Word Ranking Research Articles

Visible and Invisible: Tokening the Memory of the Holocaust in Fascist Concentration Camps

ABSTRACT This article explores commemorative plaques at former fascist concentration camps in Italy, focusing on how they reflect the memory of the Holocaust. Using lexical analysis to rank word frequency, the study identifies four narrative modes: Culpabilisation, Heroisation, Victimisation, and Redemption. Before the establishment of Holocaust Remembrance Day in 2000, plaques predominantly highlighted the Italian Resistance against Nazis, with minimal acknowledgement of the Holocaust. Although there has been a shift towards recognising the Holocaust since Law 211, the Resistance narrative remains central. Fascism is rarely mentioned, indicating a tendency to downplay Italy’s fascist past and its Nazi alliance. While these plaques meet legal requirements for remembrance, they often lack historical accuracy, focus on local events, and prioritise heroism over the recognition of atrocities and their perpetrators.

Engineering Features From Advanced Medical Technology Initiative Submissions to Enable Predictive Modeling for Proposal Success.

The U.S. Army Telemedicine and Advanced Technology Research Center Advanced Medical Technology Initiative (AMTI) demonstrate key emerging technologies related to military medicine. AMTI invites researchers to submit proposals for short-term funding opportunities that support this goal. AMTI proposal selection is guided by a time-intensive peer review process, where proposals are rated on innovation, military relevance, metrics for success, and return on investment. Utilizing machine learning (ML) could assist in proposal evaluations by learning relationships between proposal performance and proposal features. This research explores the viability of artificial intelligence/ML for predicting proposal ratings given content-based proposal features. Although not meant to replace experts, a model-based approach to evaluating proposal quality could work alongside experts to provide a fast, minimally biased estimate of proposal performance. This article presents initial stages of a project aiming to use ML to prioritize research proposals. The initial steps included a literature review to identify potential features. Then, these features were extracted from a dataset consisting of past proposals submissions. The dataset includes 824 proposals submitted to the AMTI program from 2010 to 2022. The analysis will inform a discussion of anticipated next steps toward developing a ML model. The following features were created for future modeling: requested funds; word count by section; readability by section; citations and partners identified; and term frequency-inverse document frequency word vectors. This initial process identified the top ranked words (data, health, injury, device, treatment, technology, etc.) among the abstract, problem to be solved, military relevance, and metrics/outcomes text proposal fields. The analysis also evaluated the text fields for readability using the Flesch readability scale. Most proposals text fields were categorized as "college graduate," indicating a challenging readability level. Finally, citations and partners were reviewed as an indicator of proposal successfulness. This research was the first stage of a larger project to explore the use of ML to predict proposal ratings for the purpose of providing automated support to proposal reviewers and to reveal the preferences and values of AMTI proposal reviewers and other decision-makers. The result of this work will provide practical insights regarding the review process for the AMTI program. This will facilitate reduction in bias for AMTI innovators and a streamlined and subjective process for AMTI administrators, which benefits the military health system overall.

Are the Positions in the Word Ranking of Competitive Alpine Skiers Explainable by Prominent Polymorphisms in Regulatory Genes of Mechanical and Metabolic Muscle Functioning?

ABSTRACT Background: The success of competitive alpine skiers with respective to their world ranking (WR) positions might be associated with prominent gene polymorphisms. Methods: Twenty-six competitive alpine skiers were followed from 2015 to 2019 for their WR positions (FIS-ranking). Using PCR, the genotypes of ACE-I/D, TNC, ACTN3, and PTK2 were identified. The correlations between the discipline-specific WR position (slalom—SL, giant slalom—GS, super G—SG, downhill—DH, and alpine combined—AC) and gene polymorphisms were analyzed concerning an influence with multivariate regression models. Results: The WR position and the ACE gene as well as the copy number of the ACE I-allele exhibited reciprocal relationships for speed specialists (SG and DH) but not for technical specialists (SL and GS). Similarly, the gene polymorphisms ACTN3 and (partly) PTK2 were associated with the WR position in disciplines characterized by a high number of turns (technical specialists—SL and GS) and speed (speed-specialists—SG and DH), respectively. Conclusions: Our findings emphasize the contributions of aerobic and cardiovascular metabolism in fueling muscle work and recovering from muscle fatigue for competitive success in slalom-driven skiing disciplines and highlight the contributions of sequence variants in the genes ACE, TNC, and ACTN3.

Register-based distribution of expressions of modality in COCA

This research scrutinizes expressions of modality at both word and clause ranks. Expressions at the former rank encompass modal verbal operators, modal adjuncts, and modal nominalizations, and expressions at the latter rank encompass ‘explicit subjective’ orientation such as I think and ‘explicit objective’ orientation, such as it is possible. These latter expressions are designated as interpersonal metaphors of modality within the framework proposed by Halliday. Previous studies delimited the distribution of expressions of modality either at word rank or at clause rank; in addition, they undertheorized the relationship between this distribution of modality and formality of registers because the register is testified to be an influential factor in the distribution of expressions of modality. This study, drawing on such statistical methods as variation analysis and correlation analysis, explored the distribution of expressions of modality at both word and clause ranks across registers with different degrees of formality by employing the Corpus of Contemporary American English (COCA). The findings are summarized as follows. First, registers in COCA employ expressions of modality with low and/or median values to judge the proposition at issue for the purpose of entertaining other varying voices. Second, in more formal registers, language users seem to prefer modal nominalizations to modal verbal operators and modal adjuncts at word rank, and ‘explicit objective’ orientation to ‘explicit subjective’ orientation at clause rank. In doing so, they could efficiently conceal the commentators’ subjective meaning, and thus, the objectivity of propositions is highlighted. This study will shed light on the full understanding of the distribution of expressions of modality across registers and the application of such theory as interpersonal metaphor of modality in SFL.

Synchronization of Primitive Automata

We exhibit new conditions under which a primitive automaton is synchronizing. In particular, we show that the primitivity of an automaton forces its synchronizability whenever the automaton has either a letter of defect 1 or a word of rank 2.

ROBUST DETECTION OF LINK COMMUNITIES WITH SUMMARY DESCRIPTION IN SOCIAL NETWORKS

Community detection has been extensively studied for various applications. Recent research has started to explore node contents to identify semantically meaningful communities. However, links in real networks typically have semantic descriptions and communities of links can better characterize community behaviors than communities of nodes. The second issue in community finding is that the most existing methods assume network topologies and descriptive contents carry the same or compatible information of node group membership, restricting them to one topic per community, which is generally violated in real networks. The third issue is that the existing methods use top ranked words or phrases to label topics when interpreting communities, which is often inadequate for comprehension. To address these issues altogether, we propose a new Bayesian probabilistic approach for modeling real networks and developing an efficient variational algorithm for model inference. Our new method explores the intrinsic correlation between communities and topics to discover link communities and extract semantically meaningful community summaries at the same time. If desired, it is able to derive more than one topical summary per community to provide rich explanations. We present experimental results to show the effectiveness of our new approach and evaluate the method by a case study.

WordPPR: A Researcher-Driven Computational Keyword Selection Method for Text Data Retrieval from Digital Media

ABSTRACT Despite the increasing use of digital media data in communication research, a central challenge persists – retrieving data with maximal accuracy and coverage. Our investigation of keyword-based data collection practices in extant communication research reveals a one-step process, whereas our cross-disciplinary literature review suggests an iterative query expansion process guided by human knowledge and computer intelligence. Hence, we introduce the WordPPR method for keyword selection and text data retrieval, which entails four steps: 1) collecting an initial dataset using core/seed keyword(s); 2) constructing a word graph based on the dataset; 3) applying the Personalized PageRank (PPR) algorithm to rank words in proximity to the seed keyword(s) and selecting new keywords that optimize retrieval precision and recall; 4) repeating steps 1–3 to determine if additional data collection is needed. Without requiring corpus-wide sampling/analysis or extensive manual annotation, this method is well suited for data collection from large-scale digital media corpora. Our simulation studies demonstrate its robustness against parameter choice and its improvement upon other methods in suggesting additional keywords. Its application in Twitter data retrieval is also provided. By advancing a more systematic approach to text data retrieval, this study contributes to improving digital media data retrieval practices in communication research and beyond.

WEB BASED MEDICAL CONSULTING INFORMATION FLOW FOR HOSPITAL OUT-PATIENTS USING MACHINE LEARNING TECHNIQUES

Medical consulting today is characterized by numerous demanding tasks that are prone to illness and need to be efficiently managed by medical practitioners using emerging techniques and solutions. Although there are concerted efforts by both the doctors and other medical stakeholders aimed at improving the condition, human errors abound. Sequel to this, the level of management bottlenecks recorded in the hospitals on a daily basis abound, such as the unordered information flow in the outpatients department (OPD); thus, effective health service delivery is being stifled. In this regard, the application of natural language-based medical consulting information flow cannot be undermined. This work seeks to develop a natural language-based medical consulting information flow for hospital outpatients using machine learning techniques. The architecture of the system was modeled using universal modeling language (UML) tools. The medical information flow is processed using machine learning techniques (MLT), which are named Word Rank. The word rank positions and sorts all sentences accordingly in an input corpus of a patient case report and is presented in a summarized version of the word. So, the word-rank algorithm generates a summary of the patient’s record based on the input corpus. The implemented system can be deployed in the Nigerian Electronic Health Records (NEHR) used in hospitals to ensure efficient information flow for patients in the outpatient department of every hospital. The system would therefore reduce the effort expended by the medical practitioners in comprehending a case report and offering various medical services since it would be very easy to view the salient parts promptly, resulting in a reasonably smooth information flow, an easier consultation process, and a reduction in patient waiting time. Cosine similarity Identify the applicable funding agency here. If none, delete this. is used as the metric to evaluate the accuracy of the output from the algorithm when weighted against the traditional information flow in the outpatient department.

Methodological observations concerning word rankings and z-score refinements

Abstract This article evaluates word rankings suggested by Ary L. Goldberger, Albert C. Yang, and C. Peng as a means of establishing the authorship of texts in the light of Delta, developed by John Burrows at about the same time. The tests carried out with high ranking function words and results established with the more modern approaches of Rolling Delta, Rolling Classify, and the General Imposters method give clear evidence that word rankings only return crude and unreliable results that cannot keep up with non-traditional modern methods. Even though the stylistic difference between Marlowe and Shakespeare plays could be stated, word rankings failed to recognize Shakespearean stylistics in The Jew of Malta, Edward II, and Doctor Faustus. It was only through the use of z-scores that a wider vocabulary provided a larger degree of differentiation.

ANALYSIS OF THE POSSIBLE IMPACT OF ROMANIAN AND HUNGARIAN UNIVERSITIES TO ECONOMIC DEVELOPMENT

Economic development is associated with innovation, which is why the innovation management practices and systems of the universities became essential. This research aimed to benchmark five Romanian and Hungarian universities close to the Romanian-Hungarian border to identify the strengths and weaknesses of the different approaches.The benchmarking included economic data and indicators connected to the innovation of the five biggest universities along the border regions. The analysis was performed using data derived from The Higher Education Word Ranking and Eurostat. For the qualitative part, the web pages of the universities were analyzed.The research found that although the universities on the Hungarian side of the border seem more developed, regional economic development is stronger and more natural on the Romanian side of the border. According to the literature, it is clear that geographical factors play an important role in the development of innovation systems. The closing part of this paper makes an attempt to list the possible causes of the results.

Crowdsourcing ratings for single lexical items

In this study, we investigate theoretical and practical issues connected to differentiating between core and peripheral vocabulary at different levels of linguistic proficiency using statistical approaches combined with crowdsourcing. We also investigate whether crowdsourcing second language learners’ rankings can be used for assigning levels to unseen vocabulary. The study is performed on Swedish single-word items. The four hypotheses we examine are: (1) there is core vocabulary for each proficiency level, but this is only true until CEFR level B2 (upper-intermediate); (2) core vocabulary shows more systematicity in its behavior and usage, whereas peripheral items have more idiosyncratic behavior; (3) given that we have truly core items (aka anchor items) for each level, we can place any new unseen item in relation to the identified core items by using a series of comparative judgment tasks, this way assigning a “target” level for a previously unseen item; and (4) non-experts will perform on par with experts in a comparative judgment setting. The hypotheses have been largely confirmed: In relation to (1) and (2), our results show that there seems to be some systematicity in core vocabulary for early to mid-levels (A1-B1) while we find less systematicity for higher levels (B2-C1). In relation to (3), we suggest crowdsourcing word rankings using comparative judgment with known anchor words as a method to assign a “target” level to unseen words. With regard to (4), we confirm the previous findings that non-experts, in our case language learners, can be effectively used for the linguistic annotation tasks in a comparative judgment setting.

Multi-Modal Topic Model Based on Word Rank and Relevance Semantic for Social Events Classification

Social event classification of multimedia is a hot issue in the field of multimedia research. The existing social event classification methods based on supervised topic model fail to make full use of the internal semantic information (text, vision, etc.) in the corpus, and the classification performance of the model can be further improved. To solve this problem, a multi-modal supervised topic model (multi-modal supervised topic model based on word rank and relevance semantic weighting, DPRF-MMSTM) is proposed, which integrates word rank semantics and word document relevance semantics. According to the results of dependency parsing, the contribution of text modal words to document representation can be divided, and the hierarchical semantics of text words can be mined. In addition, the relevance frequency of multi-modal words is considered to extract the relevance semantics of word documents. The two semantics are integrated into the sampling process of multi-modal words to achieve the classification of social events based on supervised topic model. Compared with the existing models, the comparative experiments on multi-modal and text modal datasets show that the DPRF-MMSTM model proposed in this paper improves the classification accuracy of social events by 1.200% and 1.630% respectively, and the topic consistency is increased by 38.0% and 8.5% respectively.

Which Words Matter Most? Operationalizing Lexical Prevalence For Rank-Ordered Word Lists

AbstractThe words in a language or language variety are often rank ordered in lists that are meant to reflect the relative importance of those words to language users and learners of a language. This rank ordering is done on the basis of the relative prevalence of words in a corpus. Lexical prevalence is often operationalized as measures of frequency, dispersion, or adjusted frequency. Yet, to date, there is no consensus on best practices for identifying and ranking prevalent words in a corpus, or for evaluating the degree to which a word’s importance is reflected through its prevalence. We begin this paper by introducing and describing a wide range of corpus-based measures for quantifying lexical prevalence. We then carry out two case studies on the Duolingo University Textbook Corpus to evaluate the methods for their ability to identify and appropriately rank words in terms of their importance. We conclude with recommendations for word list creators and researchers and practitioners interested in word importance.

Integrating corpus-based and NLP approach to extract terminology and domain-oriented information: an example of US military corpus

Within the modern information, communication and technology (ICT), seeking high efficient and accurate corpus-based approaches to process natural language data (NLD) is critical. Traditional corpus-based approaches for processing corpus (i.e. the collected NLD) mainly focused on quantifying and ranking words for assisting human in extracting keywords. However, traditional corpus-based approaches cannot identify the meanings behind the words to properly extract terminologies nor their information. To address this issue, the main objective of this paper is to propose an integrated linguistic analysis approach that combines two corpus-based approaches and a rule-based natural language processing (NLP) approach to extract and identify terminologies and create the text database for extracting deeper domain-oriented information by using the terminologies as channels to retrieve core information from the target corpus. Military domain is an uncommon research field and often classified as confidential data, which caused little researches to focus on. Nevertheless, military information is vital to national security and should not be ignored. Hence, to verify the proposed approach in extracting terminologies and information of the terminologies, the researchers adopt the US Army field manual (FM) 8-10-6 as the target corpus and empirical case. Compared with AntConc 3.5.8 and Tongpoon-Patanasorn’s hybrid approach, the results indicate that from the perspectives of terminology identification, texts database creation, domain knowledge extraction, only the proposed approach can handle all these issues.

Towards a Systemic Functional Description of the System of Temporal Values in Arabic

There is a general consensus about how difficult it is to come up with a proper description of the system of temporal values in Arabic. Overall, while Arab linguists mostly interpret the system from a temporal angle, western studies have treated it as fully aspectual. Both camps have been inspired by the work of traditional Arab grammarians who clearly stated that the system is not paradigmatically exclusive. This article will describe the system of temporal values in Arabic by following in the footsteps of Matthiessen 1996 in his systemic functional description of tense in English. By locating the system at word rank, instead of group rank, most of the past studies have overlooked a whole range of perspectives such as serial verb constructions and the tactic interdependency potential of the verbal unit in Arabic. Based on Comrie's work on aspect and his assertion that some ‘aspect’ languages can construe different temporal values, I will suggest that it may be time to treat temporal descriptions with more flexibility and instead of dividing languages into two dichotomous groups, place them on a temporal spectrum where at one end there is tense and at the other aspect.

Toward English-Chinese Translation Based on Neural Networks

In traditional interactive English translation systems, English semantic context is not obvious in the process of the English translation, and the selection of optimal feature semantics does not reach the optimal translation solution, which leads to low translation accuracy. Toward this solation, a neural network (NN) based translation approach is proposed to predict word order differences in language translation and improve translation accuracy in long sentences. In this study, a multilayer NN model has been established to victories the unlabeled text words, realize the combination of word representation, vector features, and extract effective information of various sentences and semantics. In linear ranking frameworks, the NN is used to rank and score words, obtain semantic information of sample data, and predict the difference of word order. Experimental results show that the NN preorder model can significantly improve translation accuracy and system performance. The application of NN-based translation model in the practical translation process can reduce the effort of translation work, improve the efficiency of translation, and has a good practical significance.

Hierarchical Triple Model of Hybrid Neural Machine Translation

For more than a decade, PMT and SMT models have dominated the field of machine translation, and neural machine translation has emerged as a new paradigm for machine translation by the 2015. Neural machine translation provides a simple modeling mechanism that is easy to use in practice and science. Thus, it does not require concepts such as word ranking, a key component of the statistical machine translation. While this simplicity may be seen as an advantage, on the other hand, the lack of careful spelling is to lose control of the translation. Even tough, the neural machine translation is more flexible in terms of translations that don’t exactly match the training data. This provides more opportunities for such models, but exempts translation from pre-determined restrictions. Failure to connect specific words can make it difficult to connect the target words you create to the original word. The widespread use of neural machine translation system has the advantage of allowing users to translate certain terms and translate uneducated data to a certain extent. In some cases, however, the structure and the grammar boundary of a sentence is often distorted. The paper is intended to address issues such as the control of neural machine translation, more accurate translation of unidentified data, the accuracy of sentence structure and grammar boundaries. To solve this problem, modern translation theory led to the hybrid model of machine translation. Our model is expansion of this hybrid model with a sentence and a grammar boundary. We named this model as hierarchical triple model (HTM).

English-Mongolian, Mongolian-English Neural Machine Translation

The latest neural machine translation not only performs better than systems that consider simple words and sentence structures, but also finds a delicate connection between source and target words. Neural machine translation provides a simple modeling mechanism that is easy to use in practice and science. Thus, it does not require concepts such as word ranking, a key component of the system that takes into account the structure of words and sentences. While this simplicity may be seen as an advantage, on the other hand, the lack of careful spelling is to lose control of the translation. Systems that take into account the structure of words and sentences create translations consisting of word sequences in the curriculum data. On the other hand, the neural machine translation is more flexible in terms of translations that don't exactly match the training data. This provides more opportunities for such models, but exempts translation from pre-determined restrictions. Failure to connect specific words can make it difficult to connect the target words you create to the original word. The widespread use of neural networks in the translation system has the advantage of allowing users to translate certain terms and translate uneducated data to a certain extent. In some cases, however, the structure of a sentence is often distorted. The paper is intended to address issues such as the control of neural machine translation, more accurate translation of unidentified data, the accuracy of sentence structure and grammar boundaries.

A fusion of variants of sentence scoring methods and collaborative word rankings for document summarization

AbstractDocument summarization is an important task in natural language processing that helps deal with the problem of information overload occurring due to the existence of redundant content. Summary generation with highly relevant contents and maximum coverage is particularly challenging which can only be achieved when redundancy is minimized. This article introduces a novel approach for automatic text summarization based on sentence scoring and collaborative ranking to produce summaries with minimal redundancy and improved overall performance of summarization. The proposed model is a fusion of weighted and unweighted features‐based sentence scoring methods. To learn optimal weights of text features, it has been modelled as an optimization problem. Moreover, the proposed model exploits the strength of collaborative ranking to generate the summary of a given document. Three similarity factors (proximity, significance and singularity)‐based models have been employed to find the similarity between weighted and unweighted sentence scores. The results of the comparison experiment demonstrate that the proposed (PS + Jac) method generates a closer summary to the reference summary with minimal redundant contents. On average, the proposed (PS + Jac) method generates the summaries with 61% accurate contents with greater improved rates up to 40%. The statistical testing also confirms that the performance improvement is significant at a 5% level of significance.

Сопоставление лексических ядер чешского и словацкого языков методом параметрического анализа словаря

The article suggests a solution to the problem of the sister languages’ vocabulary comparison. The subject of research is Czech and Slovak vocabulary given in small bilingual dictionaries. The same metalanguage (Russian) of the definitions in such dictionaries makes the semantic analysis of vocabulary easy. The basic method is parametric analysis. It implies the ranking of words of a language according to four system parameters: functional, syntagmatic, paradigmatic, and epidigmatic ones. For each parameter, it is necessary to distinguish a local nucleus, which includes about 1000 words. Local parametric vocabulary nuclei make it possible to identify (1) the shortest, i.e., non-derivative and common (frequently used) vocabulary in these Slavonic languages; (2) vocabulary most commonly used in various fixed expressions and collocations; (3) vocabulary that forms the largest synonymic chains; (4) the most polysemantic vocabulary. The addition of local nuclei allows forming a big parametric nucleus. It includes words, which possess parametric weight values based on at least three parameters (out of four). For Czech, the big parametric nucleus comprises 279 words. For Slovak, it comprises 282 words. These lexical sets present the subject of the further analysis aimed at classifying the meanings of the words. At first, the authors establish that Czech and Slovak nuclei contain 61 words meaning the same in both languages. Besides, among them, there are 30 words that possess not only the same meaning but the same form as well. The big parametric nuclei do not contain false friends, i.e. words that look similar but differ in meaning. Then the authors do qualitative analysis of nuclear lexemes’ semantics. They find out that, in Czech and Slovak, basic semantic groups are formally similar, but they differ in intragroup distribution. Language data illustrate the fact that such semantic groups as “Activity”, “Abstract Notions”, “Human Being” are the dominant semantic groups in both languages. In the Slovak big parametric nucleus, a quantitative gap between them is less perceptible than in the Czech one. Besides, the semantic group “Human Being” occupies a higher position in the Slovak parametric nucleus. The dominance of the semantic group “Activity” and of the big parametric nucleus, as well as a negligible percent of words in the subgroup “Imitating Activity”, justifies the fact that, for Czech native speakers, not a person but a person-agent is of a particular importance. At the same time, the high percents of the semantic group “Nature” and of subgroup “A Person of Nature” prove that great attention to nature objects is a feature of Slovak native speakers’ worldview.