Dictionary Information Research Articles

IDI Search: a metadata search app for exploring New Zealand’s administrative linked data

How can we lower barriers to reuse of linked administrative data and improve quality at the same time? Linked data resources are expensive and complex, but accompanying readily-accessible metadata supports data reuse and quality control. However, metadata systems that are unsystematic or non-automated can lead to inconsistencies, making metadata use difficult even for experienced analysts. Our national statistics office (NSO) maintains a research database (RD) of administrative datasets linkable at the individual level. With the NSO’s agreement, we extracted schema information (e.g. tables and variables) from the RD and obtained data dictionaries. R was used to extract information (e.g., descriptions, human-friendly names, codings) from the data dictionaries, which identified errors and inconsistencies that the NSO then fixed. A metadata database was collated using schema and data dictionary information about variables and datasets in the RD. Finally, a public web app was developed to enable exploration of the meta database by searching specific terms or navigating the hierarchical relationships. We now work with the NSO data team to re-extract variables and add or update dictionaries ahead of the regular data updates. Metadata coverage is displayed on the app and used by our NSO to improve metadata quality and coverage. Our scripting workflow demonstrates the utility of automation in picking up errors and inconsistencies quickly, and before they can propagate by copy and paste. The web app, available to existing and new users, is now listed by the NSO as a “go-to” resource for using the linked administrative data resources.

Making known the what and the why

Language is deeply intertwined with culture, and understanding the cultural nuances embedded in words and phrases is crucial for effective communication. Bilingual dictionaries play an important role in bridging the linguistic and cultural gap between languages. This paper examines the inclusion and representation of cultural information in bilingual lexicography, with a particular focus on Chinese-English dictionary compilation. It emphasizes the need to provide not only the meaning and usage of words and phrases (the what), but also the cultural nuances and the background behind them (the why). Furthermore, the presentation of cultural information in bilingual dictionaries requires the exploration of different methods and techniques. In this context, the paper explores the concepts of defamiliarization and foregrounding as means of emphasizing cultural aspects through explanatory notes, cultural annotations, examples from literature or popular culture, and other relevant resources.

Interpretable online network dictionary learning for inferring long-range chromatin interactions.

Dictionary learning (DL), implemented via matrix factorization (MF), is commonly used in computational biology to tackle ubiquitous clustering problems. The method is favored due to its conceptual simplicity and relatively low computational complexity. However, DL algorithms produce results that lack interpretability in terms of real biological data. Additionally, they are not optimized for graph-structured data and hence often fail to handle them in a scalable manner. In order to address these limitations, we propose a novel DL algorithm called online convex network dictionary learning (online cvxNDL). Unlike classical DL algorithms, online cvxNDL is implemented via MF and designed to handle extremely large datasets by virtue of its online nature. Importantly, it enables the interpretation of dictionary elements, which serve as cluster representatives, through convex combinations of real measurements. Moreover, the algorithm can be applied to data with a network structure by incorporating specialized subnetwork sampling techniques. To demonstrate the utility of our approach, we apply cvxNDL on 3D-genome RNAPII ChIA-Drop data with the goal of identifying important long-range interaction patterns (long-range dictionary elements). ChIA-Drop probes higher-order interactions, and produces data in the form of hypergraphs whose nodes represent genomic fragments. The hyperedges represent observed physical contacts. Our hypergraph model analysis has the objective of creating an interpretable dictionary of long-range interaction patterns that accurately represent global chromatin physical contact maps. Through the use of dictionary information, one can also associate the contact maps with RNA transcripts and infer cellular functions. To accomplish the task at hand, we focus on RNAPII-enriched ChIA-Drop data from Drosophila Melanogaster S2 cell lines. Our results offer two key insights. First, we demonstrate that online cvxNDL retains the accuracy of classical DL (MF) methods while simultaneously ensuring unique interpretability and scalability. Second, we identify distinct collections of proximal and distal interaction patterns involving chromatin elements shared by related processes across different chromosomes, as well as patterns unique to specific chromosomes. To associate the dictionary elements with biological properties of the corresponding chromatin regions, we employ Gene Ontology (GO) enrichment analysis and perform multiple RNA coexpression studies.

P D N: A Priori Dictionary Network for Fashion Parsing

The task of fashion parsing aims to assign pixel-level labels to clothing targets; thereby, parsing models are required to have good contextual recognition ability. However, the shapes of clothing components are complex, and the types are difficult to distinguish. Recent solutions focus on improving datasets and supplying abundant priori information, but the utilization of features by more efficient methods is rarely explored. In this paper, we propose a multi-scale fashion parsing model called the Priori Dictionary Network (PDN), which includes a priori attention module and a multi-scale backbone. The priori attention module extracts high dimensional features from our designed clothing average template as a priori information dictionary (priori dictionary, PD), and the PD is utilized to activate the feature maps of a CNN from a multi-scale attention mechanism. The backbone is derived from classical models, and five side paths are designed to leverage the richer features of local and global contextual representations. To measure the performance of our method, we evaluated the model on four public datasets, the CFPD, UTFR-SBD3, ModaNet and LIP, and the experimental results show that our model stands out from other State of the Art in all four datasets. This method can assist with the labeling problem of clothing datasets.

What should a dictionary contain to be useful for translators?

Translators are one of the main groups of dictionary users. The design, revision, and compilation of any kind of lexicographic or terminographic resource for translators should thus be based on their specific needs. To this end, the research examined the importance attributed by 221 BA and MA trainee translators to the various types of information in a dictionary entry, with a view to determining which are most useful for translation. Also identified were the differences between an ideal entry for translators and one for the general public. The results showed that the students’ answers varied depending on their year of study, as well as whether they were on the BA or MA program. The data collection tool was a self-reported questionnaire. The dictionary information most valued by trainee translators was usage contexts, definitions, phraseology, and translation equivalents. Furthermore, students at higher academic levels attributed more importance to usage contexts and phraseology. This study highlights the need to include phraseological and contextual information in the entries of translation-oriented dictionaries.

Hierarchical Locality-Aware Deep Dictionary Learning for Classification

Deep dictionary learning (DDL) shows good performance in visual classification tasks. However, almost all existing DDL methods ignore the locality relationships between the input data representations and the learned dictionary atoms, and learn sub-optimal representations in the feature coding stage, which are less conducive to classification. To this end, we propose a hierarchical locality-aware deep dictionary learning (HILADLE) framework for classification, which can learn locality-constrained dictionaries at different abstract levels through hierarchical dictionary learning. The locality constraints play an important role in learning informative dictionary atoms while preserving the data structure in the original input feature space. Moreover, instead of using an identity activation function like existing DDL methods, we further boost the generalization performance of our HILADLE method with a ReLU activation function to deal with the overfitting issue caused by over-parameterization, inspired by its effectiveness in deep neural networks. Finally, the concatenation of all feature representations learned at different layers is used as input to the final classifier. We demonstrate, through an extensive set of experiments on several benchmark face recognition, image classification, and age estimation datasets, that our method is able to surpass several dictionary learning, deep dictionary learning and deep learning methods.

CREATING DIGITAL DICTIONARIES OF LOCAL LANGUAGES AND UPGRADING SCHOOL TEACHERS PATENT KNOWLEDGEIN KONAWE AND EAST KOLAKA REGENCIES

Community Service Activities by a Team of Lecturers (Lecturers) of the Faculty of Law, Lecturers of Faculty of Cultureand Lecturers of Informatics Engineering Faculty of Engineering Haluoleo University at SDN 2 Puosu and SDN 1 SendangMulyasari in Konawe and East Kolaka Regencies, Southeast Sulawesi Province. This activity was carried out for 6 months starting from observation, coordination and consolidation with SDN 2 Puosu and SDN 1 SendangMulyasari followed by preparation of teaching materials, IPR / Patent laws, provision of Digital Dictionary Applications and Information Systems, implementation of activities and preparation of reports.The purpose of this Community Service activity is to provide knowledge about IPR / Patent Laws for Teachers and Students of SDN 2 Puosu and SDN 1 SendangMulyasari in Konawe and East Kolaka Regencies to implement the Creation of Regional Language Digital Dictionaries that can be used for learning students and teachers through the knowledge transfer process. And campaigning for pirated IPR / Patent laws and the use of Regional Language Digital Dictionary Copyrights to create Digital Dictionary Applications and Information Systems that can be used for students and teachers to learn at the office and at home.Some important materials presented in this Community Service Activity are socializing IPR / patent laws, understanding patent laws, copyright use of digital dictionaries of local languages, making applications and digital dictionary information systems, and others.The results obtained from this activity are expected to be able to understand IPR / Patent Laws and Copyright Use of Regional Language Digital Dictionaries for making Digital Dictionary Applications and Information Systems, especially for Teachers and Students of SDN 2 Puosu and SDN 1 SendangMulyasari in Konawe and East Kolaka Regencies, as well as additional knowledge and insight into the world of information technology. And it is hoped that it can help spread IPR / Patent laws and Copyright Use of Digital Regional Language Dictionaries as a form of implementation and educate students and teachers regarding the preservation of regional languages.

Multi-Dimensional Low-Rank with Weighted Schatten p-Norm Minimization for Hyperspectral Anomaly Detection

Hyperspectral anomaly detection is an important unsupervised binary classification problem that aims to effectively distinguish between background and anomalies in hyperspectral images (HSIs). In recent years, methods based on low-rank tensor representations have been proposed to decompose HSIs into low-rank background and sparse anomaly tensors. However, current methods neglect the low-rank information in the spatial dimension and rely heavily on the background information contained in the dictionary. Furthermore, these algorithms show limited robustness when the dictionary information is missing or corrupted by high level noise. To address these problems, we propose a novel method called multi-dimensional low-rank (MDLR) for HSI anomaly detection. It first reconstructs three background tensors separately from three directional slices of the background tensor. Then, weighted schatten p-norm minimization is employed to enforce the low-rank constraint on the background tensor, and LF,1-norm regularization is used to describe the sparsity in the anomaly tensor. Finally, a well-designed alternating direction method of multipliers (ADMM) is employed to effectively solve the optimization problem. Extensive experiments on four real-world datasets show that our approach outperforms existing anomaly detection methods in terms of accuracy.

Comparative Analysis of the «Explanatory Dictionary of the Living Great Russian language» by V.I. Dahl and the Chinese «Kangxi Dictionary»

The subject of study in the presented work is a comparative analysis of explanatory dictionaries of Russian and Chinese languages based on the material of the «Explanatory Dictionary of the living Great Russian language» by V.I. Dahl and the Chinese «Kangxi Dictionary». The purpose of the analysis is to clarify the similarities and differences in the nature of the explanation of the meaning of words and to compile the structure of explanatory dictionaries in the Russian and Chinese lexicographic tradition.
 The study was conducted using a descriptive and comparative method, based on a comparative analysis of the structure and presentation of information in the dictionary entry of the dictionaries under study.
 The obtained conclusions allow us to note the similarities between the Russian and Chinese explanatory dictionary: 1) when explaining the meaning of a word, both dictionaries use the same (or similar) methods; 2) the structure of both dictionaries reflects the formal semantic connections between words; 3) both dictionaries
 in the process of interpreting words provide information of an ethnographic and linguocultural nature; 4) both Russian and Chinese explanatory dictionaries approach a dictionary of a combined type. The paper notes important differences in the structure of the alphabetic and hieroglyphic dictionary, in the nature of the dictionary information provided.
 The results of the research conducted in this article can be used in research and teaching activities related to the study of the lexicography of the Russian and Chinese languages.

Металексикография, культура и лексикультура: структура и категории описания (на материале русского, английского, немецкого, французского и малагасийского языков)

The aim of the study is to identify the structural and descriptive features of the modern lexicographic cluster. The scientific novelty of the paper lies in a comprehensive identification of metalexicographic formats for the description of modern lexicographic science in line with correlations with the “maternal” culture, both at the level of vocabulary and in the space of a complete utterance. In addition, the study is novel in that it introduces into active scientific use the terms “culture gap”, “linguocultural tensor” in order to clarify the conceptual apparatus and provide a clearer semantic structuring of the term system of metalexicography, language theory and communication theory. One of the main epistemological aspects of the work is represented by the development of the problems of lexicultural interpretation of lexicographic space as a semiotic whole and value, that is, the issues of searching for the foundations of multicultural identity, which can be resolved one way or another by referring to the language of dictionaries of various types. An important point of the study is the discovery of correlations between the conceptual and epistemic dimensions of metalexicographic knowledge, in other words, between linguistic and conceptual analysis of vocabulary discourse in line with the interpretative-semiological approach. The results showed the importance of further development of the metalexicographic paradigm in line with the constantly updated philological knowledge and also made it possible to discover the information about the collective elements of the worldview and cognition of the linguoculturological community and national-cultural identity at the level of philological knowledge functioning. The data obtained allowed the researchers to identify a number of important elements of the linguosemiotic impact on the thinking of a consumer of dictionary information necessary to overcome critical and stressful situations associated with the accelerated dynamics of the development of high-tech lexicographic data formats on the path of creating new metalexicographic axiology in order to optimise cognition and communication models.

Chinese medical entity recognition based on the dual-branch TENER model.

Named Entity Recognition (NER) is a long-standing fundamental problem in various research fields of Natural Language Processing (NLP) and has been practiced in many application scenarios. However, the application results of NER methods in Chinese electronic medical records (EMRs) are not satisfactory, mainly due to the following two problems: (1) Existing methods do not take into account the impact of medical terminology on model recognition performance, resulting in poor model performance. (2) Existing methods do not fully utilize the Chinese language features contained in EMR, resulting in poor model robustness. Therefore, it is imminent to solve these two problems regarding the performance of the NER model for EMRs. In this paper, a TENER-based radical feature and entity augmentation model for NER in Chinese EMRs is proposed. The TENER model is first used in the pre-training stage to extract deep semantic information from each layer of the feature extractor. In the decoder part, the recognition of medical entity boundary and entity category are divided into two branch tasks. We compare the overall performance of the proposed model with existing models on different datasets using the computed F1 score evaluation metric. The experimental results show that our model achieves the best F1 score of 82.67%, 74.37%, 70.16% on the CCKS2019, ERTCMM, and CEMR data sets. Meanwhile, in the CMeEE challenge, our model surpassed the top-3 with the F1 score of 68.39%. Our proposed model is the first to divide the NER task into a two-branch tasks, entity boundary and types recognition. Firstly, the medical entity dictionary information is integrated into TENER to obtain the feature information of professional terms in Chinese EMRs. Secondly, the features of Chinese radicals in Chinese EMRs extracted by CNN are added to the entity category recognition task. Finally, the effectiveness of the model is validated on four datasets and competitive results are achieved.

Hierarchical 3D reconstruction algorithm of core image based on detailed and structural dictionaries

The microstructure of porous media is of great significance to the study and analysis of their physical properties and mechanical properties. At present, the three-dimensional reconstruction method is widely used to obtain digital models of porous media microstructures that can be used to reconstruct their three-dimensional structures from two-dimensional training images. The super-dimensional algorithm, a learning-based method, makes full use of a priori three-dimensional structural information, and its reconstruction effect is better than that of the aforementioned traditional method. However, most current algorithms are based on 128- or 256-size training image reconstruction, which is still far from the actual application of the image size. To make algorithms more suitable to the actual application, we herein propose a hierarchical super-dimensional reconstruction method based on the idea of a multiple grid to complete the reconstruction of large-size images, such as the three-dimensional reconstruction of 512- or even 1000-size images. Using this method, we designed a template of the detailed information dictionary and developed a corresponding matching algorithm for a hierarchical reconstruction process. To further improve the efficiency of reconstruction, dictionary element porosity characteristics were used for classification. Finally, to verify the effectiveness of the proposed method, four groups of sandstone image reconstruction experiments were carried out, and the reference images with different sizes and porosity were reconstructed several times. Furthermore, to determine the accuracy of the reconstruction results, we compared the real structure with the visual and statistical indicators, and pore and throat parameters.

ROLE OF COGNITIVE TECHNIQUES IN ENHANCING PERCEPTION AND ASSIMILATION OF EDUCATIONAL MATERIAL BY MEDICAL STUDENTS

Rapid changes in the life of society and an individual person, information exchange between them contribute to the development of cognitive activity, and in practical terms reflect the ability of education seekers to learn. With the cognitive approach, all processes are considered as components of the general information exchange between a person and the environment. In the conditions of an increase in the speed of information growth, it is necessary to create new cognitive learning technologies ways, techniques and methods that will allow education seekers to ensure an effective understanding of the real world, intellectual development and successful adaptation to life in an information-saturated environment.
 The purpose of this study is to reveal the characteristic features of cognitive teaching methods and determine their role in the perception and assimilation of educational material by students of higher medical education. Methods used include analysis, synthesis and systematisation of scientific and methodological work. Students must adequately perceive information presented in written or oral form.
 The cognitive aspect of learning involves the development of cognitive activity through the ability to identify keywords, draw logical conclusions, write notes and abstracts, comment on speeches, ask clarifying questions, participate in discussions, etc. Search skills play an important role in the selection of information in terminological dictionaries, atlases, educational and scientific literature, mass media and social networks. To develop practical skills in higher medical institutions of education, virtual stimulators, models, simulators, dummies and other technical teaching aids are used, which allow to simulate clinical situations, processes and various aspects of the future professional activity of medical students. Cognitive approaches aimed at the formation of clinical thinking in students play an important role in resolving various situational tasks ("Step 1" and "Step 2"). Cognitive techniques in medical education create conditions for students to perceive and assimilate incoming information, stimulate the ability to distinguish specific interdisciplinary connections and develop clinical thinking.
 Cognitive techniques in medical education are critical in creating optimal conditions for students to perceive and assimilate educational material, and in stimulating their ability to identify and establish interdisciplinary connections. By developing the cognitive abilities of students, higher medical education institutions can equip future healthcare professionals with the necessary skills and knowledge to meet the demands of an ever-evolving medical landscape.

The Lexicographer’s Dream Audience: Dictionary Use among English Majors at a Slovenian University

Abstract Interviews with undergraduate students from the University of Ljubljana, who are majoring in English and can be considered language specialists, investigated habits of dictionary use, look-up abilities, and perceptions of the utility and quality of definitions and illustrative examples. This contrasts with a parallel study (Farina et al. 2019) with undergraduates majoring in business and economics. Like the parallel study, this study was based on fourteen questions and nine contexts containing a clearly-marked common word used in an infrequent sense; participants had to locate the sense in a dictionary that, at the time of the studies, was the online Merriam–Webster Learner’s Dictionary, rebranded today as The Britannica Dictionary. Participants were asked to think aloud as they looked up words. Among other results, the study revealed that its participants, while they were linguistically-educated and experienced, did not fully grasp the complexity of presenting dictionary information online.

The lexicographic legacy of Professor B. A. Larin and modern lexicography (to the 130th anniversary of the birth)

The paper examines the lexicographic legacy left by Professor B. A. Larin (1893–1964). It also points out the direct correlation between Larin’s ideas and the controversial issues and moot points in modern lexicography. I provide a brief review of B. A. Larin’s biography and his philological interests. The conclusion underscores the significance of the lexicographic ideas in the linguist’s works as well as the versatility of his scientific interests. At the same time, they are united by such cross-cutting leitmotifs as a broad historical perspective, studying colloquial speech in its various manifestations, attention to semantics. B. A. Larin’s ideas that were far ahead of their time are identified as a result of comparing the concepts he developed and the directions of modern lexicography evolution. For example, one such idea is a complete dictionary. It directly correlates with the currently relevant project of a complex (universal) dictionary. Moreover, Larin raised the issues of combining linguistic and encyclopaedic information in a historical dictionary as well as of scientific illustration of historical dictionary entries, which are still unresolved. The ideas of bilingual author lexicography, onomastic author lexicography, and historical phraseology proposed by Larin have hardly been implemented yet. The paper emphasises that B. A. Larin is one of the founders of Russian historical lexicography. Also, he created such a type of historical dictionary as the thesaurus of one written monument and became the father of Russian sociolinguistics and comparative phraseology. He formulated principles for describing various aspects of vocabulary, proposed a number of innovative lexicographic projects, thus anticipating many modern lexicographic theories and developments.

Intra- and Inter-Class Induced Discriminative Deep Dictionary Learning for Visual Recognition

Deep dictionary learning (DDL) aims to learn dictionaries at different levels and the deepest level representations. However, existing DDL algorithms impose a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$l_{1}$</tex-math></inline-formula> -norm constraint on the deepest level representations, ignoring the constraints on different level representations. Meanwhile, they fail to discover effectively the essential discrimination information. Therefore, the obtained representations are less discriminative, which degrades model performance. To tackle those issues, we propose an intra- and inter-class induced discriminative deep dictionary learning (DDDL). Specifically, both intra-class compactness and inter-class separability of layer-wise data representations are newly devised as two discriminative constraints on deep dictionary learning. In a hierarchical structure, we obtain a more informative dictionary and the class-specific representations are thus more discriminative at each layer. Due to the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$l_{2}$</tex-math></inline-formula> -norm intra- and inter-class constraints of layer-wise data representation, we devise a layer-wise optimization strategy to efficiently learn the closed-form solution of the deepest representation for classification. Comprehensive experiments and analyses on several visual recognition tasks show that our DDDL model surpasses recent shallow and deep representation learning approaches.

Treatment of Pragmatic Information in the Chinese-English Dictionary

The study aims to investigate the pragmatic information in Chinese-English dictionaries. A general survey was made to examine the status quo of the pragmatic information in the existing Chinese-English dictionaries. It was found that all large- and medium-sized dictionaries under investigation provided some pragmatic information. However, these dictionaries only provided the pragmatic information about the Chinese headword but not the English equivalent. The author argues that since the target users of these dictionaries are Chinese EFL learners, they should state clearly the pragmatic differences between the headword in the source language and the equivalent in the target language. Moreover, the cultural notes provided should be focused on the English equivalents instead.

Accurate Management Practice of Medical Consumables in the Whole Process Based on SPD Supply Chain

Establish a hospital logistics information SPD system, carry out internet cloud procurement, and interface with the hospital HIS system, surgical anesthesia system, internal control management system, etc, to form an information system covering the entire process of external logistics, internal logistics, and internal control audit. Accurately classify consumables in multi-dimensions, improve the consumables dictionary field information dictionary database, refine supplier management, and realize the whole process of implanting interventional and high-value consumables into three codes (material code, medical insurance consumable code, charge code) traceability management, and data visualization. At the same time, it reduces hospital inventory and cost pressure, reduces the intensity of nursing staff's management of consumables, reduces the risks and loopholes of consumables management, and improves the level of digital and intelligent management of medical consumables.

LA INFORMACIÓN SINTÁCTICA EN LOS DICCIONARIOS BILINGÜES Y MONOLINGÜES DE APRENDIZAJE. SYNTACTIC INFORMATION IN BILINGUAL AND LEARNER'S DICTIONARIES

Bilingual and monolingual learner’s dictionaries provide information on the vocabulary a foreign language (FL) student should learn. Moreover, they specify grammatical information related to peculiar words, especially verbs. These dictionaries present, specifically, the syntactic contents in a more explicit way by means of construction indications and in a more implicit way by means of examples, or both. Starting from the English learner’s dictionaries, it is shown what lexicographical solutions the dictionaries analyzed in this paper adopt to transmit the syntactic information to the user. This description can be doubly useful: to teach the FL learner how to find syntactic information in dictionaries (something not everyone is able of) and to prepare new lexicographical works with more explicit indications.

Uses and functions of conceptual metaphor in pedagogical meditation discourse

In mindfulness and meditation instructional contexts, conceptual metaphor is a powerful facilitator of intersubjective communication about inner events and first-person experiences. Previous studies in meditation discourse have described space as a productive source domain that is used by meditation teachers to characterise metaphorically inner events and abstract concepts related to the meditative practice (Silvestre-López 2019, 2020). This study examines the discourse manifestations of the space source domain used to metaphorically characterise the most representative metaphorical topics (targets) in a corpus of introductory talks about meditation addressed to an audience of non-expert meditators. The analysis is sensitive to the communicative dimension of the use of metaphors in that it distinguishes between non-deliberate and deliberate uses (Steen 2015) and considers the functions they fulfil in discourse (Semino 2008; Goatly 2011). The corpus was analysed qualitatively following a bottom-up approach. Metaphor identification was carried out using DMIP (Reijnierse et al. 2018). Target and source domain coding was done with the help of dictionary information and the domains already identified in a metaphor compendium created in previous meditation discourse analyses (see Coll-Florit &amp; Climent 2019; Silvestre-López &amp; Navarro 2017; Silvestre-López 2020). This approach allowed different layers of domain specificity to be unveiled, with remarkable differences found between non-deliberate and deliberate metaphor use. Among them, differences in degree of source domain granularity (Langacker 2008) ranging from vaguer notions like undefined space or spaciousness to conceptually richer and finer-grained scenarios like the home and shelter, ocean, landscape, or sky models. The speakers were found to use these source domains consistently with explanatory purposes (explanatory function) to help their audience reconceptualise some of the most frequent target topics in their talks (e.g., the present moment, the meditator, the mind, awareness, or meditation) from more mindfully-oriented perspectives, hence allowing to become aware of and get rid of potential misconceptions. Deliberate uses proved particularly useful in promoting understanding through reconceptualisation, which was in turn facilitated by the vivid imagery evoked through detailed elaborations of source-domain scenery. Besides ideational functions, deliberate metaphors were also found to facilitate positive attitudinal changes towards the practice, as well as to provide for argument structuring and create textual cohesion. Overall, non-deliberate and deliberate uses were found to combine in the speakers’ production to aid them in skilfully achieving their own communicative intentions, which underscores the need for further studies that explore potential ways of applying similar findings to real meditation practice, and in instructional settings.