Robust Morphological Tagging with Word Representations

We present a comparative investigation of word representations for part-of-speech (POS) and morphological tagging, focusing on scenarios with considerable differences between training and test data where a robust approach is necessary. Instead of adapting the model towards a specific domain we aim to build a robust model across domains. We developed a test suite for robust tagging consisting of six languages and different domains. We find that representations similar to Brown clusters perform best for POS tagging and that word representations based on linguistic morphological analyzers perform best for morphological tagging.