Design and implementation of an open source Greek POS Tagger and Entity Recognizer using spaCy

TL;DR

This paper introduces an open-source Greek POS tagger and entity recognizer built with spaCy, achieving high accuracy and extending existing models for Greek language processing.

Contribution

It adds Greek language support to spaCy, develops a morphological POS tagger, and extends NER models, providing new tools for Greek NLP tasks.

Findings

The Greek POS tagger outperforms state-of-the-art results.

The NER model extends standard ENAMEX types for Greek.

Flexibility in handling out-of-vocabulary words is necessary.

Abstract

This paper proposes a machine learning approach to part-of-speech tagging and named entity recognition for Greek, focusing on the extraction of morphological features and classification of tokens into a small set of classes for named entities. The architecture model that was used is introduced. The greek version of the spaCy platform was added into the source code, a feature that did not exist before our contribution, and was used for building the models. Additionally, a part of speech tagger was trained that can detect the morphology of the tokens and performs higher than the state-of-the-art results when classifying only the part of speech. For named entity recognition using spaCy, a model that extends the standard ENAMEX type (organization, location, person) was built. Certain experiments that were conducted indicate the need for flexibility in out-of-vocabulary words and there is an effort for resolving this issue. Finally, the evaluation results are discussed.