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The increasing availability of cross-linguistic databases dedicated to documenting morphosyntactic, lexical and phonological features has proliferated the use of such data for studies on language evolution and human history. However, most of these databases were not designed to ensure independence of features, such that it is not valid to jointly use all their features in large-scale statistical analyses assuming independence of inputs. Here, we curate published data from five large linguistic databases to generate two global-scale cross-linguistic datasets: GBI (from the Grambank dataset), and TLI (using inputs from the World Atlas of Language Structures, AUTOTYP, PHOIBLE and Lexibank). The datasets minimize logical dependencies of features and forms of strong statistical dependencies that go beyond phylogenetic and geographical signal. They are also made available in densified form, reducing the proportion of missing data. We document our curation principles and workflows to ensure reusability of this framework with other inputs or thresholds of independence. Our curation steps on both datasets reveal robust and comparable global patterns of structural linguistic diversity. [ABSTRACT FROM AUTHOR]

Although research has established that students enrolled in pre-sessional English for academic purposes (EAP) programs make gains in English reading ability, the evidence base for whether gains made during this period of instruction make a difference to future academic outcomes is nonexistent. We report a multi-cohort longitudinal study of a 28-week university-level EAP program that was designed to improve the English language skills of international students in preparation for undergraduate study. The sample comprised N = 405 EAL students who completed the same EAP program in a Canadian university. At the beginning and end of the EAP program students completed a battery of 12 measures, including reading comprehension, reading fluency, and other established component skills of second language reading ability. We investigated whether the change scores of these measures, as markers of skill growth, predicted the post-EAP program undergraduate grade point averages (GPAs) of these same students. The Random Forests nonparametric regression technique was used to estimate the relative importance of all change scores of the reading skill tests, as well as their scores at the beginning of the program. This method identified reading fluency change during the EAP program as a high-ranking predictor of future GPAs. Additional stepwise linear regression modelling confirmed that reading fluency change made unique significant contributions to GPAs in such a way that greater growth in this measure translated into higher GPAs. These findings suggest that reading speed development during an EAP program has a long-term and beneficial impact on the academic flourishing of EALs. [ABSTRACT FROM AUTHOR]

This study examines the potential of virtual reality (VR) technology in language learning, specifically for the Maltese language. Despite the popularity and advancement of VR, its use in teaching minor languages like Maltese has not been extensively explored. The concern is that the lack of technological resources may lead to the extinction of the Maltese language in the digital world. The study utilizes a primarily qualitative research methodology, with a minor quantitative component, to collect data from twenty-five teachers regarding the use of VR in Maltese language learning contexts. The findings reveal both the potential benefits and challenges of using VR in teaching Maltese as a second language (ML2). Although the participants have not used VR for teaching Maltese due to the absence of Maltese VR apps, they have experience with VR in English. They praise VR for its ability to create an immersive and engaging learning environment with entertaining features. However, the study also highlights several challenges associated with VR, including high costs, the bulkiness of headsets, and technical issues. It underscores the pressing need for additional research and development in this field to ensure the preservation of the Maltese language in the digital era. [ABSTRACT FROM AUTHOR]

In this article, we describe the architecture of the LIMA (Libre Multilingual Analyzer) framework and its recent evolution with the addition of new text analysis modules based on deep neural networks. We extended the functionality of LIMA in terms of the number of supported languages while preserving existing configurable architecture and the availability of previously developed rule-based and statistical analysis components. Models were trained for more than 60 languages on the Universal Dependencies 2.5 corpora, WikiNer corpora, and CoNLL-03 dataset. Universal Dependencies allowed us to increase the number of supported languages and generate models that could be integrated into other platforms. This integration of ubiquitous Deep Learning Natural Language Processing models and the use of standard annotated collections using Universal Dependencies can be viewed as a kind of model and data interoperability, complementary to the technical interoperability between systems. [ABSTRACT FROM AUTHOR]

Language-related IB research predominantly targets large MNCs, often overlooking the challenges that internationalizing SMEs face in effectively utilizing their limited language resources. To address this gap and assist SMEs in overcoming their unique language challenges, we develop a scale for language operative capacity (LOC), which measures a firm's capacity to deploy language resources most productively throughout its global operations. We validate this instrument with data from 417 SMEs and examine its impact on two critical internationalization metrics: export performance and international networking capability. Our study makes three key contributions to language-related IB research and capacity research. First, we provide a comprehensive and robust LOC instrument, incorporating distinctions between potential and realized capacity from the capacity literature. Second, we offer a deeper understanding of language as a multi-level phenomenon by highlighting the interaction between individual-level language skills and organizational-level capacities. Third, we extend the scarce language-related IB research on SMEs by offering a nuanced understanding of LOC as a strategic resource, by validating the LOC instrument in the context of internationalizing SMEs, and by demonstrating its explanatory power for essential internationalization metrics. Additionally, our research provides SMEs with an actionable tool to assess their linguistic capacities, develop their language resources and optimize the use of these language resources in international operations. [ABSTRACT FROM AUTHOR]

English has long been regarded as the universal language. Countries that were earlier reluctant to learn English have also changed their stand due to its global reach. The nonnative English speaker's proficiency largely depends on the College English Teaching (CET) and its evaluation methods. Traditional teaching evaluation models failed to consider all the difficulties of language learning. This drawback has led to researchers focusing on integrating Artificial Intelligence (AI) techniques in CET evaluation. This work is in line with such an effort to contribute a novel Teaching Evaluation Method (TEM) that employs a Machine Learning (ML) algorithm. The objective of this work is to provide colleges and other institutions with a practical framework for evaluating the CET by modifying the existing models through contemporary pedagogical approaches, thereby helping the institutions meet the complex demands of teaching and collaborative research. The study proposes a novel ML model that combines Convolutional Neural Networks (CNN) with a Grey Correlation-Based Genetic Algorithm (GCBGA) to meet the challenges in the traditional TEM. The proposed TEM model fared better than the other models in terms of English Teaching Evaluation. The performance enhancement is attributed to the novel GCBGA that optimized the proposed CNN model by avoiding it from getting stuck in local minima. This new method makes it easier for CNN to consistently judge the quality of teaching because it gets rid of the subjectivity and unpredictability of the conventional methods. [ABSTRACT FROM AUTHOR]

The paper proposes universal conceptual modeling, conceptual modeling that strives to be as general-purpose as possible and accessible to anyone, professionals and non-experts alike. The idea of universal conceptual modeling is meant to catalyze new thinking in conceptual modeling and be used to evaluate and develop conceptual modeling solutions, such as modeling languages, approaches for requirements elicitation, or modeling tools. These modeling solutions should be usable by as many people and design agents as possible and for as many purposes as possible, aspiring to the ideals of universal conceptual modeling. We propose foundations of universal conceptual modeling in the form of six principles: flexibility, accessibility, ubiquity, minimalism, primitivism, and modularity. We then demonstrate the utility of these principles to evaluate existing conceptual modeling languages and understand conceptual modeling practices. Finally, we propose future research opportunities meant to realize the ideals of universal conceptual modeling. [ABSTRACT FROM AUTHOR]

Mandarin Chinese is typologically unusual among the world's languages in having flexible word order despite a near absence of inflectional morphology. These features of Mandarin challenge conventional linguistic notions such as subject and object and the divide between syntax and semantics. In the present study, we tested monolingual processing of argument structure in Mandarin verb-final sentences, where word order alone is not a reliable cue. We collected participants' responses to a forced agent-assignment task while measuring their electroencephalography data to capture real-time processing throughout each sentence. We found that sentence interpretation was not informed by word order in the absence of other cues, and while the coverbs BA and BEI were strong signals for agent selection, comprehension was a result of multiple cues. These results challenge previous reports of a linear ranking of cue strength. Event-related potentials showed that BA and BEI impacted participants' processing even before the verb was read and that role reversal anomalies elicited an N400 effect without a subsequent semantic P600. This study demonstrates that Mandarin sentence comprehension requires online interaction among cues in a language-specific manner, consistent with models that predict crosslinguistic differences in core sentence processing mechanisms. [ABSTRACT FROM AUTHOR]

The development of a standard anatomical language in Spanish dates back to the early stages of medicine, and its significance lies in the necessity for a common vocabulary to describe the structures of the human body. Anatomists and morphologists have been striving to achieve this goal since the late nineteenth century when the first Anatomical Nomenclature was created in Basel in 1895. Over the years, various versions of the International Anatomical Terminology have been published in Spanish, following the guidelines of the International Federation of Associations of Anatomists (IFAA). These versions have been produced in partnership with the Spanish Anatomical Society and have aimed to adjust the terms to the Spanish language and linguistic rules. The Ibero-Latin American Symposia on Anatomical, Histological, and Embryological Terminology (SILAT) have embraced the naming of morphological structures, keeping in mind the Latin and Greek roots, while respecting the translations accepted by the linguistic rules of the official languages. These symposia stick to the principles of universality, precision in structure description, and consistency in terminology. Despite more than a century of progress in anatomical terminology, there is a need for frequent revisions and adjustments to anatomical terms, particularly when translating them into various languages and because of the high number of international publications related to anatomy. A shared anatomical language is essential for the study and application of anatomy across various medical practices and in undergraduate education. Finally, the establishment of anatomical terminology in Spanish represents a long history of efforts to create a universal and precise language in this field. Despite advancements, the revision and adaptation of anatomical terms remain a continuous challenge to keep up with scientific progress and ensure effective communication in the field of anatomy. [ABSTRACT FROM AUTHOR]

Copyright of English Teaching & Learning is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Wurm & Hattori's Language Atlas of the Pacific Area describes the geographic speaker areas of the languages and language varieties spoken in the Pacific. Thanks to the efforts of the Electronic Cultural Atlas Initiative, this monumental piece of work has been available in digital form for over 15 years. But lacking proper identification of language varieties, this digitized data was largely unusable for today's research methods. We turned ECAI's digitized artefacts of the Language Atlas into an open, reusable geo-referenced dataset of speaker area polygons for a quarter of the world's languages. This allows for much more refined analysis methods to, for example, analyse language contact in the area of the world with the highest linguistic diversity. We also describe a number of tool applications and quality checks which may be useful for methodological development in similar digitization efforts. [ABSTRACT FROM AUTHOR]

Easy Language is a language variety that aims to make information more comprehensible and, ultimately, more accessible. Content in this variety is written and designed following a set of recommendations that have been published in different guidelines. However, it remains uncertain to what extent these recommendations are backed up by empirical research. The aim of this study is to review the existing literature that evaluates current recommendations in Easy Language guidelines, on the basis of the following research questions: (a) is there empirical research that evaluates current international Easy Language recommendations? and, (b) if so, what current international Easy Language recommendations are supported by empirical research and what results were obtained? To this end, we conducted a systematic literature review based on journal articles in three languages: Catalan, English, and Spanish. First, a systematic search was designed and performed in 10 databases of different fields of science. Then, we reviewed every article that resulted from the search and found that 6 publications out of the initial 617 met the inclusion criteria and could be considered relevant for the study. Based on the data extracted from the included publications, and after an overall review of our systematic search results, we safely state that there is indeed empirical research on some current Easy Language recommendations. Nevertheless, empirical research in the field (at least in the publication format and languages considered in our study) is not enough in terms of the number of publications, and the findings obtained are far from generalisable. Our literature review suggests future lines of research, and we hope that it fosters empirical studies in the field that help support the existing findings. [ABSTRACT FROM AUTHOR]

To obtain a reliable and accurate automatic speech recognition (ASR) machine learning model, it is necessary to have sufficient audio data transcribed, for training. Many languages in the world, especially the agglutinative languages of the Turkic family, suffer from a lack of this type of data. Many studies have been conducted in order to obtain better models for low-resource languages, using different approaches. The most popular approaches include multilingual training and transfer learning. In this study, we combined five agglutinative languages from the Turkic family—Kazakh, Bashkir, Kyrgyz, Sakha, and Tatar,—in order to provide multilingual training using connectionist temporal classification and an attention mechanism including a language model, because these languages have cognate words, sentence formation rules, and alphabet (Cyrillic). Data from the open-source database Common voice was used for the study, to make the experiments reproducible. The results of the experiments showed that multilingual training could improve ASR performances for all languages included in the experiment, except Bashkir language. A dramatic result was achieved for the Kyrgyz language: word error rate decreased to nearly one-fifth and character error rate decreased to one-fourth, which proves that this approach can be helpful for critically low-resource languages. [ABSTRACT FROM AUTHOR]

Analyzing the client's reviews from various online platform helps to improvise the business to higher levels. These User's opinions can be analyzed using Sentiment Analysis. Sentimental analysis on Indian languages is a tedious work as there is a wide diversity in different languages of the India. Kannada is one of the prominent languages in India as 43 million of Indian population use Kannada as their native language for communication and it holds 27th rank among top 30 languages across the world, as there is very less work carried out on Indian languages, especially in Kannada language, more work is required to process the Kannada language across different domains. The sentimental analysis on the Kannada language has the accuracy about 72% from the previous work. So, in this work, we have made comparative study of various machine learning algorithms for Kannada Twitter sentimental analysis. It is experimented on live Twitter data and found that Multinomial Naive Bayes Classifier has performed better with accuracy of 75%. [ABSTRACT FROM AUTHOR]

This study aims to shed light on the contribution of universal versus language specific factors on reading. We examined eye movements of Arabic readers and analyzed effects specific to Arabic such as perceptual complexity, diglossia and morphology, in addition to universal factors such as word length and frequency. Twenty native Arabic speakers read continuous texts in Modern Standard Arabic (MSA) while their eye movements were monitored. A corpus-based analyses was carried to test effects specific to Arabic and effects of the benchmark eye movement factors. We found that perceptually more complex words received longer fixation durations, moreover, differences in processing words unique in MSA versus words shared between MSA and spoken Arabic Vernacular were found. This is the first indication for these effects during an eye movement reading task. However, the effect of morphological length was not significant when included in the model with all predictors. Lastly, the benchmark factors were significant showing effects for word length, word frequency and part of speech. Short and frequent words are processed faster than longer and less frequent words. Function words are often skipped. We conclude that eye movement of Arabic readers reflect proficient reading, yet they also exhibit an on-going challenge in processing the written language. [ABSTRACT FROM AUTHOR]

Copyright of Journal of International Business Studies is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Most studies of language production have been conducted with speakers of alphabetic languages, but relatively little research has examined languages with non-alphabetic scripts, such as Chinese. Moreover, most work on language word production has investigated phonological output processing (i.e., speaking), whereas comparatively little research has focused on orthographic output, such as writing and typing. Work on non-alphabetic languages and/or written production is particularly promising, given that it speaks to universalities vs. specificity in terms of architectures and mechanisms underlying language processing across all world languages and modalities. The current article reports a dataset of word production in Chinese with spoken and written responses, which includes reaction times of 193,851 trials of naming 403 pictures obtained from 667 participants across 23 Chinese word production experiments. All data were collected in the same experimental environment and from participants with relatively homogenous characteristics, using the same protocols and parameters. The dataset enables researchers to explore how Chinese speakers produce spoken and/or written words, and to identify language-specific features underlying word production. [ABSTRACT FROM AUTHOR]

Cross-lingual spoken language understanding (cross-lingual SLU), as a key component of task-oriented dialogue systems, is widely used in various industrial and real-world scenarios, such as multilingual customer support systems, cross-border communication platforms, and international language learning tools. However, obtaining large-scale and high-quality datasets for SLU is challenging due to the high cost of dialogue collection and manual annotation, particularly for minority languages. As a result, there is increasing interest in leveraging high-resource language data for cross-lingual transfer learning. Existing approaches for zero-shot cross-lingual SLU primarily focus on the relationship between the source language sentence and the single generated cross-lingual sentence, disregarding the shared information among multiple languages. This limitation weakens the robustness of multilingual word embedding representations and hampers the scalability of the model. In this paper, we propose the multilingual mixture attention interaction framework with adversarial training to alleviate the above problems. Specifically, we leverage the source language sentence to generate multiple multilingual hybrid sentences, in which words can adaptively capture unambiguous representations from the aligned multilingual words during the encoding phase, and adversarial training is introduced to enhance the scalability of the model. Then, we incorporate the symmetric kernel self-attention module with positional embedding to learn contextual information within a sentence, and employ the multi-relation graph convolutional networks to learn different granularity information between two highly correlated intent detection and slot filling tasks. Experimental results on the public dataset MultiATIS++ demonstrate that our proposed model achieves state-of-the-art performance, and comprehensive analysis validates the effectiveness of each component. [ABSTRACT FROM AUTHOR]

The discussion about linguistic universals, initiated over 50 years ago by J. Greenberg, remains a topic of significant interest today. While there's an ongoing debate on the universality of linguistic forms - with some supporting the notion and others dismissing it as baseless—the idea of a unifying linguistic essence stands firm. The article aims to explore the linguacultural values of language, particularly how they enable the reconstruction of ideas and stereotypes within specific segments of a language system. The research found that universal language characteristics serve as a rich source of information for those studying linguacultural aspects. These traits offer nationally determined interpretations of words, drawing from associations rooted in either tangible or cultural codes. When delving into cultural codes present within every culture, the study discerned the presence of basic and universal codes. The existence of these codes provides insights into the formation of stereotypes, which encapsulate the experiences and knowledge intrinsic to the bearers of a culture. Linguistic universals, underpinned by universal grammar and semantic foundations, occupy a central position in linguacultural examinations, especially when reconstructing cultural ideas and stereotypes. Basic and universal cultural codes inherent in each culture facilitate the creation of linguistic worldviews. [ABSTRACT FROM AUTHOR]

The translation and sharing of languages around the world has become a necessary precondition for the movement of people. Teaching Chinese as a foreign language (TCFL) undertakes international function of spreading national culture. How to translate Chinese as a foreign language into English has become an important task. Machine translation has moved beyond the realm of theory to practical use as a result of advancements in computing. Deep learning is a prominent and relatively young subfield of machine learning that has shown promising results in a variety of fields. This paper aims to develop a TCFL-oriented English-Chinese neural machine translation model. First, this paper proposes a hyperbolic tangent long short-term memory network (HTLSTM). This will integrate future information and historical information to extract more sufficient contextual semantic information. Secondly, this paper proposes a multi-subspace attention mechanism. This integrates multiple attention calculation functions in the multi-subspace attention mechanism (MSATT). Thirdly, this paper combines HTLSTM with MSATT to construct an English-Chinese bilingual neural translation model called ECBTNet. The multi-subspace attention maps hidden state of hyperbolic tangent long-term short-term memory network to multiple subspaces. This then uses multiple attention calculation functions in the multi-attention mechanism when calculating the attention score. By applying different attention calculation functions in different subspaces to extract omni-directional context information features, accurate attention calculation results can be obtained. Finally, a systematic experiment is carried out, and the experimental data verify the feasibility of applying ECBTNet to the field of English-Chinese translation in TCFL. [ABSTRACT FROM AUTHOR]

Spanish is one of the most spoken languages in the world. Its proliferation comes with variations in written and spoken communication among different regions. Understanding language variations can help improve model performances on regional tasks, such as those involving figurative language and local context information. This manuscript presents and describes a set of regionalized resources for the Spanish language built on 4-year Twitter public messages geotagged in 26 Spanish-speaking countries. We introduce word embeddings based on FastText, language models based on BERT, and per-region sample corpora. We also provide a broad comparison among regions covering lexical and semantical similarities and examples of using regional resources on message classification tasks. [ABSTRACT FROM AUTHOR]

The research investigates the system of the Indo-European verb through the lens of Gustave Guillaume's psycho-systematics theory. By employing "mental vision" and phenomenology, the linguistic concept was explored at multiple levels: the surface structure of language in speech and the deeper level within the Indo-European language system. The analysis of the tense system of the Indo-European verb starts with the ontologisation of a person in both the world and language. This perspective highlights the taxonomic relevance of philosophical factors that shape the development of the "image of time" from a prehistoric viewpoint within the Indo-European language family. The purpose of this research is to delve into the Indo-European verb system using Gustave Guillaume's psycho-systematics theory as the theoretical framework. To achieve this, the authors draw on examples from French, English, Ukrainian, and Russian languages. The study aims to examine the process of the Indo-European verb's development within the ontological opposition of space and time and the ontological status of the grammatical tense and mood categories in the Indo-European verb system. It was discovered that grammatical forms of verbs in the Indo-European language family provide insights into their formal essence. These forms represent the "positions" they occupy within the ontological dialectical relationship of space, time, language, and thinking. The research sheds light on the Indo-European verb system by applying Guillaume's psycho-systematics theory. By clarifying the relationship between space, time, language, and thinking, a deeper understanding of linguistic structures and how they reflect human cognition and conceptualization of action can be gained. [ABSTRACT FROM AUTHOR]

The number of international students in Chinese higher education has expanded unprecedentedly over the past decade. As opposed to neoliberal marketisation, this expansion is backed up by a community of shared future for mankind rationale, which is well articulated within the policy rhetoric as well as keynote speeches by Chinese senior government officials. However, there is a paucity of research and thoresiation into how Chinese universities may be contributing to this strategic priority in specific contexts. This article presents a case study that examines the aspirations of a cohort of African international students enroled in a Chinese language programme in a vocational college in Southeast China. Their narratives highlight the previously neglected relevance of international higher education and Chinese language learning to the process of global citizenship education. The participants in this study displayed positive dispositions of global citizenship such as an in-depth understanding of the world, promoting hargramony and contributing to the common good. As such, this article underscores the affordances and possibilities of Chinese language learning in international higher education, in addressing a torrent of mounting concerns of 'China threat' to the world order. [ABSTRACT FROM AUTHOR]

Albeit diverse, human languages exhibit universal structures. A salient example is the syllable, an important structure of language acquisition. The structure of syllables is determined by the Sonority Sequencing Principle (SSP), a linguistic constraint according to which phoneme intensity must increase at onset, reaching a peak at nucleus (vowel), and decline at offset. Such structure generates an intensity pattern with an arch shape. In humans, sensitivity to restrictions imposed by the SSP on syllables appears at birth, raising questions about its emergence. We investigated the biological mechanisms at the foundations of the SSP, testing a nonhuman, non-vocal-learner species with the same language materials used with humans. Rats discriminated well-structured syllables (e.g., pras) from ill-structured ones (e.g., lbug) after being familiarized with syllabic structures conforming to the SSP. In contrast, we did not observe evidence that rats familiarized with syllables that violate such constraint discriminated at test. This research provides the first evidence of sensitivity to the SSP in a nonhuman species, which likely stems from evolutionary-ancient cross-species biological predispositions for natural acoustic patterns. Humans' early sensitivity to the SSP possibly emerges from general auditory processing that favors sounds depicting an arch-shaped envelope, common amongst animal vocalizations. Ancient sensory mechanisms, responsible for processing vocalizations in the wild, would constitute an entry-gate for human language acquisition. [ABSTRACT FROM AUTHOR]

In today's multilingual lexical databases, the majority of the world's languages are under-represented. Beyond a mere issue of resource incompleteness, we show that existing lexical databases have structural limitations that result in a reduced expressivity on culturally-specific words and in mapping them across languages. In particular, the lexical meaning space of dominant languages, such as English, is represented more accurately while linguistically or culturally diverse languages are mapped in an approximate manner. Our paper assesses state-of-the-art multilingual lexical databases and evaluates their strengths and limitations with respect to their expressivity on lexical phenomena of linguistic diversity. [ABSTRACT FROM AUTHOR]

Visual Question Answering (VQA) is the problem of automatically answering a natural language question about a given image or video. Standard Arabic is the sixth most spoken language around the world. However, to the best of our knowledge, there are neither research attempts nor datasets for VQA in Arabic. In this paper, we generate the first Visual Arabic Question Answering (VAQA) dataset, which is fully automatically generated. The dataset consists of almost 138k Image-Question-Answer (IQA) triplets and is specialized in yes/no questions about real-world images. A novel database schema and an IQA ground-truth generation algorithm are specially designed to facilitate automatic VAQA dataset creation. We propose the first Arabic-VQA system, where the VQA task is formulated as a binary classification problem. The proposed system consists of five modules, namely visual features extraction, question pre-processing, textual features extraction, feature fusion, and answer prediction. Since it is the first research for VQA in Arabic, we investigate several approaches in the question channel, to identify the most effective approaches for Arabic question pre-processing and representation. For this purpose, 24 Arabic-VQA models are developed, where two question-tokenization approaches, three word-embedding algorithms, and four LSTM networks with different architectures are investigated. A comprehensive performance comparison is conducted between all these Arabic-VQA models on the VAQA dataset. Experiments indicate that the performance of all Arabic-VQA models ranges from 80.8 to 84.9%, while utilizing Arabic-specified question pre-processing approaches of considering the special case of separating the question tool and embedding the question words using fine-tuned Word2Vec models from AraVec2.0 have significantly improved the performance. The best-performing model is which treats the question tool as a separate token, embeds the question words using AraVec2.0 Skip-Gram model, and extracts the textual feature using one-layer unidirectional LSTM. Further, our best Arabic-VQA model is compared with related VQA models developed on other popular VQA datasets in a different natural language, considering their performance only on yes/no questions according to the scope of this paper, showing a very comparable performance. [ABSTRACT FROM AUTHOR]

This study utilizes Cobarrubias' (1983. Ethical issues in status planning. In: Progress in language planning: international perspectives. pp. 41–85) taxonomy of language ideologies as the framework to uncover the ideologies underlying language policy and planning (LPP) in the Philippines. We discuss the different language ideologies, including linguistic assimilation, vernacularization, linguistic pluralism, and internationalization, through a historical analysis of the mandated LPP in the Philippines and how they have influenced the country's LPP. Our findings reveal that legislation and administrative measures have played a crucial role in the process of linguistic assimilation, which has historically been the dominant ideology underlying LPP in the Philippines. Furthermore, we highlight that various language ideologies can coexist and be present simultaneously within a language policy or society. We also posit that Cobarrubias' (1983. Ethical issues in status planning. In: Progress in language planning: international perspectives. pp. 41–85) framework has the potential to aid in the analysis and enhancement of LPP in other countries facing similar linguistic scenarios. Additionally, multilingual and multicultural countries must continually adapt their LPP to emphasize inclusive language policies, language maintenance initiatives, and national identity construction that values and respects linguistic diversity, empowers local communities, and strives for social inclusivity and cohesion within the context of neocolonialism and globalization. [ABSTRACT FROM AUTHOR]

Sign languages are gesture-based languages used by the deaf community of the world. Every country has a different sign language and there are more than 200 sign languages in the world. American Sign Language (ASL), British Sign Language (BSL), and German Sign Language (DGS) are well-studied sign languages. Due to their different grammatical structure and word order the deaf people feel difficulty in reading and understanding the written text in natural languages. In order to enhance the cognitive ability of the deaf subjects some translation models have been developed for these languages that translate natural language text into corresponding sign language gestures. Most of the earlier natural to sign language translation models rely on rule-based approaches. Recently, some neural machine translation models have been proposed for ASL, BSL, DGS, and Arabic sign language. However, most of these models have low accuracy scores. This research provides an improved and novel multi-stack RNN-based neural machine translation model for natural to sign language translation. The proposed model is based on encoder–decoder architecture and incorporates attention mechanism and embeddings to improve the quality of results. Rigorous experimentation has been performed to compare the proposed multi-stack RNN-based model with baseline models. The experiments have been conducted using a sizeable translation corpus comprising of nearly 50,000 sentences for Pakistan Sign Language (PSL). The performance of the proposed neural machine translation model for PSL has been evaluated with the help of well-established evaluation measures including Bilingual Evaluation Understudy Score (BLEU), and Word Error Rate (WER). The results show that multi-stacked gated recurrent unit-based RNN model that employs Bahdanau attention mechanism and GloVe embedding performed the best showing the BLEU score of 0.83 and WER 0.17, which outperform the existing translation models. The proposed model has been exposed through a software system that converts the translated sentences into PSL gestures using an avatar. The evaluation of the usability has also been performed to see how effectively the avatar-based output helps compensating the cognitive hearing deficit for the deaf people. The results show that it works well for different granularity levels. [ABSTRACT FROM AUTHOR]

Background and purpose: The Gastrointestinal Quality of Life Index (GIQLI) is an instrument for the assessment of quality of life (QOL) in diseases of the upper and lower GI tract, which is validated in several languages around the world. The purpose of this literature review is the assessment of the GIQLI in patients with benign colorectal diseases. Reports on GIQLI data are collected from several institutions, countries, and different cultures which allows for comparisons, which are lacking in literature. Methods: The GIQL Index uses 36 items around 5 dimensions (gastrointestinal symptoms (19 items), emotional dimension (5 items), physical dimension (7 items), social dimension (4 items), and therapeutic influences (1 item). The literature search was performed on the GIQLI and colorectal disease, using reports in PubMed. Data are presented descriptively as GIQL Index points as well as a reduction from 100% maximum possible index points (max 144 index points = highest quality of life). Results: The GIQLI was found in 122 reports concerning benign colorectal diseases, of which 27 were finally selected for detailed analysis. From these 27 studies, information on 5664 patients (4046 female versus 1178 male) was recorded and summarized. The median age was 52 years (range 29–74.7). The median GIQLI of all studies concerning benign colorectal disease was 88 index points (range 56.2–113). Benign colorectal disease causes a severe reduction in QOL for patients down to 61% of the maximum. Conclusions: Benign colorectal diseases cause substantial reductions in the patient's QOL, well documented by GIQLI, which allows a comparison QOL with other published cohorts. [ABSTRACT FROM AUTHOR]

In recent years, the e-commerce industry has developed rapidly. This is one aspect of the wide application of science and technology in business. Artificial intelligence technology is now inseparable from human society, and artificial technology and human society are each other to promote progress. Voice is the carrier of language, and voice recognition is actually the conversion of voice signals into symbols that can be recognized by the system. Accuracy can be greatly improved, but also converts a variety of different languages into a universal symbol so that it can be recognized by a computer and its meaning can be understood. The development of the e-commerce industry did not explode suddenly but went through a long process. At the beginning of its development, it was mainly through the Internet to communicate with the e-commerce industry under the universal application of artificial intelligence technology. E-commerce is also combined with new technologies to continuously enrich the development model. Under this development trend, B2C e-commerce has also been greatly promoted. However, there are still many B2C e-commerce which retain the traditional business model and cannot make good use of big data. Therefore, this paper analyzes the current situation of B2C e-commerce platform, introduces artificial intelligence technology into consumer behavior recognition and analyze the urgent needs of consumer behavior recognition. For B2C e-commerce companies, in order to improve management efficiency and reduce operating costs, it is necessary to classify users in a targeted manner and find the target users they need. Combine data mining algorithms to build data models, this article effectively suggests a set of consumer behavior recognition models to help companies find consumers quickly. [ABSTRACT FROM AUTHOR]

Automatic Speech Recognition (ASR) has become one of the major research areas over the past decade and gained a lot of interest. Their system implementation, adaptation to different languages and robustness in the performance are still some of the major challenges. Hindi is one of the most widely spoken languages in the world but it is a complex and resource-constraint language. Thus, speech recognition and classification systems need to be developed for Hindi language to spread the technology and to explore more communication means. But due to its language complexity than other languages and lack of standard databases, it is quite challenging to develop such systems. Deep learning is extensively used in different research fields and has proven its prominence to a broader extent. In this paper, a seven-layer 1D-convolutional neural network HindiSpeech-Net has been proposed to recognise different speech samples of the Hindi language in the respective category. A large dataset of 2400 speech samples in the Hindi language is collected in ten different classes in real-world conditions which is further accompanied by signal filtering and augmentation to enhance the dataset for making a robust model and avoid overfitting. The collected dataset is divided into training, validation and test set which were evaluated in different performance parameters. The trained HindiSpeech-Net model achieved an accuracy of 92.92% on the test set. The proposed framework is computationally less expensive, works in real-time and is suitable for implementation in embedded systems. [ABSTRACT FROM AUTHOR]

I Rom i relatives of all kinds - gapped or internally headed - display island effects, i.e. positions internal to the I rom i clause that are separated from the edge of the I rom i clause by an island cannot be relativized. But if the embedded clause does not follow the main verb, the subordinator I rom i cannot be the initial element, and behaves as I rom i in relative clauses.[4] Graph Postnominal I rom i relatives cannot be internally headed. The external syntax of gapped rom relatives In a correlative where the correlative clause is a gapped I rom i relative, the correlative clause and the demonstrative phrase associated with it cannot be separated by islands. Free-standing I rom i clauses must have an internal head, I rom i clauses that are part of an externally headed relative cannot have an internal head, while those that function as correlative clauses can. We also demonstrate that islands constrain the demonstrative- I rom i clause relationship but only in the case of gapped I rom i clauses. [Extracted from the article]

This article discusses the need for a universal language of anatomy to facilitate communication in the field. It traces the history of anatomical terminology, from the introduction of Latin anatomical nomenclature in the sixteenth century to the development of Terminologia Anatomica (TA) in 1998, which uses Latin and English. The article explores the diverging viewpoints on anatomical terminology, with one school favoring traditional Latin and the other advocating for standardized terminological structure. The author hopes that this special issue will contribute to the creation of a universal language of anatomy. [Extracted from the article]

Spelling errors are fundamental errors in text writing. The digital era has added another dimension called keyboard layout to this problem. Memorization, language orthography, and keyboard layout are sources of spelling errors in electronic texts. English is being the linked language of the world, good quantum of work towards the spelling error detection and plausible suggestions has been done for English language. But it is not the case for digital resources scarce languages like Indian languages. Marathi which is the official language of Maharashtra State in India and the world's 10th highest spoken language is not exception to this. Various computational approaches for spelling error detection and correction have been advocated in the literature. Amongst these, similarity-based measures have proven to be the prominent ones. This paper discusses the detailed contrastive study of the two popular similarity measures viz. minimum edit distance and cosine similarity measures in the context of mis-spelled Marathi words. The philosophical and empirical aspects of these methods have also been presented. For experimentation purpose we have chosen a dataset of 9, 29, 663 unique Marathi words harvested from various sources. We have obtained an accuracy of 85.88% and 86.76% for minimum edit distance algorithm and the cosine similarity algorithm, respectively. [ABSTRACT FROM AUTHOR]

Recent advancements in computer science and engineering have significantly boosted interest in and success rate of speech recognition systems. Sophisticated speech recognition systems are being developed for a variety of languages having wide-spread applications. Arabic being one of the most widely used languages in the world, augmented by religious importance, is a potential candidate in this domain, however rich vocabulary and multiple dialects open up new challenges for development of Arabic speech recognition processes. This paper focuses on development of an Arabic isolated speech recognition system for the vocabulary of Holy Quran using Mel frequency cepstral coefficients and deep neural networks. The proposed system is capable of identifying individual words from a recited verse with reasonable accuracy. It targets the 362 unique words of the first and the last 19 chapters of the Holy Quran and uses 14 hours of audio data to demonstrate the working prototype of the system. A user-friendly, web-based application has also been developed for transcription of recitations. The software is specified to make an effort in spreading the right way of recitation of the Holy Quran. The presented architecture can be extended to a complete automatic speech recognition system to help people practice recitations, mapping speech to transcription and then matching the transcribed utterance with standard templates of correct recitation to identify reciter's short comings. [ABSTRACT FROM AUTHOR]

Analyses of truncation patterns (e.g. Rob, from Robert) have traditionally focused on the templatic shape of such forms, while less attention has been paid to the question of which parts of the base word may survive in the truncatum. This feature of truncation, usually referred to as 'anchoring,' is investigated in the present paper. On the empirical basis of the generalizations emerging from an extensive database of truncation patterns in the world's languages, a formal typology of anchoring in the framework of Optimality Theory is constructed and its defining ranking conditions (its 'typological properties') are extracted. The typological properties reveal the grammatical forces shaping the various classes of truncation patterns. They show that anchoring constraints must indeed form an integral part of any model of morphological truncation since—in interaction with templatic size-restrictor constraints—they determine whether truncation occurs at all and whether output forms vary in size or are of a fixed templatic shape. A thorough analysis of anchoring thus also provides evidence for templatic shapes emerging from constraint interaction and against an approach in terms of fixed, language-specific templates. [ABSTRACT FROM AUTHOR]

The purpose of this study was to examine the use of social communication acts exhibited by nonvocal preschool-aged autistic children across different language environments. Participants from English and non-English-speaking families were exposed to social interactions with a bilingual interventionist who interacted with the participant in two conditions: English and non-English (world language). Results of this study indicated that there were differences in social communication acts across participants, but not within participants across language environments. Participants did not appear to discriminate between language environments, which supports previous research suggesting that there are no harmful effects in exposing autistic children to more than one language and parents should not be discouraged from encouraging bilingualism with their autistic children. Implications of the findings, limitations, and future research directions are discussed. [ABSTRACT FROM AUTHOR]

Machine Translation is a mechanism of transforming text from one language to another with the help of computer technology. Earlier in 2018, a machine translation system had been developed by the authors that translate Sanskrit text to Universal Networking Language expressions and was named as SANSUNL. The work presented in this paper is an extension of SANSUNL system by enhancing POS tagging, Sanskrit language processing and parsing. A Sanskrit stemmer having 23 prefixes and 774 suffixes with grammar rules are used for stemming the Sanskrit sentence in the proposed system. Bidirectional long short-term memory (Bi-LSTM) and stacked LSTM deep neural network models have been used for part of speech tagging of the input Sanskrit text. A tagged dataset of around 400 k entries for Sanskrit have been used for training and testing the neural network models. Proposed Sanskrit context-free grammar has been used with CYK parser to perform the parsing of the input sentence. Size of the Sanskrit-Universal Word dictionary has been increased from 15000 to 25000 entries. Approximately 1500 UNL generation rules have been used to resolve the 46 UNL relations. Four datasets UC-A1, UC-A2, Spanish server gold standard dataset, and 500 Sanskrit sentences taken from the general domain have been used for validating the system. The proposed system is evaluated on BLEU and Fluency score metrics and has reported an efficiency of 95.375%. [ABSTRACT FROM AUTHOR]

Scientific studies of language behavior need to grapple with a large diversity of languages in the world and, for reading, a further variability in writing systems. Yet, the ability to form meaningful theories of reading is contingent on the availability of cross-linguistic behavioral data. This paper offers new insights into aspects of reading behavior that are shared and those that vary systematically across languages through an investigation of eye-tracking data from 13 languages recorded during text reading. We begin with reporting a bibliometric analysis of eye-tracking studies showing that the current empirical base is insufficient for cross-linguistic comparisons. We respond to this empirical lacuna by presenting the Multilingual Eye-Movement Corpus (MECO), the product of an international multi-lab collaboration. We examine which behavioral indices differentiate between reading in written languages, and which measures are stable across languages. One of the findings is that readers of different languages vary considerably in their skipping rate (i.e., the likelihood of not fixating on a word even once) and that this variability is explained by cross-linguistic differences in word length distributions. In contrast, if readers do not skip a word, they tend to spend a similar average time viewing it. We outline the implications of these findings for theories of reading. We also describe prospective uses of the publicly available MECO data, and its further development plans. [ABSTRACT FROM AUTHOR]

Two views contend to account for the processes at play during the early stages of visual word recognition. The first holds that these stages are not modulated by the idiosyncratic properties of different languages. The second maintains that the structural properties of the language determine the weighting of the different domains of linguistic knowledge (e.g., orthographic and morphological domains may be differentially weighted across languages). To explore this question, we focused on orthographic priming in Arabic. In this Semitic language, lexical representations are claimed to be based on morphological similarity, with little or no role for orthographic similarity. We conducted two masked priming experiments using the yes–no and go/no-go versions of the lexical decision task to determine if Arabic target words (e.g., مدير ‘mudyr’, director) are facilitated by nonword primes that are orthographically but not morphologically related (i.e., pairs share neither a root nor a word pattern; e.g.,ماير ‘maAyr’) relative to unrelated primes. Results showed faster responses for the orthographically related target words than for the unrelated target words in the two experiments. These findings favor the view that the early phases of visual word processing in Semitic and Indo-European languages are fundamentally the same. [ABSTRACT FROM AUTHOR]

The past decades have seen substantial growth in digital data on the world's languages. At the same time, the demand for cross-linguistic datasets has been increasing, as witnessed by numerous studies devoted to diverse questions on human prehistory, cultural evolution, and human cognition. Unfortunately, most published datasets lack standardization which makes their comparison difficult. Here, we present a new approach to increase the comparability of cross-linguistic lexical data. We have designed workflows for the computer-assisted lifting of datasets to Cross-Linguistic Data Formats, a collection of standards that make these datasets more Findable, Accessible, Interoperable, and Reusable (FAIR). We test the Lexibank workflow on 100 lexical datasets from which we derive an aggregated database of wordlists in unified phonetic transcriptions covering more than 2000 language varieties. We illustrate the benefits of our approach by showing how phonological and lexical features can be automatically inferred, complementing and expanding existing cross-linguistic datasets. Measurement(s) expressions Technology Type(s) data aggregation Factor Type(s) none Sample Characteristic - Organism human language Sample Characteristic - Location global scale [ABSTRACT FROM AUTHOR]

The majority of microbial genomes have yet to be cultured, and most proteins identified in microbial genomes or environmental sequences cannot be functionally annotated. As a result, current computational approaches to describe microbial systems rely on incomplete reference databases that cannot adequately capture the functional diversity of the microbial tree of life, limiting our ability to model high-level features of biological sequences. Here we present LookingGlass, a deep learning model encoding contextually-aware, functionally and evolutionarily relevant representations of short DNA reads, that distinguishes reads of disparate function, homology, and environmental origin. We demonstrate the ability of LookingGlass to be fine-tuned via transfer learning to perform a range of diverse tasks: to identify novel oxidoreductases, to predict enzyme optimal temperature, and to recognize the reading frames of DNA sequence fragments. LookingGlass enables functionally relevant representations of otherwise unknown and unannotated sequences, shedding light on the microbial dark matter that dominates life on Earth. Computational methods to analyse microbial systems rely on reference databases which do not capture their full functional diversity. Here the authors develop a deep learning model and apply it using transfer learning, creating biologically useful models for multiple different tasks. [ABSTRACT FROM AUTHOR]

Speech recognition is a fascinating process that offers the opportunity to interact and command the machine in the field of human-computer interactions. Speech recognition is a language-dependent system constructed directly based on the linguistic and textual properties of any language. Automatic speech recognition (ASR) systems are currently being used to translate speech to text flawlessly. Although ASR systems are being strongly executed in international languages, ASR systems' implementation in the Bengali language has not reached an acceptable state. In this research work, we sedulously disclose the current status of the Bengali ASR system's research endeavors. In what follows, we acquaint the challenges that are mostly encountered while constructing a Bengali ASR system. We split the challenges into language-dependent and language-independent challenges and guide how the particular complications may be overhauled. Following a rigorous investigation and highlighting the challenges, we conclude that Bengali ASR systems require specific construction of ASR architectures based on the Bengali language's grammatical and phonetic structure. [ABSTRACT FROM AUTHOR]

The present study exemplifies the potential of International Corner Speed Chatting activities to enhance English fluency when local students use English as an international language (EIL) with other local as well as international students. For data collection during the activity, classroom observation through video recording and photography was employed; a closed and open-ended questionnaire were administered after the activity. The data analysis results show that this set of activities, incorporating mini presentation, speed chatting, reflection reporting, and group discussion, helped students develop EIL fluency. The fluency activities provided the instructor the opportunity of taking on the roles of planner and guide whilst encouraging learner autonomy. EIL fluency was achieved through maximal repeated effective integrated-skills English speaking and listening across multiple contexts with interlocutors possessing varied linguistic and cultural backgrounds. [ABSTRACT FROM AUTHOR]

Can quantum theory provide examples of metaphysical indeterminacy, indeterminacy that obtains in the world itself, independently of how one represents the world in language or thought? We provide a positive answer assuming just one constraint of orthodox quantum theory: the eigenstate-eigenvalue link. Our account adds a modal condition to preclude spurious indeterminacy in the presence of superselection sectors. No other extant account of metaphysical indeterminacy in quantum theory meets these demands. [ABSTRACT FROM AUTHOR]

Housing plays a central role in everyday life and the fulfillment of human needs. Temporary housing demand can occur due to migration, extreme environmental events or personal decisions, and is expected to increase in the upcoming years. This study aims to create a general understanding of temporary housing. We conducted an integrated comparison of 66 international temporary housing examples via tabulation (table work), in an interdisciplinary manner considering details regarding built structure, open spaces, area, infrastructure, organizational and socio-economic aspects. It is the first time that a systematic comparison via tabulation (based on the approach of Braun-Blanquet) is used to classify temporary housing environments. The process is described in detail. The application of the systematic comparison creates a detailed typology that allows extensions and further differentiations. The types that emerge from the tabulation have specific concepts regarding the structural-spatial organization, technical infrastructure, and organizational matters, among others. The typology was further examined in the context of previous groupings in published literature. This novel approach of analyzing and structuring temporary housing offers a comprehensive perspective that can work as a universal understanding and language for precise communication among different disciplines regarding temporary housing. [ABSTRACT FROM AUTHOR]

Sign languages are used by the deaf community around the globe to communicate with one another. These are gesture-based languages where a deaf person performs gestures using hands and facial expressions. Every gesture represents a word or a phrase in the natural language. There are more than 200 different sign languages in the world. In order to facilitate the learning of sign languages by the deaf community, researchers have compiled sign language repositories comprising of gestures. Similarly, algorithms have been proposed to translate the natural language into sign language, which is subsequently converted into gestures using avatar technology. On the other hand, several different approaches for gesture recognition have also been proposed in the literature, many of which use specialized hardware. Similarly, cell phone applications have been developed for learning and translation of sign languages. This article presents a systematic literature review of these multidisciplinary aspects of sign language translation. It provides a detailed analysis of carefully selected 147 high-quality research articles and books related to the subject matter. Specifically, it categorizes different approaches used for each component, discusses their theoretical foundations, and provides a comparative analysis of the proposed approaches. Lastly, open research challenges and future directions for each facet of the sign language translation problem have been discussed. To the best of our knowledge, this is the first comprehensive survey on sign language translation that discusses state-of-the-art research from multi-disciplinary perspectives. [ABSTRACT FROM AUTHOR]

This paper investigates origins, original languages and authors of bestselling translations on the annual Dutch Top 100 bestseller list. Considering the first fifty entries on the lists from the period between 1997 and 2019, the study aims to determine the Dutch position within the World Language System. The results show that about half of all the books surveyed are translations. These come from fifteen different source languages, although a clear majority are translations from English (73.2%). The analysis confirms the notion of a World Language System with central, semi-peripheral and peripheral languages and places Dutch among the peripheral languages. Furthermore, the study reveals strong globalisation and commercialisation tendencies in the Dutch book market. [ABSTRACT FROM AUTHOR]

While the demographic diversity of children served in early childhood classrooms continues to grow, world language learning opportunities for English-speaking preschoolers remain a rarity. Efforts to teach world languages in addition to English at the preschool level may be hampered by limited early childhood educator expertise, lack of programmatic fit, and uncertain expectations from participating families. This study used a foreign language exploratory (FLEX) program design for French language learning to examine whether these barriers could be overcome through a collaborative partnership between a world languages specialist, the classroom teacher, and the children's families. The 10-week study involved twelve 3-year-old children from varied home language backgrounds attending an English-only preschool. Results showed that following the world language specialist's weekly classroom visits, all participating children demonstrated some level of French language learning, and that learner engagement as a variable was strongly correlated with the child's ability to acquire French, rather than variables of age, gender, or language used at home. Furthermore, while statistically significant differences in language learning beliefs existed between families who did and did not speak a language other than English at home prior to the study, no significant belief differences between these families were found following participation in the French program. Based on the results of this programmatic effort, the authors posit that a language ladder of supports wherein the childcare center and families form parallel sides of ongoing support while the WL specialist provides the rungs that unite both sides, may be a useful model of collaboration. [ABSTRACT FROM AUTHOR]