  Increased popularity of different text representations has also brought many
improvements in Natural Language Processing (NLP) tasks. Without need of
supervised data, embeddings trained on large corpora provide us meaningful
relations to be used on different NLP tasks. Even though training these vectors
is relatively easy with recent methods, information gained from the data
heavily depends on the structure of the corpus language. Since the popularly
researched languages have a similar morphological structure, problems occurring
for morphologically rich languages are mainly disregarded in studies. For
morphologically rich languages, context-free word vectors ignore morphological
structure of languages. In this study, we prepared texts in morphologically
different forms in a morphologically rich language, Turkish, and compared the
results on different intrinsic and extrinsic tasks. To see the effect of
morphological structure, we trained word2vec model on texts which lemma and
suffixes are treated differently. We also trained subword model fastText and
compared the embeddings on word analogy, text classification, sentimental
analysis, and language model tasks.
