Deep Linguistic Analysis Research Articles

Centaurs and the Sacred Tree

Abstract Zechariah 4:12 has been perplexing exegetes and scholars throughout the ages, hampering efforts to explain the message of the lampstand vision in Zech 4 as a whole. The current paper will suggest a path towards a solution by showing that the hapax legomenon צנתרות in this verse means “centaurs.” This claim, which found little support in research so far, reaches its full persuasive power by a deep linguistic, exegetical, and cultural-comparative analysis. Namely, it will be argued that phonetically *צנתר fully accords with κένταυρος if the palatalization /k/ > [t͡s] is assumed, and when Jerome’s overlooked reading sinthoroth is considered. This interpretation is further supported by iconographic data which has so far escaped the scholarly discussion of this verse. As it demonstrates, Zech 4 is based on the Sacred Tree motif iconography, in which hybrid entities are only expected.

Hate Speech Detection in Arabic Text: Survey

In light of the continuous expansion of social media content, users have the freedom to express themselves without any boundaries or restrictions over the content. Unfortunately, this has increased the spread of hateful speech among users, which in turn has led to an increase in crimes, murders, and even acts of terrorism. Differentiating hate speech from other offensive language poses a significant challenge, requiring deep linguistic analysis. The task is further complicated by the scarcity of Arabic data resources, which significantly limits the availability of relevant information and the Arabic morphology's richness. This study seeks to conduct a comprehensive review of the current state of research on hate speech-related issues and automatic hate speech detection in Arabic text on social media platforms.

Letters of recommendations and personal statements for rhinology fellowship: A deep learning linguistic analysis.

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ЛІНГВОФОЛЬКЛОРИСТИКА «БЕЙРУТСЬКИХ ОПОВІДАНЬ» АГАТАНГЕЛА КРИМСЬКОГО

The development of a relatively new branch of philological science – lingual folklore studies – requires modern scientists to rethink the scientific and literary heritage of outstanding figures of Ukrainian culture. In particular, Agathangel Krymskyi’s literary works, especially his orientalist compositions, produce a vast area for new scientific research due to the multifaceted nature and uniqueness of the author’s personality. The purpose of the given research is to study Agathangel Krymskyi’s “Beirut Stories” through the prism of lingual folklore studies and the reflection of these studies’ trends in the idiostyle of an orientalist, as well as to outline the writer's contribution to the development of the foundations of lingual folklore studies as a branch of modern philological science. Descriptive, structural, comparative, historical and typological scientific methods were used to realise the set purpose. This article’s scientific novelty lies in attempt to change the viewpoint under which the researchers of Agatangel Krymskyi’s heritage regarded his personality. The contemporaries and followers considered the ethnographic works of the scientist as field studies rather than deep comparative linguistic analysis of two mentalities and cultures. This article analyzes the artist's literary work through the prism of lingual folklore studies, outlines the influence of the abovementioned philological science on the style of Agatangel Krymskyi, as it reinterprets the course of the national literary process and the development of domestic science at the end of the 19th and the beginning of the 20th centuries. The results of the research allow us to assert that Agatangel Krymskyi formulated the basic principles of modern linguistic science in his orientalist fiction since his scientific investigations about the life of the Arab people and Arab folklore are an invaluable source of lingual folklore knowledge.

Фальсификаты рукописей Григория Распутина Новейшего времени и их распознавание

Never before has the handwritten legacy of Grigory Rasputin become the subject of scientific scrutiny. This gap is explained by the fact that Rasputin’s manuscripts has never been separated from the surviving corpus of his texts, which were published only partially and without appropriate textual preparation. Therefore, the author tried to compile a complete corpus of Rasputin’s manuscripts found in the archives and museums of Russia as well as from the rest of the world. This exercise enabled him to identify and record the main linguistic and archaeographic characteristics of Rasputin’s manuscript heritage. The process of compiling a corpora of manuscripts of an individual author and its subsequent critical examination naturally and inevitably leads to the isolation of a number of manuscripts from this corpus for reasons of their inconsistency with the characteristics found in the main body of manuscripts. The recent study of the corpus of Grigory Rasputin’s manuscripts revealed that a number of his newly discovered letters and notes turned out to be alien. It is evident that the accuracy of the conclusions drawn from the examination of written monuments directly depends on the expert’s familiarity with the reference samples in the original. But since the corpus of Rasputin’s manuscripts had not previously been formed, the examination was carried using reproductions, and without a deep source study or linguistic analysis of the manuscripts. This inevitably led to erroneous conclusions, in particular, to the recognition of secondary manuscripts as Rasputin’s originals. To confirm his theses, the author carries out a detailed examination of five manuscripts that have been identified in recent years and have been recognized by experts as originals. The main, but not the only argument with which the author tries to refute the conclusions of experts, is a linguistic analysis. The linguistic characteristics of Grigory Rasputin’s idiom provide us with the main criterion according to which one can reach a verdict on the authenticity of a particular manuscript authored by this historical figure.

17. Jarullah Zamakhshri's Commentary on Manhaj and Style in Kashaf: A Research Review

Popularly known as Al-Kashshaaf is a commentary on the Quran by Al-Zamakhshri written in the 12th century.it is famous for its deep linguistic analysis, demonstrations of supremacy of declamation of Quran and the representation literary elements and figurative speech of Quran. In this tafsir, Al-Zamakhshri referred to many older works and commentaries, especially Zeccac'sMean'il Quran. Islamic jurisprudence is deduced according to the Hanfi school of thought. Some sufi origin and comments are criticized. One of the most criticized aspects of Al-Zamakhshri's interpretation of Quranic verses according to the Mu'tazillah view point. Although Sunni scholars have recognized his book as a landmark. The research concludes by arguing that the tafsir provide a comprehensive source of linguistic, consistent with instinct and reason, and emphasizes that Quran has possessed ability to answers to all of today’s issues. The tafsirKashshaaf is basic key and source for those people who raise the questions on the text of Quran especially orientalists.

Verbal and Nonverbal Discourse of Male Characters in Animated Movies

An observation on gender discourse (verbal and nonverbal communication) is the main concern of various scientific studies. Verbal communication serves to express one’s own views, information and ideas in the form of sound and words. The spoken language involves face-to-face communication, while nonverbal communication (also known as silent language) is the transmission of messages through eye contact, facial expressions, gestures and spatial relationship between the sender and the receiver to convey a message. Researchers have substantiated in earlier studies that verbal and nonverbal communication patterns of men and women are quite different (Eagly & Johnson, 1990; Kring & Gordon, 1998). The goal of the manuscript is to observe and analyse discourse (verbal and nonverbal) of male mice characters in two animated movies (American – “The Great Mouse Detective” and Georgian – “Tsuna and Tsrutsuna”). Observation and qualitative research methods, as well as typological analysis are used during the study. Consequently, two animated movies (“The Great Mouse Detective” (American) and “Tsuna and Trutsuna” (Georgian) have been selected for farther analysis where the main heroes represent mice and rats.The study based on the observational research has revealed the fact that animated movie characters act in similar way male and female gender perform in various situations. Observation on movie characters implies the deep social and linguistic analysis of verbal and non-verbal discourse which is expressed in their actions and interrelations towards each other.

Characterizing Social Relations Via NLP-Based Sentiment Analysis

We investigate and evaluate methods for the characterization of social relations from textual communication context, using e-mail as an example. Social relations are intrinsically characterized by the Cartesian product of weights on various axes (we employ valuation and intensity as examples). The prediction of these characteristics is performed by application of unsupervised learning algorithms on meta-data, communication statistics, and the results of deep linguistic analysis of the message body. Classification of sentiment polarity is chosen as the means of linguistic analysis. We find that prediction accuracy can be improved by introducing limited amounts of additional information.

Application of Supervised Machine Learning to Extract Brain Connectivity Information from Neuroscience Research Articles.

Understanding the complex connectivity structure of the brain is a major challenge in neuroscience. Vast and ever-expanding literature about neuronal connectivity between brain regions already exists in published research articles and databases. However, with the ever-expanding increase in published articles and repositories, it becomes difficult for a neuroscientist to engage with the breadth and depth of any given field within neuroscience. Natural Language Processing (NLP) techniques can be used to mine 'Brain Region Connectivity' information from published articles to build a centralized connectivity resource helping neuroscience researchers to gain quick access to research findings. Manually curating and continuously updating such a resource involves significant time and effort. This paper presents an application of supervised machine learning algorithms that perform shallow and deep linguistic analysis of text to automatically extract connectivity between brain region mentions. Our proposed algorithms are evaluated using benchmark datasets collated from PubMed and our own dataset of full text articles annotated by a domain expert. We also present a comparison with state-of-the-art methods including BioBERT. Proposed methods achieve best recall and [Formula: see text] scores negating the need for any domain-specific predefined linguistic patterns. Our paper presents a novel effort towards automatically generating interpretable patterns of connectivity for extracting connected brain region mentions from text and can be expanded to include any other domain-specific information.

Image in Media Landscape: a Linguistic Discourse Portrait

The review is a critical response to the monograph «Image advertorial as phenomenon of the contemporary media landscape» by L.G. Yegorova. The monograph makes a deep linguistic discourse analysis of the notion of media text, and shows relevant category features and ontological properties of image advertorial. The work is a coherent inter-disciplinary research, where the genre of image advertorial in Russian mass media discourse is studied from the position of linguo-sociological culturology and psycholinguistics, and from a communicative-pragmatic point of view.

Open information extraction

Open information extraction (Open IE) systems aim to obtain relation tuples with highly scalable extraction in portable across domain by identifying a variety of relation phrases and their arguments in arbitrary sentences. The first generation of Open IE learns linear chain models based on unlexicalized features such as Part-of-Speech (POS) or shallow tags to label the intermediate words between pair of potential arguments for identifying extractable relations. Open IE currently is developed in the second generation that is able to extract instances of the most frequently observed relation types such as Verb, Noun and Prep, Verb and Prep, and Infinitive with deep linguistic analysis. They expose simple yet principled ways in which verbs express relationships in linguistics such as verb phrase-based extraction or clause-based extraction. They obtain a significantly higher performance over previous systems in the first generation. In this paper, we describe an overview of two Open IE generations including strengths, weaknesses and application areas.

Cross-Lingual Sentiment Classification from English to Arabic using Machine Translation

Cross-lingual sentiment learning is becoming increasingly important due to the multilingual nature of user-generated content on social media and the scarce resources for languages other than English. However, cross-lingual sentiment learning is a challenging task due to the different distribution between translated data and original data and due to the language gap, i.e. each language has its own ways to express sentiments. This work explores the adaptation of English resources for sentiment analysis to a new language, Arabic. The aim is to design a light model for cross-lingual sentiment classification from English to Arabic, without any manual annotation effort which, at the same time, is easy to build and does not require deep linguistic analysis. The ultimate goal is to find an optimal baseline model and to determine the relation between the noise in the translated data and the accuracy of sentiment classification. Different configurations of several factors are investigated including feature representation, feature reduction methods, and the learning algorithms to find the optimal baseline model. Experiments show that a good classification model can be obtained from translated data regardless of the artificial noise added by machine translation. The results also show a significant cost to automation, and thus the best path to future enhancement is through the inclusion of language-specific knowledge and resources.

Technologies for Complex Intelligent Clinical Data Analysis

The paper presents the system for intelligent analysis of clinical information. Authors describe methods implemented in the system for clinical information retrieval, intelligent diagnostics of chronic diseases, patient's features importance and for detection of hidden dependencies between features. Results of the experimental evaluation of these methods are also presented. Healthcare facilities generate a large flow of both structured and unstructured data which contain important information about patients. Test results are usually retained as structured data but some data is retained in the form of natural language texts (medical history, the results of physical examination, and the results of other examinations, such as ultrasound, ECG or X-ray studies). Many tasks arising in clinical practice can be automated applying methods for intelligent analysis of accumulated structured array and unstructured data that leads to improvement of the healthcare quality. the creation of the complex system for intelligent data analysis in the multi-disciplinary pediatric center. Authors propose methods for information extraction from clinical texts in Russian. The methods are carried out on the basis of deep linguistic analysis. They retrieve terms of diseases, symptoms, areas of the body and drugs. The methods can recognize additional attributes such as "negation" (indicates that the disease is absent), "no patient" (indicates that the disease refers to the patient's family member, but not to the patient), "severity of illness", disease course", "body region to which the disease refers". Authors use a set of hand-drawn templates and various techniques based on machine learning to retrieve information using a medical thesaurus. The extracted information is used to solve the problem of automatic diagnosis of chronic diseases. A machine learning method for classification of patients with similar nosology and the methodfor determining the most informative patients'features are also proposed. Authors have processed anonymized health records from the pediatric center to estimate the proposed methods. The results show the applicability of the information extracted from the texts for solving practical problems. The records ofpatients with allergic, glomerular and rheumatic diseases were used for experimental assessment of the method of automatic diagnostic. Authors have also determined the most appropriate machine learning methods for classification of patients for each group of diseases, as well as the most informative disease signs. It has been found that using additional information extracted from clinical texts, together with structured data helps to improve the quality of diagnosis of chronic diseases. Authors have also obtained pattern combinations of signs of diseases. The proposed methods have been implemented in the intelligent data processing system for a multidisciplinary pediatric center. The experimental results show the availability of the system to improve the quality of pediatric healthcare.

Abstractive Summarization

Customization of information from web documents is an immense job that involves mainly the shortening of original texts. This task is carried out using summarization techniques. In general, an automatically generated summary is of two types – extractive and abstractive. Extractive methods use surface level and statistical features for the selection of important sentences, without considering the meaning conveyed by those sentences. In contrast, abstractive methods need a formal semantic representation, where the selection of important components and the rephrasing of the selected components are carried out using the semantic features associated with the words as well as the context. Furthermore, a deep linguistic analysis is needed for generating summaries. However, the bottleneck behind abstractive summarization is that it requires semantic representation, inference rules and natural language generation. In this paper, The authors propose a semi-supervised bootstrapping approach for the identification of important components for abstractive summarization. The input to the proposed approach is a fully connected semantic graph of a document, where the semantic graphs are constructed for sentences, which are then connected by synonym concepts and co-referring entities to form a complete semantic graph. The direction of the traversal of nodes is determined by a modified spreading activation algorithm, where the importance of the nodes and edges are decided, based on the node and its connected edges under consideration. Summary obtained using the proposed approach is compared with extractive and template based summaries, and also evaluated using ROUGE scores.

A Hybrid Method for Open Information Extraction Based on Shallow and Deep Linguistic Analysis

A Hybrid Method for Open Information Extraction Based on Shallow and Deep Linguistic Analysis

Computerization of a ‘Controlled Language’ to Write Medical Standard Operating Procedures (SOPs)

Abstract Accreditation of hospitals includes items regarding the existence of Standard Operating Procedures (SOPs); but these documents can be sources of misunderstanding and patient's safety may be jeopardized. We proposed a solution based on the Controlled Language (CL) concept and developed software services to make CL user-friendly to writers. We carried out: 1) deep linguistic analysis of SOP corpora in two medical domains; 2) language modelling to establish two adapted CLs, 3) improvement of home-made CL Authoring Software by developing software modules and a collaborative corpus-based web-accessible platform for the building of terminological and non-terminological resources, 4) evaluation through focus groups and computer-aided CL-writing test sessions. Health professionals and linguists cooperated closely in a field that is quite new to the health domain. The optimized Prolipsia CL Authoring Software appeared to be a good compromise between users’ needs and CL requirements. All actors agreed that benefits would be gained by using the proposed tools, in terms of patient safety and of work organization, institutional cohesion, and decreased liabilities. They also suggested that software solutions able to analyse the quality of existing texts and help correct them would better fit the situation of institutions which have already got a large corpus of (unsatisfactory) texts at their disposal. Such software is currently at an advanced stage of development, with a first version available.

Dementis. Un dibujo que se dio cuenta

The author of “Dementis, a drawing that realized,” tells his central experiences around the character.He proposes Dementis as a figure that summarizes the key elements to understand the essentialof human: its ability to rebuild their own plot and its ability to take distance itself through sense ofhumor. Dementis enter into the psychotherapeutic topic as the representative image of the manwho in spite of suffering makes a creatively breaks with the rigidity of the affections. Rosa Carrascoperforms a deep linguistic analysis and content of the work that places Dementis like a book richin meanings, comparing with important characters of the world literature.

Leer a Maquiavelo, traducir el Corán: Muḥammad, príncipe y legislador en el <i>Alcorano di Macometto</i> (Venecia, 1547)

This article focuses on L’Alcorano di Macometto , written by Giovanni Battista Castrodardo from Belluno (1588 ca.-1517 ca.), and published by Andrea Arrivabene at Venice in 1547. Although this text is considered to be a paraphrase of the Latin translation of the Qur’ān made by Robert of Ketton (1143 AD), a closer look shows that it was a handy pocket encyclopedia of the history of Islam and the Ottoman empire. Following a suggestion of Alessandro D’Ancona (1889), I analyze the historical texts published in the introduction to the Alcorano and especially Castrodardo’s biography of the prophet Muḥammad. This biography includes a prayer addressed to Muḥammad through the character of the monk Sergio-Bahīrā in order to persuade him to dethrone Emperor Heraclius. A deep linguistic analysis of this speech sheds light on Castrodardo’s reading of Machiavelli. While this new portrayal of Muhammad is based on medieval sources, it presents the Prophet of Islam as a distinctly Renaissance Prince, a lawgiver and a “Prophet Armed”. It also reflects the pro-Ottoman and anti-Imperial hopes of Italian circles linked to the French embassy in Venice.

Adaptive‐capacity and robust natural language watermarking for agglutinative languages

ABSTRACTWe present a robust and adaptive‐capacity watermarking algorithm for agglutinative languages. All processes, including the selection of sentences to be watermarked, watermark embedding, and watermark extraction, are based on syntactic dependency trees. We show that it is more robust to use syntactic dependency trees than the surface forms of sentences in text watermarking. For the agglutinative languages, we embed watermark using the two main characteristics of the languages. First, because a word consists of several morphemes, we can watermark sentences using morphological division/combination without deep linguistic analysis. Second, they permit relatively free word order, so we can move a syntactic constituent within its clause. Finally, to increase the information‐hiding capacity, we adaptively compute the number of watermark bits to be embedded for each sentence.We perform three kinds of evaluation: perceptibility, robustness, and capacity of our method. High capacity is achieved by dynamically determining possibly embedded watermark bits for each sentence. The secret rank based on a syntactic dependency tree strengthens robustness of our method. Finally, we show that the displacement of syntactic constituents and morphological division/combination does not affect the style and naturalness of the text. Copyright © 2011 John Wiley & Sons, Ltd.

Finding Opinionated Blogs Using Statistical Classifiers and Lexical Features

This paper systematically exploited various lexical features for opinion analysis on blog data using a statistical learning framework. Our experimental results using the TREC Blog track data show that all the features we explored effectively represent opinion expressions, and different classification strategies have a significant impact on opinion classification performance. We also present results when combining opinion analysis with the retrieval component for the task of retrieving relevant and opinionated blogs. Compared with the best results in the TREC evaluation, our system achieves reasonable performance, but does not rely on much human knowledge or deep level linguistic analysis.