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Transfer learning models have proven superior to classical machine learning approaches in various text classification tasks, such as sentiment analysis, question answering, news categorization, and natural language inference. Recently, these models have shown exceptional results in natural language understanding (NLU). Advanced attention‐based language models like BERT and XLNet excel at handling complex tasks across diverse contexts. However, they encounter difficulties when applied to specific domains. Platforms like Facebook, characterized by continually evolving casual and sophisticated language, demand meticulous context analysis even from human users. The literature has proposed numerous solutions using statistical and machine learning techniques to predict the sentiment (positive or negative) of online customer reviews, but most of them rely on various business, review, and reviewer features, which leads to generalizability issues. Furthermore, there have been very few studies investigating the effectiveness of state‐of‐the‐art pre‐trained language models for sentiment classification in reviews. Therefore, this study aims to assess the effectiveness of BERT, RoBERTa, ALBERT, DistilBERT, and XLNet in sentiment classification using the Yelp reviews dataset. The models were fine‐tuned, and the results obtained with the same hyperparameters are as follows: 98.30 for RoBERTa, 98.20 for XLNet, 97.40 for BERT, 97.20 for ALBERT, and 96.00 for DistilBERT. [ABSTRACT FROM AUTHOR]

The Boolean satisfiability problem (SAT) is a fundamental NP-complete decision problem in automated reasoning and mathematical logic. As evidenced by the results of SAT competitions, the performance of SAT solvers varies substantially between different SAT categories (random, crafted, and industrial). A suggested explanation is that SAT solvers may exploit the underlying structure inherent to SAT instances. There have been attempts to define the structure of SAT in terms of structural measures such as phase transition, backbones, backdoors, small-world, scale-free, treewidth, centrality, community, self-similarity, and entropy. Still, the empirical evidence of structural measures for SAT has been provided for only some SAT categories. Furthermore, the evidence has not been theoretically proven. Also, the impact of structural measures on the behavior of SAT solvers has not been extensively examined. This work provides a comprehensive study on structural measures for SAT that have been presented in the literature. We provide an overview of the works on structural measures for SAT and their relatedness to the performance of SAT solvers. Accordingly, a taxonomy of structural measures for SAT is presented. We also review in detail important applications of structural measures for SAT, focusing mainly on enhancing SAT solvers, generating SAT instances, and classifying SAT instances. [ABSTRACT FROM AUTHOR]

Problem statement: Sequence analysis problems are NP hard and need optimal solutions. Interesting problems include duplicate sequence detection, sequence matching by relevance, sequence analysis using approximate comparison in general or using tools i.e., Matlab and multi-lingual sequence analysis. The usefulness of these operations is highlighted and future expectations are described. Approach: This study described the concepts, tools, methodologies, algorithms being used for sequence analysis. The sequences contained precious information that needed to be mined for useful purposes. There was high concentration required to model the optimal solution. The similarity and alignments concepts can not be addressed directly with one technique or algorithm, a better performance was achieved by the comprehension of different concepts. Results: We had compared different approaches using exemplary data and found that ClustalW2 is fairly good tool in terms of analysis. We assigned different weight values for relevant features and obtained score 95 in comparison phenomenon and 45 in alignment. Conclusion: Different techniques and approaches had been evaluated and compared. Key words: Genome, multi-lingual, approximate matching, nucleotide base pair, corpora, duplicate sequences, INTRODUCTION Sequences are logical units that contain vital information, for instance consider biological sequences that compose of nucleotide base pairs in the form of A (Adenine), T (Thymine), G(Guanine) and [...]

Problem Statement: Processing texts based on rhetorical structure theory has shown interesting results. Rhetorical Structure Theory (RST) improves the ability of extracting the semantic behind the processed text. Different applications such as information retrieval, text summarization, and text generation have proved to give better result using RST. The applicability of RST to process and understand texts has been studied in several languages, but little is devoted to the Arabic language. Given an Arabic text, the more accurate the Arabic rhetorical relations are extracted the more useful the subsequent text representation will be. This, in turn, leads to a better understanding of the text and, hence, better results. Approach: We show a framework of applying RST on Arabic language in order to rhetorically parse, understand, and summarize Arabic texts. We discuss a new approach that extracts the Arabic rhetorical relations that is based on studying the English relations, analyzing Arabic corpus and understanding and using the Arabic cue phrases. Results: We obtain rhetorical relations based on Arabic cues. We show how this approach contributes in improving the understanding of the Arabic text. The study addresses the relations that rise from cues that act as connectors among Arabic clauses as well as words. Conclusion: The introduced approach suggests that realizing text coherency in the process of obtaining Arabic rhetorical relations suits the characteristics of the Arabic language and avoids the disadvantages of previous approaches. The obtained Arabic rhetorical relations will make it possible to build rhetorical trees for Arabic texts to apply in text summarization and generation, information retrieval, and text segmentation while preserving the coherency of the text. Key words: Rhetorical Structure Theory (RST), parsing Arabic texts, rhetorical relations, Arabic corpus analysis, Arabic cue phrases, Arabic text coherence., INTRODUCTION Rhetorical Structure Theory (RST) (11) was introduced to serve as a discourse structure in the computational linguistic field. It is intended to describe texts and offers an explanation of [...]