In this project, we aim at building a system that can automatically generate product reviews in a domain, such as movie reviews or book reviews. Two different approaches will be used. One of the approaches is a pattern-based approach, where the reviews are formed by filling in the slots of predefined patterns. The second one is an encoder-decoder approach, where the reviews will be generated by a deep learning model [1]. In the first part of the project, the literature on automatic review generation will be surveyed.
In this project, we aim at building a comprehensive word embedding repository for the Turkish language. Using each of the state-of-the-art word embedding methods, embeddings of all the words in the language will be formed using a corpus. First, the three commonly-used embedding methods (Word2Vec, Glove, Fasttext) will be used and an embedding dictionary for each one will be formed. Then we will continue with context-dependent embedding methods such as BERT and Elmo. Each method will be applied with varying parameters such as different corpora and different embedding dimensions.
