Multilingual Language Processing From Bytes
Table 2: A comparison of NER systems. The results are F1 scores, where a correct span annotation exactly matches a gold span annotation (start, length, and entity type must all be correct). Results of the systems described in the text are shown for English, German, Spanish, and Dutch. BTS* shows the results of the BTS model trained separately on each language while BTS is a single model trained on all 4 languages together. The top set of results leverage resources beyond the training data; the middle set do not, and thus are most comparable to our results (bottom set).
['[BOLD] Model', '[BOLD] en', '[BOLD] de', '[BOLD] es', '[BOLD] nl']
There is relatively little work on multilingual NER, and most research is focused on building systems that are unsupervised in the sense that they use resources like Wikipedia and Freebase rather than manually annotated data. Nothman et al. \shortcitenothman2013learning use Wikipedia anchor links and disambiguation pages joined with Freebase types to create a huge amount of somewhat noisy training data and are able to achieve good results on many languages (with some extra heuristics).