Text Corpus Research Articles

Rights and Wrongs in Talk of Mind-Reading Technology.

This article examines the idea of mind-reading technology by focusing on an interesting case of applying a large language model (LLM) to brain data. On the face of it, experimental results appear to show that it is possible to reconstruct mental contents directly from brain data by processing via a chatGPT-like LLM. However, the author argues that this apparent conclusion is not warranted. Through examining how LLMs work, it is shown that they are importantly different from natural language. The former operates on the basis of nonrational data transformations based on a large textual corpus. The latter has a rational dimension, being based on reasons. Using this as a basis, it is argued that brain data does not directly reveal mental content, but can be processed to ground predictions indirectly about mental content. The author concludes that this is impressive but different in principle from technology-mediated mind reading. The applications of LLM-based brain data processing are nevertheless promising for speech rehabilitation or novel communication methods.

COMPARISON OF THE SPEECH TEXTS OF INDONESIAN PRESIDENT JOKO WIDODO AND PRESIDENT SUSILO BAMBANG YUDHOYONO: STUDY USING A CORPUS LINGUISTIC APPROACH

Purpose: This research has several objectives. First, determine lexical density and compare the lexical density. Second, to determine the key lexical density and compare the key lexical density. Third, to test the independence of the relationship between lexical variations and the text of President Joko Widodo's and President Susilo Bambang Yudhoyono's speeches. Theoretical Reference: The theoretical basis used in this research is the lexical analysis approach in linguistics. The application of lexical perspective analysis is expected to be able to review the communication used by each individual. The theoretical lexical discussion will also use a statistical independence analysis approach. The application of a statistical independence analysis approach is used to review a person's individual language abilities. Method: This research uses a qualitative and quantitative corpus linguistics approach. The corpus linguistic application used in this research is the KORTARA application (Korpus Nusantara). The research data is a corpus of 9 texts of President Joko Widodo's speeches and a corpus of 9 texts of President Susilo Bambang Yudhoyono which are official speeches every 16 August before the DPR of the Republic of Indonesia. Results and Conclusion: The results of this research reveal that the text corpus of President Joko Widodo's speech is richer and more varied than the text corpus of President Susilo Bambang Yudhoyono's speech in lexical use. This research also revealed that there is a relationship between lexical variation and the type of text of the President of the Republic of Indonesia's speech with a confidence level of 95%. The difference in lexical variation and frequency between the text corpus of President Joko Widodo's speech and the text corpus of Susilo Bambang Yudhoyono's speech is statistically significant at p < 0.05. Implication of Research: The implication of this research is the realization of the KORTARA corpus linguistic approach (Korpus Nusantara) which can facilitate research for small and large scale data. This research also reveals that the application of a statistical approach provides maximum results in the analysis of large-scale linguistic phenomena. Originality/value: The current study makes a valuable empirical contribution by combining statistical analysis using corpus and qualititative analysis to give comprehensive conclusion. This study is the answer toward the question about the reliability and validity of linguistic studies.

Effect of an intensive tobacco cessation program on the smoker narrative: A content analysis and grounded theory.

The smoker's narrative during smoking quitting provides insight into aspects not fully explored in daily clinical practice. The aim of the study was to analyze the smoker narrative using two types of methodologies: content analysis and grounded theory, before and after smoking cessation intervention, provided to the smoker in a specialized Smoking Cessation Unit accredited by the Spanish Society of Pneumology and Thoracic Surgery. A prospective observational study of current smokers included in a tobacco cessation program between 2017 and 2020 was conducted at the Smoking Cessation Unit of Santiago de Compostela Health Area, Spain. Routine clinical variables and patient narrative data were collected. A descriptive analysis of the sample, the content of the textual corpus, and a grounded theory were performed in semi-structured interviews at baseline and at follow-up at 6 months. A total of 116 patients were included (mean age 55.6 ± 10.6 years; 56.9% male; mean nicotine dependence score 5.7 ± 1.6). Quantitative analysis of the narrative shows that the most frequent phrases and words are associated with smoking, nicotine craving, and predisposition for smoking cessation. After the intervention, phrases related to the manifestation of abstinence, response to pharmacological treatment, and self-perception of smoking cessation were predominant. In the qualitative analysis, the most frequent categories in the smoker's textual corpus were dependence, motivation, and emotionality, which decreased after the intervention (11.4%, 21.4%, and 9.9%, respectively) accompanied by increased satisfaction (19.2%) and the manifestation of abstinence (21.5%). Motivation, nicotine dependence, and sensitivity to emotions are all closely intertwined in the current smoker narrative and can be modified as a consequence of treatment.

Neoplatonic Nature of Love

Abstract This essay has two sections. The first explores the origin of the universe, the four elements, the knot of love, the third essence, and ‘Primordial Vessel’ by the Florentine Neoplatonic philosopher Marsilio Ficino. The second part interprets Botticelli’s The Birth of Venus of 1485–1486, now at the Galleria degli Uffizi in Florence, drawing upon Ficino’s Neoplatonic texts. Particularly some of Ficino’s Commentaries on Plato’s Dialogues (published in 1484), Symposium (completed in 1469), Philebus (completed in 1469), and Compendium on Timaeus (1480–1491). To this is added Ficino’s Theologia Platonica (published in 1482), his Letters (published in 1495), his Commentary on Proclus’s Alcibiades I (completed in 1488), and his translation of the Neoplatonic text Corpus Hermeticum, entitled Liber Mercurii Trismegisti de potestate et sapientia Dei (published in 1471). Botticelli’s beautiful painting captures the depiction of the goddess Venus’s birth as described in Hesiod’s Theogony, which Ficino recounted in his Philebus Commentary

Early detection of cervical and breast cancer: representations of primary care nurses

To analyze the practices of early detection of cervical and breast cancer performed by nurses in primary health care in Baixada Litorânea/RJ/Brazil. Qualitative, descriptive study; carried out with 43 nurses; data obtained through a self-completed form on socioprofessional profile; Focus groups, whose text corpus was prepared with IRaMuTeQ software for analysis through the Descending Hierarchical Classification guided by the Theory of Social Representation. Presented in two classes: Tensions between demands for early detection of cervical and breast cancer and nurses' professional practice; and Between public policies and daily practice: contents, meanings and attitudes. The nurses' representations showed practices permeated by scientific knowledge and common sense that consider their professional experience. Doubts and questions about the current guidelines reveal obstacles to effective care. We emphasize the need for permanent reconstruction of this knowledge with the teams and women.

Autoregressive Feature Extraction with Topic Modeling for Aspect-based Sentiment Analysis of Arabic as a Low-resource Language

This paper proposes an approach for aspect-based sentiment analysis of Arabic social data, especially the considerable text corpus generated through communications on X (formerly known as Twitter) for expressing opinions in Arabic-language tweets during the COVID-19 pandemic. The proposed approach examines the performance of several pre-trained predictive and autoregressive language models; namely, Bidirectional Encoder Representations from Transformers (BERT) and XLNet, along with topic modeling algorithms; namely, Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF), for aspect-based sentiment analysis of online Arabic text. In addition, Bidirectional Long Short Term Memory (Bi-LSTM) deep learning model is used to classify the extracted aspects from online reviews. Obtained experimental results indicate that the combined XLNet-NMF model outperforms other implemented state-of-the-art methods through improving the feature extraction of unstructured social media text with achieving values of 0.946 and 0.938, for average sentiment classification accuracy and F-measure, respectively.

Semi-automated ontology development scheme via text mining of scientific records

PurposeThe purpose of this study is to develop a domain independent, cost-effective, time-saving and semi-automated ontology generation framework that could extract taxonomic concepts from unstructured text corpus. In the human disease domain, ontologies are found to be extremely useful for managing the diversity of technical expressions in favour of information retrieval objectives. The boundaries of these domains are expanding so fast that it is essential to continuously develop new ontologies or upgrade available ones.Design/methodology/approachThis paper proposes a semi-automated approach that extracts entities/relations via text mining of scientific publications. Text mining-based ontology (TmbOnt)-named code is generated to assist a user in capturing, processing and establishing ontology elements. This code takes a pile of unstructured text files as input and projects them into high-valued entities or relations as output. As a semi-automated approach, a user supervises the process, filters meaningful predecessor/successor phrases and finalizes the demanded ontology-taxonomy. To verify the practical capabilities of the scheme, a case study was performed to drive glaucoma ontology-taxonomy. For this purpose, text files containing 10,000 records were collected from PubMed.FindingsThe proposed approach processed over 3.8 million tokenized terms of those records and yielded the resultant glaucoma ontology-taxonomy. Compared with two famous disease ontologies, TmbOnt-driven taxonomy demonstrated a 60%–100% coverage ratio against famous medical thesauruses and ontology taxonomies, such as Human Disease Ontology, Medical Subject Headings and National Cancer Institute Thesaurus, with an average of 70% additional terms recommended for ontology development.Originality/valueAccording to the literature, the proposed scheme demonstrated novel capability in expanding the ontology-taxonomy structure with a semi-automated text mining approach, aiming for future fully-automated approaches.

Natural language processing of clinical notes enables early inborn error of immunity risk ascertainment

There are now approximately 450 discrete inborn errors of immunity (IEI) described; however, diagnostic rates remain suboptimal. Use of structured health record data has proven useful for patient detection but may be augmented by natural language processing (NLP). Here we present a machine learning model that can distinguish patients from controls significantly in advance of ultimate diagnosis date. We sought to create an NLP machine learning algorithm that could identify IEI patients early during the disease course and shorten the diagnostic odyssey. Our approach involved extracting a large corpus of IEI patient clinical-note text from a major referral center's electronic health record (EHR) system and a matched control corpus for comparison. We built text classifiers with simple machine learning methods and trained them on progressively longer time epochs before date of diagnosis. The top performing NLP algorithm effectively distinguished cases from controls robustly 36 months before ultimate clinical diagnosis (area under precision recall curve> 0.95). Corpus analysis demonstrated that statistically enriched, IEI-relevant terms were evident 24+ months before diagnosis, validating that clinical notes can provide a signal for early prediction of IEI. Mining EHR notes with NLP holds promise for improving early IEI patient detection.

Юльян Ляскоўскі як беларускамоўны літаратар

A distinctive feature of the Belarusian literary process of the 19th century is the parallel presence of authorial and anonymous works. In the corpus of artistic texts of this period there are more than a hundred anonymous works. We know the names and surnames of many writers who allegedly wrote in Belarusian, but to our time no Belarusian works have survived. The subject of this study is the life and creative vicissitudes of Yulian Liaskouski (1826–1889), as well as hypotheses regarding the possible involvement of the poet in the creation of the poem Zahraj, zahraj, chłopcze mały... (Play, Play, Boy Small...) and propaganda texts for the people from the early 1860s. The article, based on the analysis of printed and handwritten materials, proves that Yulian Liaskouski, who most often performed under the pseudonyms Y. Korabicz, Marcin Mizera, and Mroczek, used not only Polish, but also Belarusian in his works. It is very likely that the poems Ziemielka maja… (My land…) and Pad dudu (Sing the Bagpipe Pad), published by him as folk ones in the article Słówko o pieśni (A Word about Song) (1882), belong to him. However, the most significant evidence in favour of the fact that Y. Liaskouski has written some works in Belarusian is the statement of Adam Honori Kirkor, in which a well-known Vilnius editor and publisher includes the poet and publicist Yulian Mroczek among the most famous Belarusian writers of the middle of the 19th century – Vincent Dunin-Marcinkewicz, Arciom Viaryha-Dareuski, Uladislau Syrakomlia, Vikentij K.

Linguistic annotation of cuneiform texts using treebanks and deep learning

Abstract We describe an efficient pipeline for morpho-syntactically annotating an ancient language corpus which takes advantage of bootstrapping techniques. This pipeline is designed for ancient language scholars looking to jump-start their own treebank projects, which can in turn serve further pedagogical research projects in the target language. We situate our work in the field of similar ancient language treebank projects, arguing that our approach shows that individual humanities scholars can leverage current machine-learning tools to produce their own richly annotated corpora. We illustrate this pipeline by producing a new Akkadian-language treebank based on two volumes from the online editions of the State Archives of Assyria project hosted on Oracc, as well as a spaCy language model named AkkParser trained on that treebank. Both of these are made publicly available for annotating other Akkadian corpora. In addition, we discuss linguistic issues particular to the Neo-Assyrian letter corpus and data-encoding complications of cuneiform texts in Oracc. The strategies, language models, and processing scripts we developed to handle both linguistic and data-encoding issues in this project will be of special interest to scholars seeking to develop their own cuneiform treebanks.

Writing and imagining ritual in the Babylonian New Year festival texts

AbstractIn cuneiform culture the New Year Festival was an important ritual of kingship. The most fundamental source for the reconstruction of this festival is a small corpus of cuneiform ritual texts that describe the ritual actions and prayers to be performed during the first days of the year. Those texts were written by Babylonian priests during the Persian‐Hellenistic period (484–140 BCE), when Babylonia had come under foreign rule. Why were these ritual texts outlining a royal ritual created at a time when Babylonia was governed by foreign rulers, who had little interest in Babylonian religious traditions? Why write down rituals at all? In this paper, I show how these New Year Festival texts are programmatic more than instructional, as they give shape to a new ritual paradigm in which Babylonian priests, not kings, are the central authority of the cult.

Improving sentiment analysis using text network features within different machine learning algorithms

Sentiment analysis poses a significant challenge due to the inherent subjectivity of natural language and the prevalence of unstandardized dialects in social networks. Regrettably, existing literature lacks a dedicated focus on network representation learning for sentiment classification. This paper addresses this gap by investigating ten machine learning algorithms, including support vector machine (SVM), random forest (RF), logistic regression (LR), and Naive Bayes (NB). Our approach integrates text network analysis and sentiment analysis to propose a comprehensive solution. We begin by applying text preprocessing techniques and converting a text corpus into a text network using word co-occurrence. Subsequently, we employ network analysis techniques to extract features based on network topology and node attributes. These network-derived features serve as inputs for sentiment prediction on Yelp reviews. Through the incorporation of diverse text network features and various machine learning algorithms, we achieve significant enhancements in sentiment classification performance. Our evaluation demonstrates an improved area under curve (AUC) of 83% on the Yelp reviews corpus, underscoring the efficacy of integrating network features to enhance sentiment classifiers. This research underscores the critical role of network representation and its potential impact on sentiment analysis, highlighting the prospect of harnessing network features for sentiment classification tasks.

Sensing complicated meanings from unstructured data: a novel hybrid approach

The majority of data on computers nowadays is in the form of unstructured data and unstructured text. The inherent ambiguity of natural language makes it incredibly difficult but also highly profitable to find hidden information or comprehend complex semantics in unstructured text. In this paper, we present the combination of natural language processing (NLP) and convolution neural network (CNN) hybrid architecture called automated analysis of unstructured text using machine learning (AAUT-ML) for the detection of complex semantics from unstructured data that enables different users to make understand formal semantic knowledge to be extracted from an unstructured text corpus. The AAUT-ML has been evaluated using three datasets data mining (DM), operating system (OS), and data base (DB), and compared with the existing models, i.e., YAKE, term frequency-inverse document frequency (TF-IDF) and text-R. The results show better outcomes in terms of precision, recall, and macro-averaged F1-score. This work presents a novel method for identifying complex semantics using unstructured data.

Understanding and Creating Word Embeddings

Word embeddings allow you to analyze the usage of different terms in a corpus of texts by capturing information about their contextual usage. Through a primarily theoretical lens, this lesson will teach you how to prepare a corpus and train a word embedding model. You will explore how word vectors work, how to interpret them, and how to answer humanities research questions using them.

Lecture de l’Écriture et écriture des Pensées

Throughout the manuscript of the Pensées and in other writings, Pascal crisscrosses his research on what the art of writing achieves and entails, on the one hand, and on the way to read and interpret the Holy Scriptures, on the other hand. Reading and writing practices are critically interwoven. This article offers a synthesis on Pascal’s reflexive account of such practices. After a summary of previous findings on the subject, it examines Pascal’s approach to (a) the rules that govern scriptural interpretation; (b) the techniques of persuasion; and (c) the way to ground an instituted body, an expression by which we refer to a textual corpus, a political society, or a community of believers.

HYBRID DISTANCE-STATISTICAL-BASED PHRASE ALIGNMENT FOR ANALYZING PARALLEL TEXTS IN STANDARD MALAY AND MALAY DIALECTS

Parallel texts corpora are essential resources in linguistics and natural language processing, especially in translation and multilingual information retrieval. The publicly available parallel text corpora are limited to certain genres, types and domains. Furthermore, the parallel dialect text is scarce, even though they are important in the analysis and study of a dialect. Collecting parallel dialect text is challenging because dialects typically appear in the form of speech and very limited dialectic texts exist. Moreover, there is no standard orthography in most dialects. The contributions of this paper are threefold. First, the paper describes a methodology in acquiring a parallel text corpus of Standard Malay and Malay dialects, particularly Kelantan Malay and Sarawak Malay. Second, we propose a hybrid of distance based and statistical-based alignment algorithm to align words and phrases the parallel text. The results show that the precision and recall values of the proposed alignment algorithm are more than 95% and better than the state-of the-art GIZA++. Third, the alignment obtained were compared to find out the lexical similarities and differences between Standard Malay and the two studied Malay dialects, contributing valuable insights into the linguistic variations within the Malay language family.

Information Potential of a Corpus of Scientific Texts

The article considers publicly available corpus of texts presented in the internet, characterises and considers the potential of corpus linguistics for analysing the development of scientific trends, discourse and changes in the field of terminology. A dataset based on a corpus of texts of scientific articles in a petroleum transport trade journal and the Google Books Corpus is presented. The dataset allows us to examine changes in term usage frequencies from 1940 to 2019.The results of analyses of term usage frequencies are presented, and a comparison is made between changes in the technology industry and the development of key vocabulary. The results show that studies made using data from corpuses of scientific and technical texts have good potential for understanding trends in technological development and the dynamics of change in industry and terminology.

From Textual Data to Theoretical Insights: Introducing and Applying the Word-Text-Topic Extraction Approach

Text analysis, particularly custom dictionaries and topic modeling, has helped advance management and organization theory. Custom dictionaries involve creating word lists to quantify patterns and infer constructs, while topic modeling extracts themes from textual documents to help understand a theoretical domain. Building on these two approaches, we propose another text analysis approach called word-text-topic extraction (WTT), which enhances the efficiency and relevance of text analysis for the sake of theoretical advancement. Specifically, we first identify relevant words for a researcher's theoretical area of interest using word-embedding algorithms. That step is followed by extracting text segments from the textual corpus using a collocation process. Finally, topic modeling is applied to capture themes relevant to the specific theoretical area of interest. To illustrate the WTT approach, we explored one research area needing further theory development—innovation. Using 841 CEOs’ letters to shareholders, we found that our WTT approach provides nuanced features of innovation that differ across industry contexts. We guide researchers on decisions and considerations related to the WTT approach in order to facilitate its use in future studies.

Unsupervised Estimation of Subjective Content Descriptions in an Information System

Let us consider the following scenario: A human is working with a corpus of text documents. In this corpus, the human needs to know documents with similar content and highlight relevant locations in the retrieved documents. An information system displaying the contents of the corpus and providing an information retrieval agent will help the human. To perform information retrieval on the corpus, the agent used internally in the information system may need additional data associated with the documents. In order to support this, the so-called Subjective Content Descriptions (SCDs) provide additional location-specific data for text documents. SCDs are subjective in the sense that the agent associates data with sentences to reflect the beliefs of users. In our scenario, the agent needs SCDs referencing sentences of similar content across various documents in the corpus and most text documents are not associated with SCDs. Therefore, this paper presents UESM, the Unsupervised Estimator for SCD Matrices, an approach to associate any corpus with SCDs. In an evaluation, we show that the performance of UESM in estimating topics of similar content in the corpus is on par with Latent Dirichlet Allocation, while UESM provides the SCDs referencing sentences of similar content.

A Five-Step Workflow to Manually Annotate Unstructured Data into Training Dataset for Natural Language Processing.

Natural Language Processing (NLP) is a powerful technique for extracting valuable information from unstructured electronic health records (EHRs). However, a prerequisite for NLP is the availability of high-quality annotated datasets. To date, there is a lack of effective methods to guide the research effort of manually annotating unstructured datasets, which can hinder NLP performance. Therefore, this study develops a five-step workflow for manually annotating unstructured datasets, including (1) annotator training and familiarising with the text corpus, (2) vocabulary identification, (3) annotation schema development, (4) annotation execution, and (5) result validation. This framework was then applied to annotate agitation symptoms from the unstructured EHRs of 40 Australian residential aged care facilities. The annotated corpus achieved an accuracy rate of 96%. This suggests that our proposed annotation workflow can be used in manual data processing to develop annotated training corpus for developing NLP algorithms.