Semantic Textual similarity is one of the subtasks of natural language processing that has attracted extensive rese arch in recent years. Measuring semantic similarity between words, sentences, paragraphs, and documents plays an important role in natural language processing and computational linguistics. Semantic similarity of texts is used in question-answering systems, fraud detection, machine translation, information retrieval and etc. Semantic similarity means calculating the degree of similarity between two textual documents, paragraphs or sentences, which are presented in both monolingual and cross lingual forms. In this article, by using the parallel corpus, for the first time, the cross lingual model of semantic similarity for Persian-English sentences is presented, and then we test and compare our model with the Multilingual BERT model. The results show that by using parallel corpuses, the quality of sentence embedding in two different languages can be improved. Pearson correlation criterion based on cosine similarity between sentence's vector of multilingual Bert has increased from 65% to 73. 77% by the proposed method. The proposed method was also tested on the Arabic-English language pair, and the results show that the proposed method is superior to the multilingual Bert.

Semantic Textual Similarity (STS) is an important NLP task that measures the degree of semantic equivalence between two texts, even if the sentence pairs contain different words. While extensively studied in English, STS has received limited attention in Turkish. This study introduces BERTurk-contrastive, a novel BERT-based model leveraging contrastive learning to enhance the STS task in Turkish. Our model aims to learn representations by bringing similar sentences closer together in the embedding space while pushing dissimilar ones farther apart. To support this task, we release SICK-tr, a new STS dataset in Turkish, created by translating the English SICK dataset. We evaluate our model on STSb-tr and SICK-tr, achieving a significant improvement of 5.92 points over previous models. These results establish BERTurk-contrastive as a robust solution for STS in Turkish and provide a new benchmark for future research.

Literary texts bear textual resemblance and similarity among themselves; and they influence each other lingually, rhythmically and sementically. Literature symbolises a streamline wherein there exists no turbulence. Every text is absorbed and transformed into another text. In other words, literary texts which come into being depend on mental creativity of an author. These texts are influenced by the ancient texts by making certain changes in them and by breaking the norms and making new innovative approaches. At times, poets and writers in the course of creating their works cross the limits of being influenced to the extent that they seem to emulate the previous text to the extent of similarity in words and their meanings. Many poets who have appreciated sonnets of Hafiz are also related in the same manner. Apart from the Iranian poets, many poets across the borders have also considered the sonnets of Hafiz and have followed them. From these persian poets stands Valaiee Haider Abadi. The main theme of this article is to study the textual resemblance of both Valaiee Haider Abadi and Hafiz.

Purpose: The current research compares the representative elements (title, abstract, and keywords) of the articles that existed in the proposal references' citation network with the proposals’ elements. The other goal of this research is to calculate representative elements’ weighted average (title, abstract, and keywords) from a textual similarity perspective. Methodology: This is an applied and quantitative research that uses citation analysis and content analysis. The research sample is 3019 articles extracted from the citation network of 31 graduated students’ proposals (M. Sc. and Ph. D. ) in Chemistry at Shiraz University. All English articles' titles in the proposals' references were searched on the Web of Science database, and each article's file and all articles’ files in its citation network were saved in Excel format. All retrieved files were merged into one file and sorted based on citation count to have the unit citation network for each user's proposal. Because some of the proposals had an extended citation network with more than a thousand articles, 100 articles with the greatest citation count of each network were analyzed to create uniformity and balance among the proposals’ citation networks. Next, the scale of textual similarity of 100 articles' representative elements with the greatest citation count in the citation network, was calculated with the proposal’s title, the proposal’s text, and the titles of the proposal’s references. The scale of textual similarity was checked using designed software based on the Python programming language and measuring the cosine similarity. Findings: The results of the Kruskal-Wallis test showed that there was a significant difference between the articles’ representative elements and the title, text, and references’ titles of the proposals from a textual similarity viewpoint,and in all three cases articles’ abstracts had the most textual similarity with the proposal elements, then, the title and keywords of the articles' citation network were in the second and third ranks,In addition, the representative elements’ weighted average was calculated. The obtained value was 0. 62 for the abstract, 0. 5 for the title, and 0. 22 for the keywords, respectively. Conclusion: Despite the use of different platforms to measure the similarity between the documents searched and the documents desired by the user, there is still a distance to reach the ideal level. Until now, no research had used the representative elements of the articles that existed in the proposal references' citation network to measure the textual similarity with the proposal elements and had not evaluated their capability. The confirmation of textual similarity among the representative elements of the articles that existed in the proposal references' citation network with proposals’ elements, indicates that the student's proposal can be used as a platform for recommending related articles. Hence, the designers of scientific recommender systems, scientific information retrieval systems, digital libraries, and scientific social networks such as LinkedIn, Academia, and ResearchGate can use the elements of articles' citation networks to recommend related articles. In addition, considering the articles’ representative elements as independent units is important not only for similarity measurement but also for keyword expansion and suggesting the appropriate journal to the authors for publishing their articles. According to the determined weight of representative elements and to increase the efficiency of information systems, it is suggested that designers of such systems use the abstracts and the titles of the articles to measure the similarity and avoid calculating the similarity of the texts as a whole unit. This saves time, resources, and energy, presents better results, and users can reach their target and desired information more easily and faster than before. In addition, for indexing articles in databases and search engines, the articles' abstracts and titles can be prioritized to save financial resources and energy.

This article presents an empirical evaluation to investigate the distributional semantic power of abstract, body and full-text, as different text levels, in predicting the semantic similarity using a collection of open access articles from PubMed. The semantic similarity is measured based on two criteria namely, linear MeSH terms intersection and hierarchical MeSH terms distance. As such, a random sample of 200 queries and 20000 documents are selected from a test collection built on CITREC open source code. Sim Pack Java Library is used to calculate the textual and semantic similarities. The nDCG value corresponding to two of the semantic similarity criteria is calculated at three precision points. Finally, the nDCG values are compared by using the Friedman test to determine the power of each text level in predicting the semantic similarity. The results showed the effectiveness of the text in representing the semantic similarity in such a way that texts with maximum textual similarity are also shown to be 77% and 67% semantically similar in terms of linear and hierarchical criteria, respectively. Furthermore, the text length is found to be more effective in representing the hierarchical semantic compared to the linear one. Based on the findings, it is concluded that when the subjects are homogenous in the tree of knowledge, abstracts provide effective semantic capabilities, while in heterogeneous milieus, full-texts processing or knowledge bases is needed to acquire IR effectiveness.

The first few paragraphs of a novel, or any other masterpiece, form one of the key elements of the text: the decisive threshold by which the reader enters the imaginary world and the author passes between silence and discourse. French critics use the term "incipit" to refer to this part and study it from different points of view. After briefly looking at the evolution of this term and putting forward a relevant Persian equivalent, this paper insists on the particular importance of "the First Part of a Text". The paper then recalls its particular roles and how to determine its limits, as well as analyzing concepts such as "Essential Principles of Imaginary World", "Para- Textual Elements" and "Reading Contracts". It should be noted that some examples of Persian novels and short stories will be cited while explaining the above topics and analyzing the said concepts in order to facilitate understanding and grasp of the subject matter.

The responses of impeccable Imams to the question of entering the world into an egg, without changing the volume of both sides, are among the problematic narratives in the field of monotheism, the interpretation of which may be considered as knowing the impossible rational matter by using the analogy of entering the image of objects into the eye, which is not consistent with the content of the original question; the issue does not end there and receiving conflicting dual answers from the Imams to the same question and considering this matter as impossible, are among the other challenges in dealing with this category of hadiths. The present study has used a descriptive-analytical method to explain these hadiths and has found that scholars only accept the apparent indication of the narratives, considering this matter as impossible, and interpret or deny the narratives that have addressed the problem through a metaphorical representation of the world entering the eye. On the other hand, one can interpret some narratives that indicate the possibility of this matter by making changes to both sides and remaining silent, and present a new interpretation based on the theories of "Holography" and "Removal of Empty Space of Atoms" in contemporary sciences, in a way that it can be considered as a solution to resolve the contradiction. In fact, the Imams have sometimes given a precise and real answer to the narrators' current state and needs, and sometimes a metaphorical and tacit answer, so that neither the scholars remain ignorant of the truth nor a pretext for denying the power of Almighty God is created for the ignorant and the opponents. Therefore, merely benefiting from a strange appearance of a narrative or initially understanding it in conflict with reason, knowledge, epistemological geometry, etc., is not a sufficient reason for rejection and denial, and a precise understanding of the hadiths requires multidimensional studies that taking into account the knowledge of contemporary humanity can be one of the requirements that can change the world of hadith understanding.

Recognition and prediction of biological function of proteins based on amino acid sequences is a simple method employed in so many software and operators. However, the sequence similarity does not always imply to similarity of biological function. The aim of this study was to determine the semantic similarity of gene ontology (GO) of six pluripotency factors, Oct4, Sox2, C-Myc, Klf-4, Lin28 and Nanog in six species and evaluate their conformity with their protein sequence similarity and phylogenetic distance. C-myc factor exhibited a significant correlation between phylogenetic distance and protein similarity. The other factors like Sox2, Klf-4 and Lin-28 showed the correct changes of phylogenetic distance and protein similarity, but Nanog and Oct4 factors did not display a correct correlation between two indices because, the increase of protein similarity was not followed with the decrease of phylogenetic distance. Following the study, the protein or nucleotide similarity was assumed as dependent variable and GO similarity in three categories of biological process (BP), molecular function (MF) and, cell component (CC) were expected as the independent variables. With this assumption, regression analysis was accomplished to determine the best model for protein and nucleotide similarity estimation. The protein or nucleotide similarity also displayed a significant regression with GO similarity for C-myc factor and category of BP and CC were selected to estimate protein or nucleotide similarity by model, but a significant regression was not observed for other pluripotency factors for estimation of protein or nucleotide similarity. It means that except of C-myc, GO similarity of other studied pluripotency factors didn’ t reflect the protein or nucleotide similarity. It is suggested that related data for five pluripotency factors, including Oct-4, Sox2, Klf4, Lin28 and Nanog in the six studied species should be reviewed.

Translator's visibility has been a widely accepted concept in the field of Translation Studies. The purpose of this study was to see whether strategies contributing to translator's visibility, i.e. foreignization and neological translations on the textual level, use of footnotes, dedications and prefaces on the paratextual level were used by Persian translators of Silverstein's books. To find answers to the research questions, three poetry books by Silverstein named Where the Sidewalk Ends, A Light in the Attic, and Falling Up, and their two Persian translations by Hamid Khademi and Razi Khodadadi Hirmandi were analyzed.Both translators used the above said strategies, though to different degrees. They were visible in terms of foreignizing strategy with a slight difference of tendency in Where the Sidewalk Ends and Falling Up. Khademi (called Translator 2 in this Research) was found to overtake Hirmandi (referred to as Translator 1) in all the three books, with higher number of neological translations, use of paratextual elements such as footnotes, dedications, and bracketed information. The implications of using such strategies were seen to be of ideological nature, in a way that Translator 2's voice was heard louder and he exerted more power in raising himself to an authorial status.