Material Detail

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

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

This video was recorded at Association for Computational Linguistics (ACL), Sofia 2013. 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.

Quality

  • User Rating
  • Comments
  • Learning Exercises
  • Bookmark Collections
  • Course ePortfolios
  • Accessibility Info

More about this material

Comments

Log in to participate in the discussions or sign up if you are not already a MERLOT member.