A General-Purpose Tagger with Convolutional Neural Networks

TL;DR

This paper introduces a versatile CNN-based tagger that effectively handles various linguistic tagging tasks without task-specific tuning, demonstrating robustness and state-of-the-art performance across multiple scenarios.

Contribution

It presents a general-purpose CNN tagger capable of achieving high accuracy across different tagging tasks without task-specific hyper-parameter tuning.

Findings

Achieves state-of-the-art results in POS, morphological, and supertagging.

Performs well on unnormalized and out-of-vocabulary texts.

Demonstrates robustness across diverse tagging tasks.

Abstract

We present a general-purpose tagger based on convolutional neural networks (CNN), used for both composing word vectors and encoding context information. The CNN tagger is robust across different tagging tasks: without task-specific tuning of hyper-parameters, it achieves state-of-the-art results in part-of-speech tagging, morphological tagging and supertagging. The CNN tagger is also robust against the out-of-vocabulary problem, it performs well on artificially unnormalized texts.