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Online social media such as Twitter are growing so rapidly. Recently, Twitter has become one of the popular microblogging services on the Internet. It lets millions of users to communicate and interact by sending short messages of up to 140 characters. The massive amount of information over the web from Twitter requires an automatic tool that can determine the topics that people are talking about. The Topic Detection task is concentrated on discovering the main topics automatically. In this article at first, we explore different approaches to detect topics of tweets. Then, we will classify these topic detection approaches to four classes of categories, including with word embedding or without word embedding, specified or unspecified, offline (RED) or online (NED), and supervised or unsupervised. Finally, we will discuss the studied approaches in detail. [ABSTRACT FROM AUTHOR]

□ This paper is a review of automated text summarization, which is concerned with a subfield of natural language processing (NLP). With the daily increase of available information on the Internet, the need for systems that are able to automatically summarize text is greater than before. The continual study of a significant number of documents is a laborious and time-consuming process, so existence of an alternative procedure will be very effective. In this article, our consideration of the effective aspects of the summarization process will be discussed in detail. Three classifications of approaches to text summarization will be presented. Also, we will review some text summarization systems and their characteristics, and finally, the manner of evaluating text summarization systems will be surveyed. [ABSTRACT FROM PUBLISHER]

Cross-lingual word embeddings display words from different languages in the same vector space. They provide reasoning about semantics, compare the meaning of words across languages and word meaning in multilingual contexts, necessary to bilingual lexicon induction, machine translation, and cross-lingual information retrieval. This paper proposes an efficient approach to learn bilingual transform mapping between monolingual word embeddings in language pairs. We choose ten different languages from three different language families and downloaded their last update Wikipedia dumps1 Then, with some pre-processing steps and using word2vec, we produce word embeddings for them. We select seven language pairs from chosen languages. Since the selected languages are relative, they have thousands of identical words with similar meanings. With these identical dictation words and word embedding models of each language, we create training, validation and, test sets for the language pairs. We then use a generative adversarial network (GAN) to learn the transform mapping between word embeddings of source and target languages. The average accuracy of our proposed method in all language pairs is 71.34%. The highest accuracy is achieved for the Turkish-Azerbaijani language pair with the accuracy 78.32%., which is noticeably higher than prior methods. [ABSTRACT FROM AUTHOR]

Abstract This article is improved the random forest algorithm by selecting the most appropriate penalized regression methods, and it is tried to improve the post-selection boosting random forest (PBRF) algorithm using elastic net regression. The proposed method with the highest efficiency is called Reducing and Aggregating Random Forest Trees by Elastic Net (RARTEN). The introduced method consists of three steps. In the first step, the random forest algorithm is used as a predictor. In the second step, Elastic Net, as a penalized regression method, is applied to reduce the number of trees and improve the random forest and PBRF. In the last step, selected trees are aggregated. The obtained results of the real data and Monte Carlo simulation are evaluated using various statistical performance criteria. The simulation study shows that the RARTEN with 7%, 5%, and 8.5% reduction in the linear, nonlinear, and noise model, respectively improve the accuracy of the traditional random forest and the proposed method by Wang. In addition, this method has a significant reduction compared to other penalized regression methods. Moreover, the real data results show that the proposed method in our study with a reduction of almost 16% confirms the validity of the proposed model. [ABSTRACT FROM AUTHOR]