COMBO: State-of-the-Art Morphosyntactic Analysis

TL;DR

COMBO is a neural NLP system that provides accurate, multilingual morphosyntactic analysis including tagging, lemmatisation, and dependency parsing, with exposed vector representations for enhanced linguistic insights.

Contribution

It introduces COMBO, a fully neural, end-to-end system with jointly trained modules that achieves state-of-the-art accuracy across over 40 languages.

Findings

Achieves better prediction quality than existing SOTA models.

Provides vector representations of features for linguistic analysis.

Balances efficiency and high accuracy in morphosyntactic tasks.

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.