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The state-of-the-art named entity recognition (NER) systems are supervisedmachine learning models that require large amounts of manually annotated datato achieve high accuracy. However, annotating NER data by human is expensiveand time-consuming, and can be quite difficult for a new language. In thispaper, we present two weakly supervised approaches for cross-lingual NER withno human annotation in a target language. The first approach is to createautomatically labeled NER data for a target language via annotation projectionon comparable corpora, where we develop a heuristic scheme that effectivelyselects good-quality projection-labeled data from noisy data. The secondapproach is to project distributed representations of words (word embeddings)from a target language to a source language, so that the source-language NERsystem can be applied to the target language without re-training. We alsodesign two co-decoding schemes that effectively combine the outputs of the twoprojection-based approaches. We evaluate the performance of the proposedapproaches on both in-house and open NER data for several target languages. Theresults show that the combined systems outperform three other weakly supervisedapproaches on the CoNLL data.

Weakly Supervised Cross-Lingual Named Entity Recognition via Effective Annotation and Representation Projection