  Automatically learned vector representations of words, also known as "word
embeddings", are becoming a basic building block for more and more natural
language processing algorithms. There are different ways and tools for
constructing word embeddings. Most of the approaches rely on raw texts, the
construction items being the word occurrences and/or letter n-grams. More
elaborated research is using additional linguistic features extracted after
text preprocessing. Morphology is clearly served by vector representations
constructed from raw texts and letter n-grams. Syntax and semantics studies may
profit more from the vector representations constructed with additional
features such as lemma, part-of-speech, syntactic or semantic dependants
associated with each word. One of the key objectives of the ReTeRom project is
the development of advanced technologies for Romanian natural language
processing, including morphological, syntactic and semantic analysis of text.
As such, we plan to develop an open-access large library of ready-to-use word
embeddings sets, each set being characterized by different parameters: used
features (wordforms, letter n-grams, lemmas, POSes etc.), vector lengths,
window/context size and frequency thresholds. To this end, the previously
created sets of word embeddings (based on word occurrences) on the CoRoLa
corpus (P\u{a}i\c{s} and Tufi\c{s}, 2018) are and will be further augmented
with new representations learned from the same corpus by using specific
features such as lemmas and parts of speech. Furthermore, in order to better
understand and explore the vectors, graphical representations will be available
by customized interfaces.
