Lexical Co-occurrence Research Articles

Analysis of Correlation Features in Material Application for Architectural Design Based on Big Data Text Mining

This study conducted a big data mining and lexical co-occurrence network analysis on design texts sourced from architectural media over the past decade, systematically evaluating the application trends of materials in architectural design. This approach distinguishes itself from traditional case studies that rely on small samples of material applications in architectural design, thereby significantly enhancing the generalizability of the conclusions. The research reveals a 27.2% increase in the material–project frequency of architectural materials over the past ten years, indicating that the materiality of architecture is gradually making a comeback to counteract the trend toward architectural visualization. Within this context, material properties, material interactions, and material presentation in architectural components emerge as universal concerns in architectural design. Notably, the sustainability and energy efficiency of wood and concrete have emerged as pivotal topics in architectural practice, in addition to their structural functions. Additionally, over the past decade, the material–project frequencies of brick and stone have grown rapidly, with respective increases of 24.3% and 11.8%. Brick and stone primarily respond to the locality and style of architectural design by emphasizing their inherent texture and color, thereby enhancing the spatial perception and experience within the designs. This study lays the groundwork for quantitative research on the influence of building materials on architectural design, and it illuminates current trends and preferences in material application within architectural design, thereby empowering architects to make more informed decisions in their material selection.

The development and preliminary validation of the Serbian value lexicon – An archival approach to value measurement

Values refer to stable beliefs and principles held by individuals, which guide their attitudes, behaviours, and judgments, and play a crucial role in shaping their identities and interactions with others. Studying values in social psychology is important as it provides insights into the motivational forces that drive individuals’ behaviour and decision-making, shaping the dynamics of interpersonal relationships and societal interactions. The aim of this paper is to test the possibility of measuring basic values in the archive and text materials. Based on the Schwartz’s theory of values and earlier work on the value lexicon in English, the Serbian lexicon of values was developed and preliminarily validated on a large-scale Internet-based survey. The lexical co-occurrence of words in the natural language use on the Internet was analysed in order to assess the convergent, discriminant and predictive validity of the lexicon. Lexical co-occurrence analysis showed that the words representing the same values co-occurred significantly more in comparison to the words denoting different values. The pattern of correlations between the values measured in the archive material on the Internet using the value lexicon showed high convergence with the pattern of correlations between the values assessed by the self-reported measures used in the European Social Survey in 2018. The relative prominence of the specific values on the official websites of the exemplar societal institutions and organizations identified by the value lexicon was in line with the expectations and preliminarily confirmed the criterion validity of the lexicon of values. Possible applications of the lexicon of values, as well as some methodological issues pertaining to its future use, are discussed in the final part.

Graph-Based Recommendation System Enhanced by Community Detection

Many researchers have used tag information to improve the performance of recommendation techniques in recommender systems. Examining the tags of users will help to get their interests and leads to more accuracy in the recommendations. Since user-defined tags are chosen freely and without any restrictions, problems arise in determining their exact meaning and the similarity of tags. However, using thesaurus and ontologies to find the meaning of tags is not very efficient due to their free definition by users and the use of different languages in many data sets. Therefore, this article uses mathematical and statistical methods to determine lexical similarity and co-occurrence tags solution to assign semantic similarity. On the other hand, due to the change of users’ interests over time this article has considered the time of tag assignments in co-occurrence tags for determining the similarity of tags. Then the graph is created based on similarity of tags. For modeling the interests of the users, the communities of tags are determined by using community detection methods. So, recommendations based on the communities of tags and similarity between resources are done. The performance of the proposed method has been evaluated using two criteria of precision and recall through evaluations on two public datasets. The evaluation results show that the precision and recall of the proposed method have significantly improved, compared to the other methods. According to the experimental results, the criteria of recall and precision have been improved, on average by 5% and 7%, respectively.

La ‘Galassia MQDQ:’ un concetto di filologia tradizionale, digitale, sostenibile

The investigation of intertextuality within a corpus of Latin poetry is the main objective of the research functions of the so-called ‘Musisque Deoque Galaxy’: formular recurrences, and lexical and metric-verbal co-occurrences draw a dense network of relationships between texts, where poetic memory presents itself in various modalities. This contribution aims to analyse the latest developments in the “Musisque Deoque Galaxy” both from the point of view of the tools created to enable new textual acquisitions and new analyses, and from the technological point of view, with reference to the work carried out to ensure a long-term sustainability.

Quantitative risk assessment of railway intrusions with text mining and fuzzy Rule-Based Bow-Tie model

With the increasing traveling speed of railway transportation, rail right-of-way intrusions can cause high-consequence accidents and pose severe challenges to railway safety. Although intrusion detection technologies have been widely studied and applied, they can only support in-event inspection and post-event control. In the current complex environment, there is a critical need to analyze the causal chain of railway intrusions and mitigate safety risks before or during the risk evolution process. This paper developed a novel methodological framework on the cause-consequence model based on the text mining techniques and fuzzy bow-tie modeling to systematically investigate the railway intrusion risks. In order to mine both critical factors and their interrelationships, a lexical co-occurrence analysis was carried out on a customized corpus of intrusion accident recordings. Then structured bow-tie diagrams were developed based on the networks generated by unstructured data. To overcome the data uncertainty issue, this paper utilized cause-consequence-based probabilistic analysis and fuzzy theory to quantify the risks involving the occurrence probability of top events and outcomes in terms of expert judgements. The application of the proposed bow-tie model was demonstrated based on the case of the Hualien Derailment accident. The findings based on the bow-tie model and historical accidents in this research have systematically summarized basic events and causal chains. Ultimately, they can be utilized by researchers and practitioners both to identify the critical risk factors and enhance railway safety via proactive and reactive measures.

Lexical Collocations and their Acquisition in French as a Foreign Language (FLE)

Summary The importance of stereotypical uses of language is recognized by most didactic studies of the vocabulary (Granger and Paquot, 2008). Collocations are an area of vocabulary which is difficult to master by non-native learners. This type of lexical relation often presents itself as a semi-frozen binary lexical co-occurrence. The meaning of collocations is often transparent in reception, while in production, it requires a special effort on behalf of the learner. In this paper, we have tried to verify this assertion by comparing the corpus of French language and that of learners, and to define the regularities of lexical combinations in the French interlanguage of Lithuanian learners with level B1. Thanks to the corpus at our disposal, we observed that nomino-adjectival collocations at this level are more frequent than verbal collocations. However, in the percentage of total, the number of collocations only represents a contingent part of the corpus. The corpus also reveals that the present combination of words is not always diversified either by its syntax or by its lexical content, which is sometimes atypical of standard French. Learners choose from a fairly limited number of rather free lexical units. The process of interference and hybridization can be seen as an essential contamination of collocations, both lexically and syntactically. The data received makes it possible to note that collocational competence is insufficient. The results of this study also show that the analysis of collocational constructions can reveal the relationships between competence and performance of speakers.

Semantic projection recovers rich human knowledge of multiple object features from word embeddings.

How is knowledge about word meaning represented in the mental lexicon? Current computational models infer word meanings from lexical co-occurrence patterns. They learn to represent words as vectors in a multidimensional space, wherein words that are used in more similar linguistic contexts-that is, are more semantically related-are located closer together. However, whereas inter-word proximity captures only overall relatedness, human judgements are highly context dependent. For example, dolphins and alligators are similar in size but differ in dangerousness. Here, we use a domain-general method to extract context-dependent relationships from word embeddings: 'semantic projection' of word-vectors onto lines that represent features such as size (the line connecting the words 'small' and 'big') or danger ('safe' to 'dangerous'), analogous to 'mental scales'. This method recovers human judgements across various object categories and properties. Thus, the geometry of word embeddings explicitly represents a wealth of context-dependent world knowledge.

Functions of Russian verb-derived discourse markers slušaj and smotri from the perspective of information management and interpersonal regulation

While a significant amount of research related to discourse markers in the Russian language has been devoted to particles, very few scholars have focused on verb-derived discourse markers such as slušaj (‘listen’), smotri (‘look’), znaeš’ (‘y'know’), ponimaeš’ (‘d'you understand’), and vidiš’ (‘d'you see’). In this paper, I focus on slušaj and smotri, exploring their functions of information management and interpersonal regulation in talk-in-interaction. Based on data collected from YouTube interview videos, I examine the features of slušaj and smotri in terms of speaker roles, position in utterances, utterance structure, and lexical co-occurrence. A qualitative analysis supported by quantitative results shows how slušaj and smotri perform their information management and interpersonal functions in requesting/providing information, reformulating utterances, establishing contact, and indicating empathy. Considering the similarities and differences observed from the analysis, the results of this study suggest that slušaj is likely to be an interpersonal marker typical of response utterances, while smotri is an information-oriented marker leading a hearer to understand an utterance.

Lexical cooccurrence of mental verbs in the Chinese language

Lexical cooccurrence of mental verbs in the Chinese language

Looking for concepts in Early Modern English

Abstract The idea that conceptual meaning in discourse could be identified in constellations of lexical co-occurrences in a particular “universe” of discourse was key in guiding the computational historical semantic–pragmatic work conducted in the Linguistic dna project. The project mapped prominent lexical co-occurrences across the two hundred years of publications in Early English Books Online (eebo-tcp; Text Creation Partnership edition), yielding concept models – constellations of non-adjacent lemmas that consistently co-occur across spans of up to 100 tokens. The goal was to map meaning onto concept models as “discursive concepts”, using encyclopaedic knowledge, pragmatic analysis and context. The first question concerns the effectiveness of making early hypotheses about the discursive meaning of concept models based on the inferred connections between the lemmas in a quad constellation. The second question is whether the meaning of frequent, apparently stable concept models changes upon their closer scrutiny in the discourses they lead us into. A reader familiar with the particular universe of discourse in which these quads occur, and with the social, historical, literary and philosophical traditions, and the context that they occupy, might be effectively primed by their encyclopaedic knowledge to hypothesise this discursive meaning. This paper demonstrates the efficacy of hypothesis building using encyclopaedic knowledge and pragmatic analysis to interpret optimally relevant concept models.

Investigating the Scientometrics of the Studies Conducted in the Field of Islam

the present study investigated the status of the studies conducted in Islam’s field, employing scientometric methods. Extracted from Web of Science database, 6837 articles were surveyed for the growth rate, core journals, core authors, average citation rate, effective authors, countries’ contributions, lexical co-occurrence map, most frequent key terms, thematic domains and published formats. Ravar Matrix, USI Net and R environment were utilized to analyze the data. The findings indicated that the studies on Islam's field have had an ascending trend; studies are published more in article format, the average citation rate to the outputs in Islam's field shows a rise from 2008 to 2008 and a fall from 2008 to 2020; Christianity was the most frequent discussed subject in the studies on Islam’s field, key terms of “Islam”, Islamophobia” and “religion” were the most frequent key terms; Belhaj, Padela, and Prat are the most productive authors in the Islam's field; the journal ISLAM-ZEITSCHRIFT FUR GESCHICHTE UND KULTUR DES ranked first in publishing articles on Islam; and finally in terms of country, America received the first rank in publishing the outputs on Islam's field. Some suggestions are also presented by the researchers as to the implications of the research findings.

Topic models do not model topics: epistemological remarks and steps towards best practices

The social sciences and digital humanities have recently adopted the machine learning technique of topic modeling to address research questions in their fields. This is problematic in a number of ways, some of which have not received much attention in the debate yet. This paper adds epistemological concerns centering around the interface between topic modeling and linguistic concepts and the argumentative embedding of evidence obtained through topic modeling. It concludes that topic modeling in its present state of methodological integration does not meet the requirements of an independent research method. It operates from relevantly unrealistic assumptions, is non-deterministic, cannot effectively be validated against a reasonable number of competing models, does not lock into a well-defined linguistic interface, and does not scholarly model topics in the sense of themes or content. These features are intrinsic and make the interpretation of its results prone to apophenia (the human tendency to perceive random sets of elements as meaningful patterns) and confirmation bias (the human tendency to perceptually prefer patterns that are in alignment with pre-existing biases). While partial validation of the statistical model is possible, a conceptual validation would require an extended triangulation with other methods and human ratings, and clarification of whether statistical distinctivity of lexical co-occurrence correlates with conceputal topics in any reliable way.

Beyond base and collocate

Abstract Support verb constructions figure among the most frequently investigated topics in the literature on collocation. So far, most studies of this kind have focused on bipartite structures, consisting of a verbal collocate and a nominal base. Accordingly, the analysis of how support verbs are distributed has concentrated almost exclusively on the lexical control exerted by the base. In this article, we draw attention towards the influence exerted by the participation of verb and noun in more complex patterns of lexical co-occurrence. We contend that the distribution of the support verb collocate is contingent not only on the base noun but also on other elements of the lexical context. This highlights the need to enrich the theoretical framework of collocation analysis with the additional descriptive category of ‘second-order collocate’. The proposal is illustrated with two case studies using a large-scale web corpus of English.

Semantic association effect and its neural mechanism from the perspective of lexical co-occurrence frequency

<p id="C2">Word recognition and memory are influenced by the semantic associative relationship between words. The free association method has some limitations in establishing lexical semantic associations. Researchers have proposed a new method called co-occurrence association. This study analyzed the semantic association effect and its neural mechanism from the perspective of lexical co-occurrence frequency. The results showed that the number and intensity of semantic association influenced the processing of words. Words with a large number of semantic associations led to an increase in the amplitude of P200 and a decrease in the amplitude of N400. The brain regions involved in semantic association processing are mainly the superior frontal gyrus and the left medial temporal lobe. Future research should discuss the differences between free and co-occurrence association in establishing a semantic association. Based on co-occurrence association, the behavioral characteristics and neural mechanisms of semantic association are further explored. Research on the semantic association effect and neural mechanism from the perspective of co-occurrence association is recommended.

Inferior parietal lobule is sensitive to different semantic similarity relations for concrete and abstract words.

Similarity measures, the extent to which two concepts have similar meanings, are the key to understand how concepts are represented, with different theoretical perspectives relying on very different sources of data from which similarity can be calculated. While there is some commonality in similarity measures, the extent of their correlation is limited. Previous studies also suggested that the relative performance of different similarity measures may also vary depending on concept concreteness and that the inferior parietal lobule (IPL) may be involved in the integration of conceptual features in a multimodal system for the semantic categorization. Here, we tested for the first time whether theory-based similarity measures predict the pattern of brain activity in the IPL differently for abstract and concrete concepts. English speakers performed a semantic decision task, while we recorded their brain activity in IPL through fNIRS. Using representational similarity analysis, results indicated that the neural representational similarity in IPL conformed to the lexical co-occurrence among concrete concepts (regardless of the hemisphere) and to the affective similarity among abstract concepts in the left hemisphere only, implying that semantic representations of abstract and concrete concepts are characterized along different organizational principles in the IPL. We observed null results for the decoding accuracy. Our study suggests that the use of the representational similarity analysis as a complementary analysis to the decoding accuracy is a promising tool to reveal similarity patterns between theoretical models and brain activity recorded through fNIRS.

Effects of semantic plausibility, syntactic complexity and n-gram frequency on children's sentence repetition.

Theories of language processing differ with respect to the role of abstract syntax and semantics vs surface-level lexical co-occurrence (n-gram) frequency. The contribution of each of these factors has been demonstrated in previous studies of children and adults, but none have investigated them jointly. This study evaluated the role of all three factors in a sentence repetition task performed by children aged 4-7 and 11-12 years. It was found that semantic plausibility benefitted performance in both age groups; syntactic complexity disadvantaged the younger group but benefitted the older group; while contrary to previous findings, n-gram frequency did not facilitate, and in a post-hoc analysis even hampered, performance. This new evidence suggests that n-gram frequency effects might be restricted to the highly constrained and frequent n-grams used in previous investigations, and that semantics and morphosyntax play a more powerful role than n-gram frequency, supporting the role of abstract linguistic knowledge in children's sentence processing.

Competing Explanations of Competing Explanations: Accounting for Conflict Between Scientific and Folk Explanations.

People who hold scientific explanations for natural phenomena also hold folk explanations, and the two types of explanations compete under some circumstances. Here, we explore the question of why folk explanations persist in the face of a well-understood scientific alternative, a phenomenon known as explanatory coexistence. We consider two accounts: an associative account, where coexistence is driven by low-level associations between co-occurring ideas in experience or discourse, and a theory-based account, where coexistence reflects high-level competition between distinct sets of causal expectations. We present data that assess the relative contributions of these two accounts to the cognitive conflict elicited by counterintuitive scientific ideas. Participants (134 college undergraduates) verified scientific statements like "air has weight" and "bacteria have DNA" as quickly as possible, and we examined the speed and accuracy of their verifications in relation to measures of associative information (lexical co-occurrence of the statements' subjects and predicates) and theory-based expectations (ratings of whether the statements' subjects possess theory-relevant attributes). Both measures explained a significant amount of variance in participants' responses, but the theory-based measures explained three to five times more. These data suggest that the cognitive conflict elicited by counterintuitive scientific ideas typically arises from competing theories and that such ideas might be made more intuitive by strengthening scientific theories or weakening folk theories.

Sense disambiguation for Punjabi language using supervised machine learning techniques

Automatic identification of a meaning of a word in a context is termed as Word Sense Disambiguation (WSD). It is a vital and hard artificial intelligence problem used in several natural language processing applications like machine translation, question answering, information retrieval, etc. In this paper, an explicit WSD system for Punjabi language using supervised techniques has been analysed. The sense tagged corpus of 150 ambiguous Punjabi noun words has been manually prepared. The six supervised machine learning techniques Decision List, Decision Tree, Naive Bayes, K-Nearest Neighbour (K-NN), Random Forest and Support Vector Machines (SVM) have been investigated in this proposed work. Every classifier has used same feature space encompassing lexical (unigram, bigram, collocations, and co-occurrence) and syntactic (part of speech) count based features. The semantic features of Punjabi language have been devised from the unlabelled Punjabi Wikipedia text using word2vec continuous bag of word and skip gram shallow neural network models. Two deep learning neural network classifiers multilayer perceptron and long short term memory have also been applied for WSD of Punjabi words. The word embedding features have experimented on six classifiers for the Punjabi WSD task. It has been observed that the performance of the supervised classifiers applied for the WSD task of Punjabi language has been enhanced with the application of word embedding features. In this work, an accuracy of 84% has been achieved by LSTM classifier using word embedding feature.

Realizaciones temporales de la construcción fraseológica [Ssing PREP Ssing

El objetivo de este trabajo es realizar una descripción tanto formal como semánticopragmática de las realizaciones de valor temporal de la construcción binomial [Ssing PREP Ssing], de uso habitual en español. Para ello se lleva a cabo un análisis detallado, basado en datos de corpus, que incluye la justificación de su carácter fraseológico a través de los rasgos de frecuencia y de fijación, así como la explicación de los significados detectados en distintos contextos de uso y de las diferencias fundamentales entre las preposiciones empleadas y los sustantivos temporales que cubren el slot. En el análisis se presta especial atención a los datos de coocurrencia léxica.

Creating and Testing Specialized Dictionaries for Text Analysis

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