Transcription Of Text Research Articles

Identification and analysis of communication barriers for construction projects with time uncertainties

PurposeThe purpose of this study is to improve communication success through barrier identification and analysis so that the identified barriers can help project teams establish effective information-exchange strategies.Design/methodology/approachThe recent publications on construction communication about time management are reviewed. Then, the semi-structured interviews are performed with both questionnaires and audio recordings (n1 = 18). Next, the collected data are analyzed using both statistical measures on the questionnaire survey and qualitative coding analysis on the text transcripts from an audio recording. Particularly, the identified barriers are substantiated using a scientometrics approach based on the published articles (2011–2020, n2 = 52,915) for purposeful information-sharing solutions in construction time management. Furthermore, the intervention strategies from the top 10 most-cited articles are analyzed and validated by comparisons with the results from construction surveys and relevant studies.FindingsBased on the discussed communication difficulties, five main barriers were identified during time-cost risk management: probability and statistical concepts, availability of data from external resources, details of team member experiences, graphics (and graphical presentation skills), and spatial and temporal (a.k.a. 4D) simulation skills. For the improvement of communication skills and presentation quality regarding probability and statistical concepts, project teams should emphasize context awareness, case studies and group discussions. Details of communication techniques can be adjusted based on the backgrounds, experiences and expectations of team members.Research limitations/implicationsThe dataset n1 has both size and duration limits because of the availability of the invited industry professionals. The dataset n2 considers the literature from 2011 to 2020. Any before-the-date and unpublished studies are not included in the study.Practical implicationsA thorough comprehension of communication barriers can help project teams develop speaking, writing and analytical thinking skills that will enable the teams to better deliver ideas, thoughts and meanings. Additionally, the established discussion on barrier-removal strategies may enhance time management effectiveness, reduce project delays, avoid confusion and misunderstanding and save rework costs.Social implicationsThis research calls for the awareness of communication barriers in construction project execution and team collaboration. The identified barriers and the established solutions enrich the approaches of construction companies to share information with communities and society.Originality/valueThis is the first identification model for communication barriers in the time management of the construction industry to the authors' knowledge. The influencing factors and the countermeasures of communication difficulties highlighted by the research were not examined systematically and holistically in previous studies. The findings provide a new approach to facilitate the development of powerful communication strategies and to improve project execution.

Training RNN language models on uncertain ASR hypotheses in limited data scenarios

Training domain-specific automatic speech recognition (ASR) systems requires a suitable amount of data comprising the target domain. In several scenarios, such as early development stages, privacy-critical applications, or under-resourced languages, only a limited amount of in-domain speech data and an even smaller amount of manual text transcriptions, if any, are available. This motivates the study of ASR language model (LM) training on a limited amount of in-domain speech data. Early works have attempted training of n-gram LMs from ASR N-best lists and lattices but training and adaptation of recurrent neural network (RNN) LMs from ASR transcripts has not received attention. In this work, we study training and adaptation of RNN LMs using alternate, uncertain ASR hypotheses embedded in ASR confusion networks obtained from target domain speech data. We explore different methods for training the RNN LMs to deal with the uncertain input sequences. The first method extends the cross-entropy objective into a Kullback–Leibler (KL) divergence based training loss, the second method formulates a training loss based on a hidden Markov model (HMM), and the third method performs training on paths sampled from the confusion networks. These methods are applied to limited data setups including telephone and meeting conversation datasets. Performance is evaluated under two settings wherein no manual transcriptions or a small amount of manual transcriptions are available to aid the training. Moreover, a model adaptation setting is also evaluated wherein the RNN LM is pre-trained on an out-of-domain conversational corpus. Overall the sampling method for training RNN LMs on ASR confusion networks performs the best, and results in up to 12% relative reduction in perplexity on the meeting dataset as compared to training on ASR 1-best hypotheses, without any manual transcriptions. However, the perplexity reductions do not translate into equivalent WER reductions. A detailed analysis of the perplexity reductions obtained by the different methods is performed in order to understand this effect.

Explainable Alzheimer’s Disease Detection Using Linguistic Features from Automatic Speech Recognition

Introduction: Alzheimer’s disease (AD) is the most prevalent type of dementia and can cause abnormal cognitive function and progressive loss of essential life skills. Early screening is thus necessary for the prevention and intervention of AD. Speech dysfunction is an early onset symptom of AD patients. Recent studies have demonstrated the promise of automated acoustic assessment using acoustic or linguistic features extracted from speech. However, most previous studies have relied on manual transcription of text to extract linguistic features, which weakens the efficiency of automated assessment. The present study thus investigates the effectiveness of automatic speech recognition (ASR) in building an end-to-end automated speech analysis model for AD detection. Methods: We implemented three publicly available ASR engines and compared the classification performance using the ADReSS-IS2020 dataset. Besides, the SHapley Additive exPlanations algorithm was then used to identify critical features that contributed most to model performance. Results: Three automatic transcription tools obtained mean word error rate texts of 32%, 43%, and 40%, respectively. These automated texts achieved similar or even better results than manual texts in model performance for detecting dementia, achieving classification accuracies of 89.58%, 83.33%, and 81.25%, respectively. Conclusion: Our best model, using ensemble learning, is comparable to the state-of-the-art manual transcription-based methods, suggesting the possibility of an end-to-end medical assistance system for AD detection with ASR engines. Moreover, the critical linguistic features might provide insight into further studies on the mechanism of AD.

Seeger Lecture, Society for Ethnomusicology Virtual Meeting 2021

Seeger Lecture, Society for Ethnomusicology Virtual Meeting 2021

ВИКОРИСТАННЯ ЛІНГВІСТИЧНИХ КОРПУСІВ У ВИВЧЕННІ СЛОВОБУДОВИ ДІАЛЕКТІВ АМЕРИКАНСЬКОГО ВАРІАНТУ СУЧАСНОЇ АНГЛІЙСЬКОЇ МОВИ

The article deals with the principles of using the concepts of corpus linguistics in studying American English dialects. The origins of corpus linguistics of American English dialects are considered, and developments in corpus linguistics by W. Francis and G. Kuchera are investigated (Brown Corpus, 1964). The concept of «corpus linguistics» is defined, and some problematic aspects are described. The qualifications for using the corpus-linguistic approach in the study of dialect grammatical features of American English are defined. The grammatical differences between British and American English are outlined. "Spoken English" is defined as how the latter was dialectical in the United States. The American dialects and Standardized American English are distinguished. Attention is focused on comparative and legal developments in corpus linguistics of British and American English (Brown and Lancaster Corps). Discographic studies of spoken American (dialectical) are mentioned: Lancaster/IBM SEC and Corpus of Spoken American English. The peculiarities of using the International Corpus of English, which studies British and American English in philological and methodological unity, are outlined. The processes of expanding the language corpus of the American English language from 1990 till the present (through the prism of statistics from The Bank of English) are determined. The main methodological developments in the systematization of the corpus of American English from the point of view of the theory of Corpus Linguistics are indicated: software corpus; problem-parametric industry selections; text transcription; search and system work; description encoding of "preserved" dialect words and word formations.

Finding Structure in One Child's Linguistic Experience.

Neural network models have recently made striking progress in natural language processing, but they are typically trained on orders of magnitude more language input than children receive. What can these neural networks, which are primarily distributional learners, learn from a naturalistic subset of a single child's experience? We examine this question using a recent longitudinal dataset collected from a single child, consisting of egocentric visual data paired with text transcripts. We train both language-only and vision-and-language neural networks and analyze the linguistic knowledge they acquire. In parallel with findings from Jeffrey Elman'sseminal work, the neural networks form emergent clusters of words corresponding to syntactic (nouns, transitive and intransitive verbs) and semantic categories (e.g., animals and clothing), based solely on one child's linguistic input. The networks also acquire sensitivity to acceptability contrasts from linguistic phenomena, such as determiner-noun agreement and argument structure. We find that incorporating visual information produces an incremental gain in predicting words in context, especially for syntactic categories that are comparatively more easily grounded, such as nouns and verbs, but the underlying linguistic representations are not fundamentally altered. Our findings demonstrate which kinds of linguistic knowledge are learnable from a snapshot of a single child's real developmentalexperience.

Analyzing lower half facial gestures for lip reading applications: Survey on vision techniques

Lip reading has gained popularity due to the proliferation of emerging real-world applications. This article provides a comprehensive review of benchmark datasets available for lip-reading applications and pioneering works that analyze lower facial cues for lip-reading applications. A comprehensive review of lip reading applications is broadly classified into five distinct applications: Lip Reading Biometrics (LRB), Audio Visual Speech Recognition (AVSR), Silent Speech Recognition (SSR), Voice from Lips, and Lip HCI (Human–computer interaction). LRB entails extensive research in the fields of authentication and liveness detection. AVSR covers key findings that have contributed significantly to applications such as voice assistants, video-to-text transcription, hearing aids, and pronunciation-correcting systems. SSR analyzes the efforts made for silent-video-to-text transcription and surveillance camera applications. The voice from lips section discusses applications such as voice for the voiceless and vision-infused speech inpainting. In lip HCI, LR-HCI for smartphones, smart TVs, computers, robots, and musical instruments is reviewed in detail. Comprehensive coverage is given to cutting-edge techniques in computer vision, signal processing, machine learning, and deep learning. The advancements that aid the system in learning to lip-read and authenticate lip gestures, generate text transcription, synthesize voice based on lip movements, and control systems via lip movements (lip HCI) are covered. The work concludes by highlighting the limitations of existing frameworks, the road maps of each application illustrating the evolution of techniques employed over time, and future research avenues in lip-reading applications.

An Exploratory Study of ICU Pediatric Nurses' Feelings and Coping Strategies after Experiencing Children Death.

This study aims to explore the feelings and experiences of nursing staff when faced with the death of a pediatric patient in the ICU. A qualitative study based on hermeneutic phenomenology was conducted through semi-structured interviews. Ten nurses (30% of staff) from the Pediatric Intensive Care Unit of a referral hospital were interviewed in April 2022. Text transcripts were analysed using latent content analysis. Content analysis indicated that the interviewees had feelings of sadness and grief; they had a misconception of empathy. They had no structured coping strategies, and those they practiced were learned through personal experience, not by specific training; they reported coping strategies such as peer support, physical exercise, or strengthening ties with close family members, especially their children. The lack of skills to cope with the death and the absence of support from personnel management departments were acknowledged. This can lead to the presence of compassion fatigue. The feelings that PICU nurses have when a child they care for die are negative feelings and sadness, and they possess coping strategies focused on emotions learned from their own experience and without institutional training support. This situation should not be underestimated as they are a source of compassion fatigue and burnout.

Capturing populist elements in mediated discourse

The article measures the degree of populist elements present in party leaders’ discourses delivered as a part of their appearances in TV debates during the run up to the 2020 Slovak parliamentary elections. With the use of the ‘holistic grading’ approach, we empirically capture the presence and prominence of populist elements in textual transcripts of speeches delivered by 4 opposition party leaders. We start by defining populism as a discourse which can express a set of unique ideas and then follow up with arguments for the use of party leaders’ communication as the object of analysis. We then proceed to operationalize the exact coding process of the ‘holistic grading’ method which is used in our analysis. The measurement results quantitatively represent the degree of populist elements in the communication delivered during the electoral campaign. We note that the ‘holistic grading’ method was successfully able to capture populist elements in the performances of actors who have been previously labelled populist. We find consistent use of populist discourse in the performances of Boris Kollár, Igor Matovič, and Marián Kotleba, with the latter two producing several extremely populist performances. We then discuss our results and further implications derived from the collected data.

Nautilus

When a digital collection has been processed by OCR, the usability expectationsof patrons and researchers are high. While the former expect full textsearch to return all instances of terms in historical collections correctly, the latterare more familiar with the impacts of OCR errors but would still like toapply big data analysis or machine-learning methods. All of these use casesdepend on high quality textual transcriptions of the scans. This is why theNational Library of Luxembourg (BnL) has developed a pipeline to improveOCR for existing digitised documents. Enhancing OCR in a digital library notonly demands improved machine learning models, but also requires a coherentreprocessing strategy in order to apply them efficiently in production systems. The newly developed software tool, Nautilus, fulfils these requirements usingMETS/ALTO as a pivot format. The BnL has open-sourced it so that otherlibraries can re-use it on their own collections. This paper covers the creationof the ground truth, the details of the reprocessing pipeline, its production useon the entirety of the BnL collection, along with the estimated results. Basedon a quality prediction measure, developed during the project, approximately28 million additional text lines now exceed the quality threshold.

On Practical Uses of Ten Sefirot: Material Readings in an Early Modern Kabbalistic Collectaneum (MS Michael 473)

AbstractBy the end of the sixteenth century, textual manifestations of kabbalah—a variety of Jewish mysticism that first emerged in medieval Provence and Catalonia—achieved the status of elite but authoritative lore in Eastern and Central Europe, even if at times they stirred religious opposition. At the same time, and especially in the seventeenth century, the so-called practical kabbalah, associated with magic and a talismanic approach to religious ritual, gained substantial popularity among Ashkenazi (i.e., Eastern and Central European) Jews. This study centers on a multiple-text and composite codex, Oxford-Bodleian MS Michael 473, and throws into relief the dynamics of circulation of various kabbalistic traditions in early modern Eastern and Central Europe. By zooming in on a single codex, this article foregrounds the hermeneutic potential of contextual reading of texts in complex manuscripts and of interpreting material choices taken by their cocreators. It does so with a methodological agenda that goes beyond tracing of authorial genealogies, and beyond the sociology of texts and their producers, toward exploring the interpretive relations of literary and material form in early modern handwritten kabbalistic texts. The article showcases a single textual unit, Qabbalat ‘Eser Sefirot, that MS Michael 473 contains, in order to focus on the position of practical kabbalistic texts and practices within the spectrum of kabbalistic traditions of seventeenth- and early eighteenth-century Eastern and Central Europe, ushered in by the contemporary modes of reading and transcription of texts.

Gapassipi

Gapassipi

A study of the role of surface parameters on the relationship between biotribology and cognitive perception involved in the design of tactile graphics

Tactual encoding of information is an essential aspect of communication in many aspects of life, but especially so for persons with visual impairments. The most common encoding is the use of topological features on a static medium such as paper. While Braille is widely used and effective for text transcription, the effective use of tactile graphics to communicate spatial or graphical information is less standardized. Tactile graphical techniques typically employ collections of individual topological features or texture patterns to communicate spatial information (maps, plots, diagrams), with the goal of minimizing reader confusion among the elements. In this study, the authors investigated the tribology and perception aspects of this complex issue in order to identify fundamental surface parameters which play a dominant role. Using texture specification guidelines published by the Braille Authority of North America (BANA), the investigators produced raised parallel-ridge textures on thermally activated tactile paper. The texture parameters modified included the width of ridges, spacing between ridges, and the inclination of the ridges to the pattern boundaries. Subjects reported their perception of tactile similarity or difference between members of texture pairs that were presented under a blind. Measurements of friction force were also collected for these textures to determine if friction mechanisms were most responsible for perceptive ability, or if other surface attributes played a dominant role. The study found that modifying pattern pitch improved perceptive ability to differentiate between adjacent mismatched texture patterns, but that ridge width had a deleterious effect. The results suggested that perceptive ability was not strongly related to various friction mechanisms proposed, but was closely correlated with changes in the number of pattern ridges in contact with the fingertip during tactile graphic exploration.

Classification and analysis of text transcription from Thai depression assessment tasks among patients with depression.

Depression is a serious mental health disorder that poses a major public health concern in Thailand and have a profound impact on individuals' physical and mental health. In addition, the lack of number to mental health services and limited number of psychiatrists in Thailand make depression particularly challenging to diagnose and treat, leaving many individuals with the condition untreated. Recent studies have explored the use of natural language processing to enable access to the classification of depression, particularly with a trend toward transfer learning from pre-trained language model. In this study, we attempted to evaluate the effectiveness of using XLM-RoBERTa, a pre-trained multi-lingual language model supporting the Thai language, for the classification of depression from a limited set of text transcripts from speech responses. Twelve Thai depression assessment questions were developed to collect text transcripts of speech responses to be used with XLM-RoBERTa in transfer learning. The results of transfer learning with text transcription from speech responses of 80 participants (40 with depression and 40 normal control) showed that when only one question (Q1) of "How are you these days?" was used, the recall, precision, specificity, and accuracy were 82.5%, 84.65, 85.00, and 83.75%, respectively. When utilizing the first three questions from Thai depression assessment tasks (Q1 - Q3), the values increased to 87.50%, 92.11%, 92.50%, and 90.00%, respectively. The local interpretable model explanations were analyzed to determine which words contributed the most to the model's word cloud visualization. Our findings were consistent with previously published literature and provide similar explanation for clinical settings. It was discovered that the classification model for individuals with depression relied heavily on negative terms such as 'not,' 'sad,', 'mood', 'suicide', 'bad', and 'bore' whereas normal control participants used neutral to positive terms such as 'recently,' 'fine,', 'normally', 'work', and 'working'. The findings of the study suggest that screening for depression can be facilitated by eliciting just three questions from patients with depression, making the process more accessible and less time-consuming while reducing the already huge burden on healthcare workers.

Predicting Generalized Anxiety Disorder From Impromptu Speech Transcripts Using Context-Aware Transformer-Based Neural Networks: Model Evaluation Study.

The ability to automatically detect anxiety disorders from speech could be useful as a screening tool for an anxiety disorder. Prior studies have shown that individual words in textual transcripts of speech have an association with anxiety severity. Transformer-based neural networks are models that have been recently shown to have powerful predictive capabilities based on the context of more than one input word. Transformers detect linguistic patterns and can be separately trained to make specific predictions based on these patterns. This study aimed to determine whether a transformer-based language model can be used to screen for generalized anxiety disorder from impromptu speech transcripts. A total of 2000 participants provided an impromptu speech sample in response to a modified version of the Trier Social Stress Test (TSST). They also completed the Generalized Anxiety Disorder 7-item (GAD-7) scale. A transformer-based neural network model (pretrained on large textual corpora) was fine-tuned on the speech transcripts and the GAD-7 to predict whether a participant was above or below a screening threshold of the GAD-7. We reported the area under the receiver operating characteristic curve (AUROC) on the test data and compared the results with a baseline logistic regression model using the Linguistic Inquiry and Word Count (LIWC) features as input. Using the integrated gradient method to determine specific words that strongly affect the predictions, we inferred specific linguistic patterns that influence the predictions. The baseline LIWC-based logistic regression model had an AUROC value of 0.58. The fine-tuned transformer model achieved an AUROC value of 0.64. Specific words that were often implicated in the predictions were also dependent on the context. For example, the first-person singular pronoun "I" influenced toward an anxious prediction 88% of the time and a nonanxious prediction 12% of the time, depending on the context. Silent pauses in speech, also often implicated in predictions, influenced toward an anxious prediction 20% of the time and a nonanxious prediction 80% of the time. There is evidence that a transformer-based neural network model has increased predictive power compared with the single word-based LIWC model. We also showed that the use of specific words in a specific context-a linguistic pattern-is part of the reason for the better prediction. This suggests that such transformer-based models could play a useful role in anxiety screening systems.

Affective social anthropomorphic intelligent system

Human conversational styles are measured by the sense of humor, personality, and tone of voice. These characteristics have become essential for conversational intelligent virtual assistants. However, most of the state-of-the-art intelligent virtual assistants (IVAs) are failed to interpret the affective semantics of human voices. This research proposes an anthropomorphic intelligent system that can hold a proper human-like conversation with emotion and personality. A voice style transfer method is also proposed to map the attributes of a specific emotion. Initially, the frequency domain data (Mel-Spectrogram) is created by converting the temporal audio wave data, which comprises discrete patterns for audio features such as notes, pitch, rhythm, and melody. A collateral CNN-Transformer-Encoder is used to predict seven different affective states from voice. The voice is also fed parallelly to the deep-speech, an RNN model that generates the text transcription from the spectrogram. Then the transcripted text is transferred to the multi-domain conversation agent using blended skill talk, transformer-based retrieve-and-generate generation strategy, and beam-search decoding, and an appropriate textual response is generated. The system learns an invertible mapping of data to a latent space that can be manipulated and generates a Mel-spectrogram frame based on previous Mel-spectrogram frames to voice synthesize and style transfer. Finally, the waveform is generated using WaveGlow from the spectrogram. The outcomes of the studies we conducted on individual models were auspicious. Furthermore, users who interacted with the system provided positive feedback, demonstrating the system’s effectiveness.

Unlocking the Power of Voice for Financial Risk Prediction: A Theory-Driven Deep Learning Design Approach

Unstructured multimedia data (text and audio) provides unprecedented opportunities to derive actionable decision-making in the financial industry, in areas such as portfolio and risk management. However, due to formidable methodological challenges, the promise of business value from unstructured multimedia data has not materialized. In this study, we use a design science approach to develop DeepVoice, a novel nonverbal predictive analysis system for financial risk prediction, in the setting of quarterly earnings conference calls. DeepVoice forecasts financial risk by leveraging not only what managers say (verbal linguistic cues) but also how managers say it (vocal cues) during the earnings conference calls. The design of DeepVoice addresses several challenges associated with the analysis of nonverbal communication. We also propose a two-stage deep learning model to effectively integrate managers’ sequential vocal and verbal cues. Using a unique dataset of 6,047 earnings call samples (audio recordings and textual transcripts) of S&P 500 firms across four years, we show that DeepVoice yields remarkably lower risk forecast errors than that achieved by previous efforts. The improvement can also translate into nontrivial economic gains in options trading. The theoretical and practical implications of analyzing vocal cues are discussed.

The “Who Is He?” Commentary Attributed to St. Gregory of Narek [Արուսյակ Թամրազյան. Գրիգոր Նարեկացուն Վերագրվող Մեկնութիւն Ո՞վ Է Դա-ի երկը] by Arusyak T‘amrazyan

Reviewed by: The “Who Is He?” Commentary Attributed to St. Gregory of Narek [Արուսյակ Թամրազյան. Գրիգոր Նարեկացուն Վերագրվող Մեկնութիւն Ո՞վ Է Դա-ի երկը] by Arusyak T‘amrazyan Arpi Nakashian (bio) Arusyak T‘amrazyan, The “Who Is He?” Commentary Attributed to St. Gregory of Narek [Արուսյակ Թամրազյան. Գրիգոր Նարեկացուն Վերագրվող Մեկնութիւն Ո՞վ Է Դա-ի երկը]. Yerevan, Nairi, 2019, 512 pp. Available online at https://www.matenadaran.am/ftp/el_gradaran/A.T'amrazyan.pdf. The scholarly world is indebted to the late Hrach‘ya T‘amrazyan, former head of the Matenadaran, for his extensive research and writing on the Narekean School, particularly for his three volumes of essays entitled Grigor Narekats‘i and the School of Narek [Գրիգոր նարեկացին եւ նարեկեան դպրոցը], published by Nairi in 2013, 2016 and 2017. Now, his daughter, Matenadaran research scholar Arusyak T‘amrazyan, has added to that legacy with a new volume on one of Grigor Narekats‘i’s lesser-known works: his commentary on Job 38–39, a passage often referred to by its first words, “Who Is He?” Edited by Abraham Terian and Gohar Muradyan, the book comprises four chapters, a full transcription of the Narek text, five appendices, and is followed by a bibliography of manuscript and printed sources (pp. 499–508). The subject of T‘amrazyan’s work is a significant but understudied piece attributed to St. Gregory of Narek on the Book of Job. Her writing is a valuable addition to the various works already existing on the Book of Job in Armenian, including Claude Cox’s study of the [End Page 187] Armenian Book of Job1 and more recently, Elya Saribekyan’s article on “Theological Issues in Grigor Tat‘ewats‘i’s Scholia on Job.”2 The four chapters of the volume make up a loosely constructed description of various aspects of the evolution of the commentaries on the Book of Job 38–39 in the Armenian tradition, beginning with their basis in Cyril of Jerusalem’s catechetical lectures. Each chapter section is a short, unique argument that can stand alone or serve as a stepping-stone to the next. The author uses her footnotes to provide a running subtext. T‘amrazyan’s greatest contributions in this volume are two: the first is her reconstruction of the Narek text. Narek’s commentary was published in volume 12 of the series Armenian Classical Authors [Մատենագիրք Հայոց], pp. 885–910, with a one-page introduction by Hakob K‘yoseyan. However, the printed text is based on the only surviving manuscript of the work, Venice ms. 1030/339, dated 1299. The manuscript is severely damaged in many places, and the first pages are entirely missing. T‘amrazyan reconstructs significant parts of the defective text, based both on close physical examination of the manuscript and on comparative study with other commentaries that make use of the Narek material. An extensive footnote at the beginning of the reconstructed text (n. 360, pp. 358–359) describes T‘amrazyan’s reconstruction process. Facsimile fragments from the manuscript are included on pp. 115–116, with a detailed description. T‘amrazyan’s second major contribution in this volume is her comparison of the Narek commentary on Job 38–39 with other relevant works. In chapter 1, she primarily highlights resonance between the commentary and Grigor Narekats‘i’s other works, in particular his Book of Prayers, Festal Odes, and Commentary on the Song of Songs. Among her observations, she emphasizes that the underlying theme of the Book of Prayers is Job’s story, and that Narek, in the persona of Job, is the subject of his own narration. In chapter 2, [End Page 188] she traces references to Job 38–39 in Narekats‘i’s works. She examines themes and symbolism, focusing special attention on the themes and imagery of fragrance and sacrifice. The closing section of the chapter briefly suggests a connection between the sacrifice imagery found in the commentary and the reading of Job 38–39 during Lent. In addition, she discusses the themes’ biblical background or context and compares them with themes that appear in Armenian hymnography. While chapters 1–2 focus on the interconnections within Grigor Narekats‘i’s own works, chapter 3 explores connections with the commentaries of other Church fathers who have written on the Book of Job. The chapter begins with an introduction to the sole surviving manuscript...

The Topical Theme Depicted in the Talks of Education in Indonesian’s TEDx

The education-related TEDx presentations delivered by Indonesian speakers are a distinct linguistic phenomenon. It is a language in use where the message is communicated entirely through spoken speech as opposed to speaking itself. In light of systemic functional linguistics (SFL), this study examines the management of topical theme in relation to the message organisation of TE in TEDx. The aim of this study was to pinpoint and define the message's current theme in the TEDx format. Using the documentation method, the information was gleaned from an official TEDx broadcaster online. Two methods of analysis have been used: detailed text fragmentation and transcription. Following the analysis, the findings were presented and their theoretical SFL-based interpretation was given. This study shows that the phrasal and clausal forms of language, which are the constructional units of language, have taken on the topical theme in TE of TEDx. Phrasal constructs can be categorised into four different categories: noun phrase (NP), verb phrase (VP), adjective phrase (AP), and prepositional phrase (PP). The topical theme has also been functionalized as a component of other themes and the chain for coherence. This study recommended more examination of the organisation of the other component of the message. To ensure that the entire description of the thematic structure of TE in TEDx is respected, the combinatory analysis of theme and rheme will be essential.

A Comparison of Different Topic Modeling Methods through a Real Case Study of Italian Customer Care

The paper deals with the analysis of conversation transcriptions between customers and agents in a call center of a customer care service. The objective is to support the analysis of text transcription of human-to-human conversations, to obtain reports on customer problems and complaints, and on the way an agent has solved them. The aim is to provide customer care service with a high level of efficiency and user satisfaction. To this aim, topic modeling is considered since it facilitates insightful analysis from large documents and datasets, such as a summarization of the main topics and topic characteristics. This paper presents a performance comparison of four topic modeling algorithms: (i) Latent Dirichlet Allocation (LDA); (ii) Non-negative Matrix Factorization (NMF); (iii) Neural-ProdLDA (Neural LDA) and Contextualized Topic Models (CTM). The comparison study is based on a database containing real conversation transcriptions in Italian Natural Language. Experimental results and different topic evaluation metrics are analyzed in this paper to determine the most suitable model for the case study. The gained knowledge can be exploited by practitioners to identify the optimal strategy and to perform and evaluate topic modeling on Italian natural language transcriptions of human-to-human conversations. This work can be an asset for grounding applications of topic modeling and can be inspiring for similar case studies in the domain of customer care quality.