Intrinsic and Extrinsic Evaluation of Word Embedding Models

In this study, we aimed to understand and analyze how word embedding models work on both Turkish and English. We focused on the word2vec word embedding model. We tried to improve the quality of word representations by changing the orientation of context windows. By changing the context window orientation, we aimed to train models with better accuracy results without increasing the training time. The impact of different window sizes and vector dimensions on the quality of word representations was analyzed both intrinsically and extrinsically.