"Translation can't change a name": Using Multilingual Data for Named Entity Recognition
This work addresses named entity recognition for multilingual NLP applications, but it is incremental as it builds on existing discriminative NER systems with new features.
The paper tackled the problem of named entity recognition by leveraging multilingual data, showing that using unsupervised word clusters from secondary languages as features improves performance, with significant increases particularly for person and location identification and more benefits from phylogenetically close languages.
Named Entities (NEs) are often written with no orthographic changes across different languages that share a common alphabet. We show that this can be leveraged so as to improve named entity recognition (NER) by using unsupervised word clusters from secondary languages as features in state-of-the-art discriminative NER systems. We observe significant increases in performance, finding that person and location identification is particularly improved, and that phylogenetically close languages provide more valuable features than more distant languages.