Dictionaries like Wordnet can help in a variety of Natural Language Processing applications
by providing additional morphological data. They can be used in Digital Humanities
research, building knowledge graphs and other applications. Creating dictionaries
from large corpora of texts written in a natural language is a task that has not been
a primary focus of research, as other tasks have dominated the field (such as chat-bots),
but it can be a very useful tool in analysing texts. Even in the case of contemporary
texts, categorizing the words according to their dictionary entry is a complex task,
and for less conventional texts (in old or less researched languages) it is even harder
to solve this problem automatically. Our task was to create a software that helps
in expanding a dictionary containing word forms and tagging unprocessed text. We used
a manually created corpus for training and testing the model. We created a combination
of Bidirectional Long-Short Term Memory networks, convolutional networks and a distancebased
solution that outperformed other existing solutions. While manual post-processing
for the tagged text is still needed, it significantly reduces the amount of it.
