Word Length Research Articles

The role of vision in the acquisition of words: Vocabulary development in blind toddlers.

What is vision's role in driving early word production? To answer this, we assessed parent-report vocabulary questionnaires administered to congenitally blind children (N=40, Mean age=24months [R: 7-57months]) and compared the size and contents of their productive vocabulary to those of a large normative sample of sighted children (N=6574). We found that on average, blind children showed a roughly half-year vocabulary delay relative to sighted children, amid considerable variability. However, the content of blind and sighted children's vocabulary was statistically indistinguishable in word length, part of speech, semantic category, concreteness, interactiveness, and perceptual modality. At a finer-grained level, we also found that words' perceptual properties intersect with children's perceptual abilities. Our findings suggest that while an absence of visual input may initially make vocabulary development more difficult, the content of the early productive vocabulary is largely resilient to differences in perceptual access. RESEARCH HIGHLIGHTS: Infants and toddlers born blind (with no other diagnoses) show a 7.5 month productive vocabulary delay on average, with wide variability. Across the studied age range (7-57months), vocabulary delays widened with age. Blind and sighted children's early vocabularies contain similar distributions of word lengths, parts of speech, semantic categories, and perceptual modalities. Blind children (but not sighted children) were more likely to say visual words which could also be experienced through other senses.

Finding the longest delay paths for the array-form multipliers using a genetic algorithm

Traditional digital multipliers are often designed in array forms or other variants. Timing of array-form multipliers can be analyzed by static timing analysis (STA), but the obtained timing result is conservative and pessimistic. Although statistical static timing analysis (SSTA) can partly solve the pessimism, it still does not generate test patterns for those near-to-longest delay paths. Finding near-to-longest delay paths can be helpful to designing error tolerant circuits, with which aggressive timing (with timing violation) can be exploited. In such design scenarios one should find test vectors to activate those near-to-longest delay paths in order to further run SPICE-precision diagnose on those potential timing violating critical paths. Test vector generation for such a testing problem is essentially an exhaustive enumeration problem when dealing with different forms of array multipliers. However, large size multipliers would result in an extremely large enumeration space for finding the longest delay path (LDP) test vectors. Currently there is no deterministic method that can guarantee to find test vectors for exact LDPs of a large size multiplier. Only very few research papers have addressed this problem, proposals are limited to heuristic methods without guarantee of finding the LDPs with the testing vectors. This paper investigates the potential of a genetic algorithm (GA) for searching the extensive test pattern space. By a fine design of GA, experimental running shows that a combination of well tuned evolutionary operators does empower the possibility of finding the LDPs for a set of moderate size carry-save adders (CSA) multipliers with the wordlength (WL) up to 25 bits on a plain laptop computer. Statistical properties of the proposed GA are examined.

Linguistic Characteristics of Research Article Titles in National and International ELT Journals

Researchers have extensively studied the linguistic characteristics of research article (RA) titles. However, no research has investigated RA titles viewed from research design types published in national and international English Language Teaching (ELT) journals. Thus, the present study aims to fill this gap by investigating the average word length, syntactic structure, and syntactic structure type based on research design. This study used a comparative mixed method design to analyze 120 RA titles published in 2022-2023. The average length of titles was analyzed by counting the parts of speech of the tiles, the syntactic structures of titles were analyzed using the Framework of Cheng et al. (2012), and syntactic structure types based on research design were analyzed by reading the method section of the articles. Results indicate that the average length of the RA titles was 12 words in national journals and 13 words in international journals. Nominal structure was the most used RA title type in national journals, while compound structure was the most used in international journals. Furthermore, the nominal structure was often used for experimental design in international journals, while it was often used for descriptive study design in national journals. The compound and Verb-ing phrase structures were often used for case study design in both international and national journals. In conclusion, there are important differences and similarities in RA title characteristics between the two groups of ELT journal rankings. The findings contribute to the scholarship on the contrastive aspect of titling practices in English for Publishing Purposes.

On Learning Polynomial Recursive Programs

We introduce the class of P-finite automata. These are a generalisation of weighted automata, in which the weights of transitions can depend polynomially on the length of the input word. P-finite automata can also be viewed as simple tail-recursive programs in which the arguments of recursive calls can non-linearly refer to a variable that counts the number of recursive calls. The nomenclature is motivated by the fact that over a unary alphabet P-finite automata compute so-called P-finite sequences, that is, sequences that satisfy a linear recurrence with polynomial coefficients. Our main result shows that P-finite automata can be learned in polynomial time in Angluin's MAT exact learning model. This generalises the classical results that deterministic finite automata and weighted automata over a field are respectively polynomial-time learnable in the MAT model.

Area and delay efficient RNS-based FIR filter design using fast multipliers

In today's digital age, speed, and area are the primary design concerns. Increasing the rate at which multiplication and addition are performed has always been a need for developing cutting-edge technologies. Wallace multipliers, and Dadda multipliers, are one of the fastest multipliers used in many processors to accomplish fast arithmetic operations. A novel approach to design a LookUp Table (LUT) multiplier was proposed and implemented in Finite Impulse Response (FIR) filter. To improve the Residue Number System (RNS) based FIR filter's performance, several adders like Carry Look ahead (CLA) adder, Kogge Stone adder (KSA) and proposed adder architectures have been implemented. When compared with the 16 tap with 32 bit proposed adder with LUT multiplier, the hardware resource utilization (Logic Elements) is decreased by 5.97 % and in 32 tap with 16 bit combination, it decreases by 7.60 %.When compared with 32 tap with 4 bit word length, the proposed adder with LUT multiplier in the highlighted combinations, the Fmax is increased by 19.28 % and in 32 tap with 16 bit, it increases by 29.74 %. The Low-pass RNS FIR filter is designed for a cut-off frequency of 50 Hz and generated filter coefficients in MATLAB and implemented to denoising the ECG signal.

How long is long? Word length effects in reading correspond to minimal graphemic units: An MEG study in Bangla.

This paper presents a magnetoencephalography (MEG) study on reading in Bangla, an east Indo-Aryan language predominantly written in an abugida script. The study aims to uncover how visual stimuli are processed and mapped onto abstract linguistic representations in the brain. Specifically, we investigate the neural responses that correspond to word length in Bangla, a language with a unique orthography that introduces multiple ways to measure word length. Our results show that MEG signals localised in the anterior left fusiform gyrus, at around 130ms, are highly correlated with word length when measured in terms of the number of minimal graphemic units in the word rather than independent graphemic units (akśar) or phonemes. Our findings suggest that minimal graphemic units could serve as a suitable metric for measuring word length in non-alphabetic orthographies such as Bangla.

Application of Corpus Analysis Software and Error Analysis Theory on Non-English Majors’ Chinese-English Translation Features

This research focuses on non-English major students’ Chinese-English (C-E) translation features. It aims at annotating and summarizing the features and error types in students’ C-E translation by applying to corpus analysis software Readability Analyzer. This study first collects students’ C-E translation texts from a computer-based exam, then forms a self-constructed corpus and a reference corpus. The translation materials in the target corpus and the reference corpus are first analyzed and compared by Readability Analyzer to figure out the features of students C-E translation texts from the perspectives of sentence length, word length, percentage of difficult words, Flesch Reading Ease, Dale-Chall Score, Fry Readability Grade Level. After that, different types of errors in students C-E translation are labeled and analyzed based on statistical analysis and Error Analysis (EA) theory. According to Readability Analyzer, students C-E translation texts have the following features. From passage statistics, it shows that translation texts that get lower scores tend to use shorter sentences and easier words. In addition, from readability scores, Flesch scores, Dale-Chall Scores shows and Fry Readability Grade Level are taken into consideration. Flesch scores shows that compared with the reference corpus, students’ translation which get lower scores are harder to understand since there are more language errors than higher-score translation. Dale-Chall Scores indicate that translation materials which score 8-9 are very close in the difficulty of word-using with the reference corpus. Fry Readability Grade Level shows that texts of lower scores are simpler and more accurate than texts of higher scores. According to Error Analysis (EA) Theory, students’ errors are further classified and discussed on lexical level, sentence level and textual level. The conclusions of this research are expected to make contributions to the improvement of C-E translation teaching and help college students, especially non-English majors, to enhance their C-E translation competence.

Effectiveness of Automated Formative Feedback in an Online Tutorial for Promoting Summarizing

We conducted a study with the aim to investigate the effectiveness of automated formative feedback in improving students’ ability to summarize. One-hundred and thirty-eight undergraduate students in an elementary education program were asked to summarize six scientific texts, with the experimental group (N=87) receiving automated formative feedback in a computer-based learning environment (FALB). FALB provides automated feedback about content coverage, copying words avoidance, redundancy avoidance, relevance, and length. Comparing the experimental group to a control group (N=51), results implied that summarizing skills could be fostered when interacting with FALB. In particular, the automated formative feedback promoted the adherence to the predefined length and the avoidance of copying words while maintaining a high content coverage, fostering cognitive processes essential for constructing a mental model of a text. In addition, students in the experimental group were able to maintain high quality summaries in their final session when not scaffolded. In conclusion, FALB supports the alignment of internal standards with external standards and provides an incentive to revise and engage with texts.

Software for assessment and monitoring the decoding skills development of children from the elementary school: validity based on response process.

To continue the validation process of the Decoding Development Monitoring Protocol (PRADE) in software format in the validity evidence stage based on response processes. 250 individuals participated in this study, 125 individuals from private schools and 125 individuals from public schools. The assessment was carried out in person using the software that hosts the instrument's tasks, which are organized into decoding linguistically balanced words and non-words, respecting the decoding rules of Brazilian Portuguese. The software prepares an individual performance report for each participant, counting the decoding time for each stimulus, as well as the number of words decoded correctly. The data is organized considering the correct decoding time of the stimuli, decoding accuracy and percentage of correct answers. All data underwent statistical analysis using SPSS software. The data indicated an important effect of the length of words and non-words on public and private school students. Furthermore, it was possible to observe the evolution of decoding, depending on the school year, in all the variables studied. In both groups, a strong influence of non-words on student performance throughout Elementary School I was observed. The data indicate validity in the analysis of response processes, since it was possible to adequately characterize the performance of school children public and private throughout Elementary School I, characterizing each group, as well as their differences according to the advancement of schooling.

Exploring English Translation Strategies Oriented by Big Data Technology

Abstract English translation is not a simple task, and it needs to be translated on the basis of different languages and cultures in order to get the ideal translation effect and promote cross-cultural communication between different languages. In this paper, with the help of web crawler technology to crawl the Chinese-English translation corpus data, and use the inverse document frequency and dynamic planning ideas to align the corpus data, the Chinese-English translation corpus is established. The LDA model is used to mine subject information in the Chinese-English translation corpus, and edge distribution estimation is used to construct the English translation model. For the application of the translation model and the Chinese-English translation corpus in improving the quality and effect of English translation, the word and sentence features, target word blocks, and the comparison of language features of human-computer translation from the Chinese-English corpus are verified in various aspects. The results show that the class/formant ratio of the source language in the corpus is between 56.73% and 75.84%, its average word length fluctuates in the range of [3.91,5.62], and the target word chunks of the English text are mainly based on nominal and verbal structures. The English translation model has a less significant discrepancy in other language features than human translation, except for the difference of 4.526 between sentence lengths. English translation needs to adopt a reasonable annotation method, semantic conversion, and technical tools to improve the quality of English translation.

A Textual Logical Analysis of the Evolution of Marx’s Urban Critical Thought

Abstract Focusing on elucidating Marx’s urban critical thought is of positive significance for understanding China’s urbanization path and shaping the socialist urban development model with Chinese characteristics in the new era. The main point of Marx’s urban critical thought is the starting point of this paper, and it examines the manifestation and evolution logic of Marx’s urban critical thought. For the textual logic of Marx’s urban critical thought, the Word2vec model is utilized to extract textual features. Then, the BERT model is introduced for text word vector construction, and a multiclassification model of Marx’s urban critical thought text is established based on the fusion of multiple semantic information features. After completing the construction of the method, relevant texts about Marx’s urban critical thought were collected to construct a dataset, and the classification effect was analyzed to explore the evolutionary development trend of Marx’s urban critical thought. The results show that the text classification model can achieve the optimal classification accuracy of 93.15% by applying the 15th layer. The optimal performance is obtained when the local text word length is 100, and the sustainability growth of Marx’s urban critical thought between 1950 and 2020 reaches 102.34%. An in-depth analysis of Marx’s urban critical thought evolutionary logic can provide theoretical guidance for the modernization of Chinese cities.

A Comprehensive Analysis of Text Value and Linguistic Characteristics of Chinese Language Literature Based on Text Mining Technology

Abstract In this paper, webpage information is extracted by the directed crawling method in data crawling technology so as to obtain a collection of Chinese language literature, which is processed by data cleaning, Chinese word splitting, and de-duplication. Text mining techniques such as machine learning, the LDA model, and semantic networks are used to perform operations such as sentiment analysis, theme extraction, and linguistic association analysis on the acquired text. Based on the mined text, text value assessment and linguistic characterization of literary works written in Chinese are carried out. The text value is quantified and graded using the PMC index model, and the linguistic features of the text, including punctuation, vocabulary, and sentences, are analyzed by constructing a linguistic feature model. Eight of the 10 Chinese literary works selected in this paper, including Alive, have text values at an excellent level. The frequently used punctuation mark in works is the period. The average word length is around 2.75, and the degree of discreteness of sentences is small.

Word length dependent sensitivity penalty in high speed VCSEL based optical interconnects

Word length dependent sensitivity penalty in high speed VCSEL based optical interconnects

Text Value and Linguistic Characterization in Chinese Language Literature Based on Text Mining Techniques

Abstract This study applies text mining techniques to deeply analyze Chinese language and literature’s text value and linguistic features. The study adopts the methods of textual disambiguation, vector space modeling, semantic network and Labeled-LDA model. Taking the novels of Yu Hua and Ge Fei as an example, it reveals the differences between the two writers in linguistic features such as using punctuation, average word length, and sentence discrete degree. The study provides a comprehensive heat score for the novels based on three dimensions: reading base group, reading gain, and reading discussion. The results show that the frequency of period use in Yu Hua’s works is decentralized, while Ge Fei’s works are more concentrated. Ge Fei’s average word length is slightly higher, showing a tendency to use multi-syllabic words. The novel popularity and heat scores conform to a power law distribution, reflecting the Pareto rule that 80% of the popularity is concentrated on 20% of the hot novels. This study provides a new perspective on Chinese language and literature through the application of text mining technology, and its methods and tools can effectively enhance the effectiveness and efficiency of teaching.

Stylistic Analysis of Chinese Language Literature Based on Text Mining Techniques

Abstract Chinese literary style highlights the distinctive features of different Chinese language literary works, which is of enormous significance to the research in the field of Chinese language literature. In this paper, we use web crawler technology to construct a dataset of 183 Chinese-language literary works obtained from online reading websites, and we use statistical-based segmentation methods, de-duplication, and other methods to preprocess the text data. The conditional cooccurrence matrix represents the acquired texts, and the BRET-AE model extracts text features. Appropriate classifiers are selected for different literary style analysis tasks. This paper selects seven literary works by Mo Yan and Jia Pingwa for empirical analysis of the Chinese language literary style. While Jia Pingwa’s word formation rate is higher than that of Mo Yan’s. Are selected for empirical analysis of Chinese language literary style. The average word length of all of Mo Yan’s works is 1.5115, and the average word length of Jia Pingwa’s 7 works is 1.3995. In addition, the average sentence length of Mo Yan’s works exceeds that of Jia Pingwa’s works, while the word formation rate of Jia Pingwa’s works is higher than that of Mo Yan’s works. Eventually, the clustering degree analysis reveals that Mo Yan’s Red Red Sorghum Family has the lowest clustering degree, while Wine Country has the highest clustering degree, highlighting the literary style transformation of Mo Yan’s works.

Reading Easy Language texts written by public authorities

Previous research has shown that word length, frequency and word repetition influence word reading times (Rayner 1998; 2009). Guidelines for Easy Language advise writers to use frequent and short words, and to repeat words instead of using synonyms. However, some of these guidelines are based on research that has been misinterpreted, simplified, or is outdated (Wengelin 2015), and studies focusing on effects of word length, frequency and word repetition among adult readers in the Easy Swedish target group are lacking. This eye-tracking study investigated the reading of Easy Language texts written by public authorities, as well as the effects of word length, frequency, and word repetition on readers in a day centre for people with intellectual disabilities. The results showed significant effects for word length and frequency in all readers. In addition, the effects were significantly greater in the target group than in the control group. The effects for word repetition were not as clear, affecting only one of the reading measures. Furthermore, the study revealed poor comprehension rates in the target group, i.e., when asked, they were not able to reproduce the main contents of the texts. The significantly greater effects of word length and frequency suggest that the related Easy Language guidelines are valid for this group of readers. The poor comprehension rates indicate that the texts were too difficult for these readers.

The distributional properties of prefixes influence lexical decision latencies

Abstract Morphological processing has been extensively studied in English and European languages, but there is a growing interest in extending the research to other languages. Here we examined Malay, an Austronesian language that is morphologically rich. We investigated the effects of morphological constituents on lexical decisions for prefixed words. Specifically, we explored whether readers are sensitive to any distributional properties of the prefix and root morphemes. Variables investigated included length and family size for both prefixes and roots, as well as number of allomorphs, consistency, and productivity for prefixes. Decision latencies were collected for 1,280 Malay words of various morphological structures. Data from the 640 prefixed words were analyzed in a series of GAMM models. We observed a facilitative effect of root family size and an effect of several distributional properties of prefixes on decision latencies after accounting for word frequency and length. Furthermore, a larger interaction between frequency and several distributional properties of prefixes was found for words with three-letter prefixes than for those with two-letter prefixes. These findings provide insight into the types of distributional properties to which Malay readers are sensitive in multimorphemic words.

Investigation of the Change in the Features of Turkish Items in the High School Entrance System 2018-2022

aim of this study is to examine in detail the structural features of the Turkish items in the High School Entrance System (LGS), which was first implemented in 2018, and to determine the changes in these features over the years. In this descriptive survey study, the changes in test difficulty, discrimination, and internal consistency of a total of 100 Turkish items in the LGS administered in 2018-2022 were examined first. Then, the textual (sentence, word, and syllable length; readability), contextual (visuality, meaning of the item stem, common stem) and purpose features (content domain, text type) of the items were determined and the changes in these features over the years were examined. As a result of the study, it was determined that the test difficulty of the LGS Turkish test decreased over the years; in other words, the items became more difficult over time. Although there was no general change in the test discrimination of the test, it was determined that there was a general decrease in the test discrimination of the test. In the study, it was also determined that the item lengths varied over the years and the average length of the test increased over the years. The texts in the items were mostly composed of informative texts; poetry and narrative texts were not included in the items in some years.

Big data mining and comparative analyses across lexica on the relationship between syllable complexity and word stress

For about four decades, phonological theories have claimed that word stress assignment depends on the word’s syllabic structure complexity in relation to syllabic position. This study analyzes the syllabic structure implications for word stress in three languages with weight-sensitive lexical stress, namely Brazilian Portuguese, British English, and American English. After creating three corpora and applying Random Forest modeling, syllabic structure distributions for word stress were found to be bound to stress pattern and word length in number of syllables. To account for these observations, models of word naming must be extended with aspects of word stress.

High Performance and Scalable Hybrid Memristor-CMOS Based Full Adder

The field of digital computation has recently drawn attention to the memristor as a circuit element due to its nanoscale structure and integration capabilities with MOSFETs. As a result, there have been various attempts to use hybrid memristor-CMOS circuits to rebuild the fundamental digital building blocks. This paper presents a new hybrid memristor-CMOS-based full adder (HMC-FA) design. This design aims to minimize propagation delay, power dissipation, power delay product (PDP), and device count (memristor and transistor count). The proposed design is implemented in Cadence Virtuoso toolset utilizing CMOS gpdk 180nm process technology parameters. Comparative investigation demonstrates that the proposed HMC-FA improves PDP by 71.95%−99.83%. The proposed circuit is designed with 12 transistors and 6 memristors. The proposed design consumes 8.24µW of total power while the propagation delay is 112.71ps at 1.8V supply voltage. To verify the scalability of the HMC-FA cell, full adders up to 32-bit word length are designed, and performance parameters are evaluated at 1.8V, 100MHz supply voltage.