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Sentence-final completion tasks serve as valuable tools in studying language processing and the associated predictive mechanisms. There are several established sentence-completion norms for languages like English, Portuguese, French, and Spanish, each tailored to the language it was designed for and evaluated in. Yet, cultural variations among native speakers of the same language complicate the claim of a universal application of these norms. In this study, we developed a corpus of 2925 sentence-completion norms specifically for Mexican Spanish. This corpus is distinctive for several reasons: Firstly, it is the most comprehensive set of sentence-completion norms for Mexican Spanish to date. Secondly, it offers a substantial range of experimental stimuli with considerable variability in terms of the predictability of word sentence completion (cloze probability/surprisal) and the level of uncertainty inherent in the sentence context (entropy). Thirdly, the syntactic complexity of the sentences in the corpus is varied, as are the characteristics of the final word nouns (including aspects of concreteness/abstractness, length, and frequency). This paper details the generation of the sentence contexts, explains the methodology employed for data collection from a total of 1470 participants, and outlines the approach to data analysis for the establishment of sentence-completion norms. These norms provide a significant contribution to fields such as linguistics, cognitive science, and machine learning, among others, by enhancing our understanding of language, predictive mechanisms, knowledge representation, and context representation. The collected data is accessible through the Open Science Framework (OSF) at the following link: https://osf.io/js359/?view_only=bb1b328d37d643df903ed69bb2405ac0. [ABSTRACT FROM AUTHOR]

As the bilingual Spanish-speaking population in the United States (U.S.) steadily rises (U.S. Census Bureau in Language spoken at home, https://data.census.gov/cedsci/profile?q=United%20States&g=0100000US, 2020), the need for systemic therapists who are competent in working with Spanish-speaking clients also increases. While it is unrealistic to require all mental health practitioners to be bilingual English and Spanish speakers, systemic therapists can improve their understanding of the impact that clients' native language can have on the therapeutic process. In this paper, we synthesize concepts from narrative therapy and linguistic relativity to provide non-Spanish-speaking clinicians with a unique perspective of case conceptualizations and therapeutic interventions for clients who natively speak Spanish. A clinical vignette is presented to illustrate the practical application of linguistic relativity informed systemic therapy. Potential theoretical and clinical implications of this treatment suggestion are explored. [ABSTRACT FROM AUTHOR]

The present paper aims to describe the process of creating CHARG—Corpus de Habla Radiofónica de Guayaquil (the Guayaquil Radiophonic Speech Corpus). It is the first systematized spoken corpus for this rather under-researched variety of Spanish. Guayaquil is the most populated city of Ecuador, while its capital city is Quito. Therefore, Ecuador is a rare case of a Spanish-speaking country with two major urban centers that belong to two separate dialectal zones, offering a very peculiar sociolinguistic context. CHARG is a corpus composed of Guayaquil radio programs. Its structure is organized by non-linguistic criteria (program type) in order to ensure a representative and balanced sample. The paper describes the design of the corpus (defining the study population, sample and stratification) and its construction (recording procedure, speakers and speech style coding, transcription and annotation). As a result, CHARG consists of 24 h of transcriptions and annotations of recordings from 142 speakers. The paper's potential use is twofold: since it presents a step-by-step procedure of corpus construction that can be replicated, the readers might be interested in both the procedure and the corpus itself as a research material. [ABSTRACT FROM AUTHOR]

A sign language recognition system can be applied to reduce a communication gap between deaf and normal persons. However, the Indian sign language recognition (ISL) systems are in the developing stage. Most of the recent ISL recognition systems use convolutional neural networks (CNNs) where applied convolution operation shifts a kernel to overlapping portions over the image. However, these kernels may learn redundant data since real-world images have very high correlations. The training process of neural networks is challenging for redundant image data. To overcome this limitation, an ISL recognition system has been proposed in this paper that uses the network deconvolution technique. This technique reduces not only pixel-wise correlation but also a channel-wise correlation in images. The proposed model is also augmented with a spatial transformer network to increase spatial invariance of convolution operations against spatial transformations. The proposed recognizer offers better accuracy most of the time than other experimented systems on two ISL datasets VUCS_ISL_I and created VUCS_ISL_II and standard datasets of other sign languages, i.e., American sign language, Arabic sign language, Spanish sign language. [ABSTRACT FROM AUTHOR]

The widespread availability of scientific documents in multiple languages, coupled with the development of automatic translation and editing tools, has created a demand for efficient methods that can detect plagiarism across different languages. In this paper, we present a novel cross-lingual plagiarism detection approach. The algorithm is based on the merger of two existing approaches that in turn achieve state-of-the-art (SOTA) or comparable to SOTA results on different benchmarks. The detailed analysis stages of existing approaches were sequentially merged levelling out the disadvantages of the approaches. The obtained algorithm significantly outperforms the ones it was merged of surpassing them by from 23 to 33% Plagdet Score, depending on different language pairs. The comparison between observed approaches was evaluated on a newly generated multilingual (English, Russian, Spanish, Armenian) test collection, where each suspicious document could contain plagiarised fragments from several languages. The merged method is applicable to various under-resourced languages which is shown on the example of the Armenian language. [ABSTRACT FROM AUTHOR]

This paper introduces a system that incorporates several strategies based on scientific models of how the brain records and recovers memories. Methodologically, an incremental prototyping approach has been applied to develop a satisfactory architecture that can be adapted to any language. A special case is studied and tested regarding the Spanish language. The applications of this proposal are vast because, in general, information such as text way, reports, emails, and web content, among others, is considered unstructured and, hence, the repositories based on SQL databases usually do not handle this kind of data correctly and efficiently. The conversion of unstructured textual information to structured one can be useful in contexts such as Natural Language Generation, Data Mining, and dynamic generation of theories, among others. [ABSTRACT FROM AUTHOR]

In this paper, we highlight the importance of distilling the computational assessments of constructed responses to validate the indicators/proxies of constructs/trins using an empirical illustration in automated summary evaluation. We present the validation of the Inbuilt Rubric (IR) method that maps rubrics into vector spaces for concepts' assessment. Specifically, we improved and validated its scores' performance using latent variables, a common approach in psychometrics. We also validated a new hierarchical vector space, namely a bifactor IR. 205 Spanish undergraduate students produced 615 summaries of three different texts that were evaluated by human raters and different versions of the IR method using latent semantic analysis (LSA). The computational scores were validated using multiple linear regressions and different latent variable models like CFAs or SEMs. Convergent and discriminant validity was found for the IR scores using human rater scores as validity criteria. While this study was conducted in the Spanish language, the proposed scheme is language-independent and applicable to any language. We highlight four main conclusions: (1) Accurate performance can be observed in topic-detection tasks without hundreds/thousands of pre-scored samples required in supervised models. (2) Convergent/discriminant validity can be improved using measurement models for computational scores as they adjust for measurement errors. (3) Nouns embedded in fragments of instructional text can be an affordable alternative to use the IR method. (4) Hierarchical models, like the bifactor IR, can increase the validity of computational assessments evaluating general and specific knowledge in vector space models. R code is provided to apply the classic and bifactor IR method. [ABSTRACT FROM AUTHOR]

In this paper, a public dataset for Offline Handwriting Recognition, along with an appropriate evaluation method to provide benchmark indicators at sentence level, is presented. This dataset, called SPA-Sentences, consists of offline handwritten Spanish sentences extracted from 1617 forms produced by the same number of writers. A total of 13,691 sentences comprising around 100,000 word instances out of a vocabulary of 3288 words occur in the collection. Careful attention has been paid to make the baseline experiments both reproducible and competitive. To this end, experiments are based on state-of-the-art recognition techniques combining convolutional blocks with one-dimensional Bidirectional Long Short Term Memory (LSTM) networks using Connectionist Temporal Classification (CTC) decoding. The scripts with the entire experimental setting have been made available. The SPA-Sentences dataset and its baseline evaluation are freely available for research purposes via the institutional University repository. We expect the research community to include this corpus, as is usually done with English IAM and French RIMES datasets, in their battery of experiments when reporting novel handwriting recognition techniques. [ABSTRACT FROM AUTHOR]