Section: New Results

Multilingual POS-tagging

Participant : Benoît Sagot.

Morphosyntactic lexicons and word vector representations have both proven useful for improving the accuracy of statistical part-of-speech taggers. We compare the performances of four systems on datasets covering 16 languages, two of these systems being feature-based (MEMMs —in the case of our own system MElt— and CRFs) and two of them being neural-based (bi-LSTMs). We show that, on average, all four approaches perform similarly and reach state-of-the-art results. Yet we obtained better performances with feature-based models on lexically richer datasets (e.g. for morphologically rich languages), whereas neural-based results are higher on datasets with less lexical variability (e.g. for English). These conclusions hold in particular for the MEMM models relying on our system MElt, which benefited from newly designed features [32], [44]. Thus we have shown that, under certain conditions, feature-based approaches enriched with morphosyntactic lexicons are competitive with respect to neural methods.