FRAKE: Fusional Real-time Automatic Keyword Extraction

Keyword extraction is called identifying words or phrases that express the main concepts of texts in best. There is a huge amount of texts that are created every day and at all times through electronic infrastructure. So, it is practically impossible for humans to study and manage this volume of documents. However, the need for efficient and effective access to these documents is evident in various purposes. Weblogs, News, and technical notes are almost long texts, while the reader seeks to understand the concepts by topics or keywords to decide for reading the full text. To this aim, we use a combined approach that consists of two models of graph centrality features and textural features. In the following, graph centralities, such as degree, betweenness, eigenvector, and closeness centrality, have been used to optimally combine them to extract the best keyword among the candidate keywords extracted by the proposed method. Also, another approach has been introduced to distinguishing keywords among candidate phrases and considering them as a separate keyword. To evaluate the proposed method, seven datasets named, Semeval2010, SemEval2017, Inspec, fao30, Thesis100, pak2018 and WikiNews have been used, and results reported Precision, Recall, and F- measure.

Multimodal price prediction

Valorization is one of the most heated discussions in the business community, and commodities valorization is one subset in this task. Features of a product is an essential characteristic in valorization and features are categorized into two classes: graphical and non-graphical. Nowadays, the value of products is measured by price. The goal of this research is to achieve an arrangement to predict the price of a product based on specifications of that. We propose five deep learning models to predict the price range of a product, one unimodal and four multimodal systems. The multimodal methods predict based on the image and non-graphical specification of product. As a platform to evaluate the methods, a cellphones dataset has been gathered from GSMArena. In proposed methods, convolutional neural network is an infrastructure. The experimental results show 88.3% F1-score in the best method.