Set Of Words Research Articles

A multitask comparison of word- and character-frequency effects in Chinese reading.

In this study, we examined the effects of word and character frequency across three commonly used word-identification tasks (lexical decision, naming, and sentence reading) using the same set of two-character target words (N = 60) and participants (N = 82). Facilitatory effects of word frequency were observed across all three tasks. The character-frequency effects, however, were facilitatory for naming but inhibitory for both lexical decision and reading. Further correlational analyses indicated that participants' performance (as measured using overall response latencies and the sizes of the frequency effects) was not consistent across tasks but was relatively reliable within the lexical-decision and reading tasks. These findings are discussed in relation to what is known about the reading of Chinese versus alphabetic scripts, word-identification tasks, and models of word identification. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Semantic reconstruction of continuous language from non-invasive brain recordings.

A brain-computer interface that decodes continuous language from non-invasive recordings would have many scientific and practical applications. Currently, however, non-invasive language decoders can only identify stimuli from among a small set of words or phrases. Here we introduce a non-invasive decoder that reconstructs continuous language from cortical semantic representations recorded using functional magnetic resonance imaging (fMRI). Given novel brain recordings, this decoder generates intelligible word sequences that recover the meaning of perceived speech, imagined speech and even silent videos, demonstrating that a single decoder can be applied to a range of tasks. We tested the decoder across cortex and found that continuous language can be separately decoded from multiple regions. As brain-computer interfaces should respect mental privacy, we tested whether successful decoding requires subject cooperation and found that subject cooperation is required both to train and to apply the decoder. Our findings demonstrate the viability of non-invasive language brain-computer interfaces.

Influence of Speech Recognition Ability on Acceptable Noise Level for Mandarin (Chinese) Speakers with Normal Hearing

Introduction: Noise can induce hearing loss and reduce speech understanding. The Acceptable Noise Level (ANL) test has been widely used in audiology. However, strategies used by listeners to determine ANLs are unclear. The current study evaluated the role of speech recognition in selecting ANL and how well ANL could predict speech understanding in a noisy situation. Methods: Forty-five Mandarin speakers with normal hearing were tested in both ears. ANL is defined as Most Comfortable Level (MCL) minus Background Noise Level (BNL). To obtain ANL monaurally with an earphone, the study measured participants’ MCL to hear a Mandarin story in quiet and the maximum BNL to tolerate while following the story. Then, based on the participant’s ANL, speech recognition in noise was examined using a set of phonemic-balanced Mandarin words. The signal-to-noise ratio (SNR) was adjusted to ANL, ANL – 10 dB (“degraded noise condition”), and ANL + 10 dB (“improved noise condition”). Results: The mean ANLs were 2.4 dB and 2.6 dB for the left and right ears, respectively. The mean speech recognition with SNR adjusted to ANL was relatively high for both ears (81–83% correct). Even for those ear samples with very low ANL (<0 dB), speech performance obtained at SNR = ANL was still high. The mean speech recognition obtained at SNR = ANL was 5 percentage points lower than the mean speech recognition at the improved noise condition and 14 percentage points higher than the mean speech recognition at the degraded noise condition. Speech recognition obtained at SNR = ANL and ANL − 10 dB correlated significantly with ANL. Conclusion: Speech recognition in noise appears to play an important role for listeners with normal hearing in deciding their ANLs. Additionally, ANL can predict speech performance (r-squared = 53–61%) in the degraded noise condition.

Kurdish and Persian: Dialects or Separate Languages?

Although it is not quietly supported nowadays, some linguistics and orientalists believed/proposed that due to the large resemblance between the two and somehow mutual ineligibility, Kurdish is a dialect of Persian. The objective of this paper was to answer this hypothesis from a lexical point of view. Lexical Similarity measures the similarity and/or difference between a set of words from any given two languages. Despite the abundance of lexical similarity coefficients between various world languages in the literature, there are no available data on Kurdish and Persian, even though many Kurdish natives learn Persian as a second language. Our results showed (51.5 %) lexical similarity between Persian and Kurdish by using the Leipzig-Jakarta list. This result shows close proximity and common genetic roots between these two languages. However, it is not high enough to claim a dialect relation between the two; like, for example, 85% similarity index between Catalan and Spanish. Hence, this outcome, in a lexical point of view, is against the hypothesis of ‘Kurdish is a dialect of Persian’.

The longitudinal relation between executive functioning and multilayer network topology in glioma patients.

Many patients with glioma, primary brain tumors, suffer from poorly understood executive functioning deficits before and/or after tumor resection. We aimed to test whether frontoparietal network centrality of multilayer networks, allowing for integration across multiple frequencies, relates to and predicts executive functioning in glioma. Patients with glioma (n = 37) underwent resting-state magnetoencephalography and neuropsychological tests assessing word fluency, inhibition, and set shifting before (T1) and one year after tumor resection (T2). We constructed binary multilayer networks comprising six layers, with each layer representing frequency-specific functional connectivity between source-localized time series of 78 cortical regions. Average frontoparietal network multilayer eigenvector centrality, a measure for network integration, was calculated at both time points. Regression analyses were used to investigate associations with executive functioning. At T1, lower multilayer integration (p = 0.017) and epilepsy (p = 0.006) associated with poorer set shifting (adj. R2 = 0.269). Decreasing multilayer integration (p = 0.022) and not undergoing chemotherapy at T2 (p = 0.004) related to deteriorating set shifting over time (adj. R2 = 0.283). No significant associations were found for word fluency or inhibition, nor did T1 multilayer integration predict changes in executive functioning. As expected, our results establish multilayer integration of the frontoparietal network as a cross-sectional and longitudinal correlate of executive functioning in glioma patients. However, multilayer integration did not predict postoperative changes in executive functioning, which together with the fact that this correlate is also found in health and other diseases, limits its specific clinical relevance in glioma.

Automatic detection of semantic primitives using optimization based on genetic algorithm.

In this article, we propose a method for the automatic retrieval of a set of semantic primitive words from an explanatory dictionary and a novel evaluation procedure for the obtained set of primitives. The approach is based on the representation of the dictionary as a directed graph with a single-objective constrained optimization problem via a genetic algorithm with the PageRank scoring model. The problem is defined as a subset selection. The algorithm is fit to search for the sets of words that should fulfil several requirements: the cardinality of the set should not exceed empirically selected limits and the PageRank word importance score is minimized with cycle prevention thresholding. In the experiments, we used the WordNet dictionary for English. The proposed method is an improvement over the previous state-of-the-art solutions.

A Spacing Contribution on the Retention of L2 Word Forms and Meanings

This study examined the contribution of spacing on facilitating learning and retention of L2 word forms and meanings. 38 Saudi Arabian learners of English studied 30 English words (beyond their current language proficiency) using a spaced and massed displays. The former refers to the frequent distribution of repetition across multiple learning sessions, whereas the latter deals with the repetition of words into one single learning session. The 15 lexemes, hence, under the massed condition were classified into three categories of five words, each set was studied three times in one of the three sessions, whereas the other set of words were studied under the spaced condition in which all 15 words were learned once in each of the three sessions. One offline test was conducted to measure the meaning of word knowledge, and one online lexical decision task measured respondents’ accuracy and speed in recognizing the form of target words. These two tests were administrated in immediate post-test (IPT) and delayed post-test (DPT), conducted two weeks later. The results show that the meaning and form of spaced L2 words are learned and retained better than those of massed L2 words. The reaction time (RT) results also show that L2 learners who are under the spaced condition are faster in recognizing L2 target word forms than those who are under the massed condition. These findings can have meaningful theoretical and pedagogical implications for developing L2 vocabulary learning and retention.

Frequency of word usage by Hebrew preschoolers: implications for AAC core vocabulary

This study’s goal was to inform the selection of the most frequently used words to serve as a reference for core vocabulary selection for Hebrew-speaking children who require AAC. The paper describes the vocabulary used by 12 Hebrew-speaking preschool children with typical development in two different conditions: peer talk, and peer talk with adult mediation. Language samples were audio-recorded, transcribed, and analyzed using the CHILDES (Child Language Data Exchange System) tools to identify the most frequently used words. The top 200 lexemes (all variations of a single word) in the peer talk and adult-mediated peer talk conditions accounted for 87.15% (n = 5008 tokens) and 86.4% (n = 5331 tokens) of the total tokens produced in each language sample (n = 5746, n = 6168), respectively. A substantially overlapping vocabulary of 337 lexemes accounted for up to 87% (n = 10411) of the tokens produced in the composite list (n = 11914). The results indicate that a relatively small set of words represent a large proportion of the words used by the preschoolers across two different conditions. General versus language-specific implications for core vocabulary selection for children in need of AAC devices are discussed.

Generating watermarked adversarial texts

Adversarial example generation (AEG) has been a hot spot in recent years because it can cause deep neural networks (DNNs) to misclassify the generated adversarial examples, which reveals the vulnerability of DNNs, motivating us to find good solutions to improve the robustness of DNN models. Due to the extensiveness and high liquidity of natural language over the social networks, various natural language-based adversarial attack algorithms have been proposed in the literature. These algorithms generate adversarial text examples with high semantic quality. However, the generated adversarial text examples and the corresponding attack models may be maliciously or illegally used. To tackle this problem, we present a general framework encapsulated in the cloud application programming interfaces (APIs) for generating watermarked adversarial text examples to protect adversarial text examples and corresponding adversarial text attack models. For each word in a given text, a set of candidate words are determined to ensure that all the words in the set can be used to carry secret bits or facilitate the construction of adversarial example. By applying a word-level adversarial text generation algorithm, the watermarked adversarial text example can be finally generated. Experiment results show that the adversarial text examples generated by the proposed method not only successfully fool advanced DNN models, but also carry watermarks that can effectively verify the ownership and trace the source of the adversarial examples and the corresponding attack models. Moreover, the watermark can still survive after attacked with AEG algorithms, which has shown the applicability and superiority.

Effects of Multimedia Academic Vocabulary Instruction for Linguistically Diverse Fifth Graders in a Rural Setting

As K-12 classrooms in the U.S. grow in linguistic diversity, there is a need to develop and evaluate the effects of instructional approaches that build knowledge of academic vocabulary for multilingual learners. While multimedia vocabulary instruction may represent a promising avenue for improving word learning for linguistically diverse classrooms of upper-elementary students, there is a dearth of empirical evidence on this topic, with no research on multimedia vocabulary instruction having been conducted with linguistically diverse populations in rural contexts. This randomized control trial evaluated the effects of a multimedia vocabulary instructional approach (CAP-S), relative to business-as-usual (BAU) vocabulary instruction on researcher-developed and standardized measures of word knowledge for a linguistically diverse sample of Grade 5 students learning in rural schools (n = 418). The intervention took place over 8 weeks; CAP-S and BAU teachers addressed the same target set of academic words. Hierarchical linear modeling indicated statistically significant and meaningful effects on a custom measure of word learning, as well as small but statistically significant effects on a standardized assessment of word knowledge. On the custom vocabulary measure, ELs benefited more than non-ELs from CAP-S instruction, relative to their counterparts in the BAU condition. Findings suggest the potential advantages of employing efficient, feasible multimedia vocabulary approaches to serve diverse, multilingual student groups in rural, upper-elementary classrooms.

A Co-Optimization Algorithm Utilizing Particle Swarm Optimization for Linguistic Time Series

The linguistic time-series forecasting model (LTS-FM), which has been recently proposed, uses linguistic words of linguistic variable domains generated by hedge algebras (HAs) to describe historical numeric time-series data. Then, the LTS-FM was established by utilizing real numeric semantics of words induced by the fuzziness parameter values (FPVs) of HAs. In the existing LTS-FMs, just the FPVs of HAs are optimized, while the used word set is still chosen by human experts. This paper proposes a co-optimization method of selecting the optimal used word set that best describes numeric time-series data in parallel with choosing the best FPVs of HAs to improve the accuracy of LTS-FMs by utilizing particle swarm optimization (PSO). In this co-optimization method, the outer loop optimizes the FPVs of HAs, while the inner loop optimizes the used word set. The experimental results on three datasets, i.e., the “enrollments of the University of Alabama” (EUA), the “killed in car road accidents in Belgium” (CAB), and the “spot gold in Turkey” (SGT), showed that our proposed forecasting model outperformed the existing forecasting models in terms of forecast accuracy.

Risk factors for adverse outcomes of percutaneous endoscopic gastrostomy

Introduction. Presently, percutaneous endoscopic gastrostomy is considered to be the operation of choice when long-term enteral feeding in dysphagia syndrome is required. The technique is characterized by low incidence of early and long-term complications due to low traumatic access and operation time. Aim of the work was to assess the risk predictors of percutaneous endoscopic gastrostomy complications and their significance for the postoperative period. Materials and methods. We searched publications and analyzed literature sources using PubMed and MedLine databases and Cochrane Library and elibrary.ru scientific libraries mainly for the last ten years. The search was performed using a set of key words: percutaneous endoscopic gastrostomy, dysphagia syndrome, postoperative complications, risk factors. Results and discussion. The leading and most studied predictors of early and long-term postoperative period complications after gastrostomy are hypoalbuminemia, uncompensated diabetes mellitus, thrombocytopenia, elevated levels of C-reactive protein, Charlson's comorbidity index (4 points or higher), antiaggregant and anticoagulant therapy, morphoconstitutional features of patients. Conclusion. Data analysis has shown that percutaneous endoscopic gastrostomy at high patient flow and gastropectomy supplementation with gastropexy is practically safe and effective if the selection criteria for the operation are observed taking into account the general condition, risk factors and exclusion of absolute contraindications.

Intergeneration Division Based on Key Component Analysis in an Autonomous Transportation System Using the Natural Language Processing Method

Advancement of emerging technologies and increasing of transport demands accelerate the evolution of the autonomous transportation system (ATS). Framework and architecture of ATS are becoming a research hotspot; however, by far, few studies on transportation intergeneration division are not basically involved. Previous works indicate that key components are critical representation in the distinguishing of long-term era. Besides, massive text material accumulates as the research work goes on, and natural language processing technique keeps developing, which makes quantitative research on key components in intergeneration division become possible. In this work, a method based on the massive text analysis is proposed. First, the LDA2vec is used to get the relationship between components and other elements. Then, a word set is from the component word set extraction module based on component items. Finally, the component word set is clustered to get ATS generation and to generate key components. Based on an analysis of large-scale important traffic texts, our method divides the traffic system into three generations for Chinese traffic from 2010 to 2022. The key components of our method given are consistent with human cognition of ATS. Successful application indicates that this work can be extended to other intergeneration division fields.

Polysyllabic shortening in Modern Standard Arabic

Abstract This is the first study that investigates polysyllabic shortening in Arabic. It aims to find out the extent to which Modern Standard Arabic employs this phonetic mechanism, which is usually associated with stress-timed languages. Data were collected from 10 Jordanian speakers reading 6 sets of words representing the six monophthongs in Arabic in a carrier sentence. Based on the acoustic analysis of 180 vowels, it was found that the vowels in monosyllabic words were significantly longer than their counterparts in polysyllabic words. However, the differences between the vowels in disyllabic and trisyllabic words were much smaller. Findings suggest that polysyllabic shortening is not invoked as a mechanism to maintain the stress-timed rhythm of stress-timed languages. Rather, it seems to be a universal phonetic feature that applies in different degrees that are commensurate with the rhythm of the language in question.

The Impact of the Implementation of International Law on Marine Environmental Protection on International Public Health Driven by Multi-Source Network Comment Mining

With the increase of people's living space, global warming caused by the decrease of greening urban spaces and the serious decline of greenspace quality has led to extreme weather events and coastal erosion, which has become the biggest threat to the ocean and has also led to the occurrence of international public safety incidents. Therefore, it is of great practical significance to explore the tense relationship between the current marine environmental protection and global public safety for the development of an international healthy community. Firstly, this paper discusses the influence of implementing the international law of marine environmental protection on global public health after the reduction of green urban space and the decline of green space quality. Secondly, K-means and discrete particle swarm optimization algorithms are introduced and the particle swarm optimization-K-means clustering (PSO-K-means) algorithm is designed to screen and deal with the mapping relationship between latent variables and word sets about the impact of implementing the international marine ecological protection law on the international public health community in network data information. Moreover, the influencing factors are clustered and the scenarios are evaluated. The results show that the clustering analysis of the marine environment can promote the clustering of marine characteristic words. Meanwhile, the PSO-K-means algorithm can effectively cluster vulnerability data information. When the threshold is 0.45, the estimated recall rate of the corresponding model is 88.75%. Therefore, the following measures have been formulated, that is, increasing greening urban spaces and enhancing the quality of green space to enhance the protection of marine environment, which has practical reference value for realizing the protection of marine environment and the sustainable development of marine water resources and land resources.

Khmer Sentiment Lexicon Based on PU Learning and Label Propagation Algorithm

The sentiment lexicon is an important tool for natural language processing tasks. In addition to being able to determine the sentiment polarity of words or phrases, it can assist attribute-level, sentence-level, and text-level sentiment analysis tasks. In light of the fact that tagging data and corpora for the Khmer language are scarce, where most resources related to sentiment lexicons are for English, this paper proposes a method for constructing a sentiment lexicon for Khmer based on Positive-Unlabeled learning (PU Learning) and the label propagation algorithm. Sentiment words are first extracted from a corpus using the Spy technique of PU learning method. The main idea is to purify the set of N-class examples, train the MLP classifier, and continuously delete spy words and increase the number of P-class words in the iterative process. Following this, the sentiment polarity of the candidate words is determined. By considering the problem of determining the sentiment polarity of the candidate words as one of calculating its probability distribution, a small number of labeled sentiment words and candidate words are used to construct a graph model. The contextual information of the candidate words is used to construct a simple supplementary graph model of the set of sentiment words through word co-occurrence and triangulation, where this enhances the correlation between data items. The sentiment polarity of the candidate words is then determined through the label propagation algorithm. The results of experiments show that the proposed method can be used to construct a Khmer sentiment lexicon with a small number of labeled data and a small corpus without requiring excessive manual labeling.

Swardspeak as a Communication Medium Among University Students: Empirical Evidence from the Philippines

This descriptive study identified the profile, common swardspeak and frequency of its use, and the situations of usage among the Bachelor of Secondary Education students in a higher education institution in the Philippines during the academic year 2020-2021. Thirty (30) randomly selected respondents supplied the data through a survey questionnaire, which was analyzed using frequency count and percentage. Results showed that most of the students who use swardspeak are female within the age range of 19-22 years old. The most common swardspeak used are afam, akeno/aketch/akis, awra, baboosh, beks/vaklangtow, bonak/junakis, bongga, borlog, chaka/shonget, char/charot/chos, cheka/chika, chosera/chusera, churva, daks, dites/ditey, eclavu, eme/keme, ganern, gora/gorabels, gurl, Hagardo Versoza, hanash, imas, juntis/juntils, juts, kabog, kalerki/nakakalurky, keribels/keriboomboom/keri lang, kiber, knowangji, krayola, lafang, Lotlot/Lotlot De Leon, mamsh/momshee/mudra/mudrakels, murayta, pagoda, pakak, payola, pudra, shala, sinetch/sinetchitey, sizt, shokot, shonga/shunga, shupatid, spyokang, Tom Jones, waley/wis/wit/wiz, warla, and Winwin Marquez. Most of these swardspeak words are sometimes used in public places with friends, and in instances or situations where they have to go with or join a group. These cross-sectional findings can affect further studies as this swardspeak can be enhanced as this generation goes out and come up with sets of new words.

A functional test for hearing aids outcomes assessment

We present a functional test for speech understanding called hearing aids (HAs) objective outcomes – HO2 (pronounced as /hɑ:t/) app. It uses natural speech stimuli and generates word level accuracies, phonetic, and broad-phonetic confusion matrices. In the current best practices, hearing loss (HL) is diagnosed using pure tone audiometry (PTA). While PTA is also a functional test, it characterizes only sound detection ability of discrete pure tones, but not overall speech intelligibility due to cochlear impairments leading to sensorineural hearing loss. HO2 app stimuli are based on minimal contrast sets (MCS) of words in a given language. Each word within a subgroup of MCS words differ in a minimal set of (acoustic) phonetic features resulting in a different meaning—phonemics of the given language. The tool includes configuration parameters to include video and closed captions to minimize the role of higher level (cortical) language processes. Researchers can save HO2 app experimental conditions in a configuration file and share with others for repeatability and reproducibility. We present the feasibility of normal and hearing-impaired subjects (mild and moderate HL) to self-administer speech comprehension tests in multiple listening conditions using HO2 app. For the hearing-impaired subjects, results in unaided and aided conditions are presented.

Representation of deterministic graphs by defining pair of words

The paper proposes a representation of deterministic graphs (D-graphs) by sets of words in the alphabet of labels of their vertices. Nowadays graphs are a conceptual tool for analytics, development and testing of various software. Among all graphs, there is a subclass of labeled graphs whose elements have labels from a predefined alphabet. Such graphs are actively used to describe and model computational processes in programming, robotics, model verification and validation, etc. Labeled graphs are an information environment for mobile agents, whose movement along the graph can be represented by sequences of vertex labels - words in the label alphabet. A vertex-labeled graph is said to be D-graph if all vertices in the neighborhood of every its vertex have different labels. For such graphs, in the case when its graph map (i.e., the set of vertices and edges and the labeling function) and the initial vertex from which the agents start their movements are known, there is an unambiguous correspondence between the sequence of labels of the vertices visited by the agent and the trajectory of this agent's movements in the graph. In the case when the map of the studied D-graph is unknown to an external observer, the movements of agents can be organized in such a way that, based on their analysis, the observer receives the desired information about the structure of the graph (for example, the map of the graph, the shortest paths between vertices, comparison of the studied graph with the reference graph). Such an analysis can significantly simplify the linguistic representation of a D-graph - the mapping of a graph to one or more finite sets of words in the alphabet of graph vertex labels, which can be used to reconstruct the graph. In this paper, we propose a representation of deterministic graphs by a defining pair of word sets, the first component of which describes the cycles of the graph, and the second - its leaf vertices. This representation is analogous to the system of defining relations for automata. We proposed the algorithm, that for any pair of sets, builds a D-graph for which this pair is deterministic, or reports that it is impossible to do so is given. The algorithm for constructing a canonical defining pair for a D-graph is also given and numerical estimates of this pair for D-graphs with a known number of vertices and edges are found. Further directions of research on this topic are outlined. The results will allow to use new methods and algorithms for solving problems of analysis of graphs with marked vertices.

Differences in young children's emotional valence ratings of 180 stimuli

Words and pictures are often used as stimuli in studies of investigate relations between emotion and cognition. Selecting controlled stimuli for experimental design in this area represents a crucial issue. Only few standardized databases of pictures or words are available for children. None compile at the same time ratings for both lexical and visual stimuli. The present study provides a French child database for a corpus of 180 stimuli (90 words, 90 pictures, 90 combined words-pictures). These stimuli were rated on emotional valence (emotional: negative, positive, and non-emotional: neutral) by 178 French children differing in age (4, 5 and 6-year-old) and gender (boys, girls). Results showed age and gender differences in the children's ratings. With increase of age, the stimuli rated as positive decreased, and the stimuli rated as neutral increased. We found gender related differences in the valence ratings, 5 and 6-year-old girls shown more neutral ratings than did boys. We observed a significant difference in ratings between stimulus modalities. Children were more accurate while rating words and pictures than combined words-pictures in each age group and gender. The norms and the set of words and pictures are available in OSF (Center for Open Science), https://osf.io/zcge3/.