COMBO: State-of-the-Art Morphosyntactic Analysis
This provides an efficient and accurate tool for NLP researchers and practitioners working with over 40 languages, though it appears incremental as it builds on existing neural approaches.
The authors tackled the problem of morphosyntactic analysis in NLP by introducing COMBO, a fully neural system for tasks like part-of-speech tagging and dependency parsing, which achieves often better prediction quality than state-of-the-art methods.
We introduce COMBO - a fully neural NLP system for accurate part-of-speech tagging, morphological analysis, lemmatisation, and (enhanced) dependency parsing. It predicts categorical morphosyntactic features whilst also exposes their vector representations, extracted from hidden layers. COMBO is an easy to install Python package with automatically downloadable pre-trained models for over 40 languages. It maintains a balance between efficiency and quality. As it is an end-to-end system and its modules are jointly trained, its training is competitively fast. As its models are optimised for accuracy, they achieve often better prediction quality than SOTA. The COMBO library is available at: https://gitlab.clarin-pl.eu/syntactic-tools/combo.