Name Tagging for Low resource Incident Languages based on Expectation driven Learning
In this paper we tackle a challenging name tagging problem in an emergent setting the tagger needs to be complete within a few hours for a new incident language (IL) using very few resources. Inspired by observing how human annotators attack this challenge, we propose a new expectation driven learning framework. In this framework we rapidly acquire, categorize, structure and zoom in on ILspecific expectations (rules, features, patterns, gazetteers, etc. from various non traditional sources: consulting and encoding linguistic knowledge from native speakers, mining and projecting patterns from both mono lingual and cross lingual corpora, and typing based on cross lingual entity linking. We also propose a cost aware combination approach to compose expectations. Experiments on seven low resource languages demonstrate the effectiveness and generality of this framework: we are able to setup a name tagger for a new IL within two hours, and achieve 33.8% 65.1% F score 1.
NAACL