Arabic Named Entity Recognition — Video Lecture

This is a presentation that I gave at ACL entitled:  “Named Entity Recognition using Cross-lingual Resources: Arabic as an Example” (pdf). In this work, I use rich English resources with cross-lingual links to Arabic to properly identify the names of persons, locations, and organizations in Arabic text.  The named entity recognizer is available for download as part of Farasa Arabic processing toolkit.

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

Here is the abstract of the paper:

Some languages lack large knowledge bases and good discriminative features for Name Entity Recognition (NER) that can generalize 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 incorporating cross-lingual features and knowledge 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. The features led to improvements of 17.1% and 20.5% on the new news and microblogs test sets respectively