Building Named Entity Recognition Taggers via Parallel Corpora.

The lack of hand curated data is a major impediment to developing statistical semantic processors for many of the world languages. Our paper aims to bridge this gap by leveraging existing annotations and semantic processors from multiple source languages by projecting their annotations via the statistical word alignments traditionally used in Machine Translation. Taking the Named Entity Recognition (NER) task as a use case, this work presents a method to automatically induce Named Entity annotated data using parallel corpora without any manual intervention. The projected annotations can then be used to automatically generate semantic processors for the target language helping to overcome the lack of training data for a given language. The experiments are focused on 4 languages: German, English, Spanish and Italian, and our empirical evaluation results show that our method obtains competitive results when compared with models trained on gold-standard, albeit out-of-domain, data. The results point out that our projection algorithm is effective to transport NER annotations across languages thus providing a fully automatic method to obtain NER taggers for as many as the number of languages aligned in parallel corpora. Every resource generated (training data, manually annotated test set and NER models) is made publicly available for its use and to facilitate reproducibility of results.