Since word embeddings have been the most popular input for many NLP tasks, evaluating their quality is of critical importance. Most research efforts are focusing on English word embeddings. This paper addresses the problem of constructing and evaluating such models for the Greek language. We created a new word analogy corpus considering the original English Word2vec word analogy corpus and some specific linguistic aspects of the Greek language as well. Moreover, we created a Greek version of WordSim353 corpora for a basic evaluation of word similarities. We tested seven word vector models and our evaluation showed that we are able to create meaningful representations. Last, we discovered that the morphological complexity of the Greek language and polysemy can influence the quality of the resulting word embeddings.
 The word embedding methods have been proven to be very useful in many tasks of NLP (Natural Language Processing). Much has been investigated about word embeddings of English words and phrases, but only little attention has been dedicated to other languages. The recently introduced continuous Skip-gram model is an efficient method for learning high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships. In this paper we present several extensions that improve both the quality of the vectors and the training speed. By subsampling of the frequent words we obtain significant speedup and also learn more regular word representations. We also describe a simple alternative to the hierarchical softmax called negative sampling. An inherent limitation of word representations is their indifference to word order and their inability to represent idiomatic phrases. For example, the meanings of "Canada" and "Air" cannot be easily combined to obtain "Air Canada". Motivated by this example, we present a simple method for finding phrases in text, and show that learning good vector representations for millions of phrases is possible. Croatian is poorly resourced and highly inflected language from Slavic language family. Nowadays, research is focusing mostly on English. We created a new word analogy corpus based on the original English Word2vec word analogy corpus and added some of the specific linguistic aspects from Croatian language. Next, we created Croatian WordSim353 and RG65 corpora for a basic evaluation of word similarities. We compared created corpora on two popular word representation models, based on Word2Vec tool and fastText tool. Models has been trained on 1.37B tokens training data corpus and tested on a new robust Croatian word analogy corpus. Results show that models are able to create meaningful word representation. This research has shown that free word order and the higher morphological complexity of Croatian language influences the quality of resulting word embeddings. Testing word embeddings for Polish Distributional Semantics postulates the representation of word meaning in the form of numeric vectors which represent words which occur in context in large text data. This paper addresses the problem of constructing such models for the Polish language. The paper compares the effectiveness of models based on lemmas and forms created with Continuous Bag of Words (CBOW) and skip-gram approaches based on different Polish corpora. For the purposes of this comparison, the results of two typical tasks solved with the help of distributional semantics, i.e. synonymy and analogy recognition, are compared. The results show that it is not possible to identify one universal approach to vector creation applicable to various tasks. The most important feature is the quality and size of the data, but different strategy choices can also lead to significantly different results. Testowanie wektorowych reprezentacji dystrybucyjnych slow jezyka polskiego Semantyka dystrybucyjna opiera sie na zalozeniu, ze znaczenie slow wyrazone jest za pomocą wektorow reprezentujących, w sposob bezpośredni bądź pośredni, konteksty, w jakich slowo to jest uzywane w duzym zbiorze tekstow. Niniejszy artykul dotyczy ewaluacji wielu takich modeli skonstruowanych dla jezyka polskiego. W pracy porownano skutecznośc modeli opartych na lematach i formach slow, utworzonych przy wykorzystaniu sieci neuronowych na danych z dwoch roznych korpusow jezyka polskiego. Ewaluacji dokonano na podstawie wynikow dwoch typowych zadan rozwiązywanych za pomocą metod semantyki dystrybucyjnej, tzn. rozpoznania wystepowania synonimii i analogii miedzy konkretnymi parami slow. Uzyskane wyniki dowodzą, ze nie mozna wskazac jednego uniwersalnego podejścia do tworzenia modeli dystrybucyjnych, gdyz ich skutecznośc jest rozna w zalezności od zastosowania. Najwazniejszą cechą wplywającą na jakośc modelu jest jakośc oraz rozmiar danych, ale wybory roznych strategii uczenia sieci mogą rowniez prowadzic do istotnie odmiennych wynikow.