COMBO: State-of-the-Art Morphosyntactic Analysis

Mateusz Klimaszewski; Alina Wróblewska

EMNLP 2021system demonstrations6 citations

COMBO: State-of-the-Art Morphosyntactic Analysis

Abstract

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.

BibTeX

@inproceedings{klimaszewski-wroblewska-2021-combo-state,
    title = "{COMBO}: State-of-the-Art Morphosyntactic Analysis",
    author = "Klimaszewski, Mateusz  and
      Wr{\'o}blewska, Alina",
    editor = "Adel, Heike  and
      Shi, Shuming",
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-demo.7/",
    doi = "10.18653/v1/2021.emnlp-demo.7",
    pages = "50--62"
}