  Word embeddings are fixed-length, dense and distributed word representations
that are used in natural language processing (NLP) applications. There are
basically two types of word embedding models which are non-contextual (static)
models and contextual models. The former method generates a single embedding
for a word regardless of its context, while the latter method produces distinct
embeddings for a word based on the specific contexts in which it appears. There
are plenty of works that compare contextual and non-contextual embedding models
within their respective groups in different languages. However, the number of
studies that compare the models in these two groups with each other is very few
and there is no such study in Turkish. This process necessitates converting
contextual embeddings into static embeddings. In this paper, we compare and
evaluate the performance of several contextual and non-contextual models in
both intrinsic and extrinsic evaluation settings for Turkish. We make a
fine-grained comparison by analyzing the syntactic and semantic capabilities of
the models separately. The results of the analyses provide insights about the
suitability of different embedding models in different types of NLP tasks. We
also build a Turkish word embedding repository comprising the embedding models
used in this work, which may serve as a valuable resource for researchers and
practitioners in the field of Turkish NLP. We make the word embeddings,
scripts, and evaluation datasets publicly available.
