  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.
