-lingual Resources: Arabic as an Example

ACL ID P13-1153
Title Named Entity Recognition using Cross-lingual Resources: Arabic as an Example
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2013
Authors

Some languages lack large knowledge bases and good discriminative features for Name Entity Recognition (NER) that can general- ize to previously unseen named entities. One such language is Arabic, which: a) lacks a capitalization feature; and b) has relatively small knowledge bases, such as Wikipedia. In this work we address both problems by in- corporating cross-lingual features and knowl- edge bases from English using cross-lingual links. We show that such features have a dramatic positive effect on recall. We show the effectiveness of cross-lingual features and resources on a standard dataset as well as on two new test sets that cover both news and microblogs. On the standard dataset, we achieved a 4.1% relative improvement in F- measure over the best reported result in the literature. ...