Word List Research Articles

An Analysis of Affixation Found in Sasak Language in Kelayu Dialect: A Descriptive Study

In the development of language, there are many variations of language in society, this is because language is changing continuously, and it is changing in different places. One aspect of language that is interesting to research is affixation. Affixation is a morphological process in linguistics where affixes are added to the root of a word to form a new word. The study aimed at describing prefixes and suffixes that are exist in Kelayu dialect. This is research was descriptive qualitative research. It concerned in the uses of affixation found in Sasak language used by Kelayu villagers. There were three domains chosen. The first was family, second was friendship and third was neighborhood. The data were collected based on five instruments, namely: observation, recording, documentation study/documentation, word list, and note taking. The result of the study showed that there are 7 kinds of prefixes and 4 kinds of suffixes that are exist in Kelayu dialect. The prefixes are {be-}, {ke-}, {me-}, {ng-}, {nge-}, {pe-}, and {te-}. The suffixes were found are {-an}, {-ang}, {-in}, and {-ye}. There was also no indication of infix, it happened because there are no words influenced by infix.

Correlation Between News and Stock Price Based on Stock Market Indices: Can News Classification Be Used as a Tool to Make Better Decisions?

The stock market is influenced by various factors, including news events, economic indicators, and investor sentiment. Understanding the correlation between news and stock price movements interests market participants and researchers. In this paper, we explore the relationship between news sentiment and stock market trends using stock market indices. We employ natural language processing (NLP) techniques to classify news articles and analyze their impact on stock market indices, focusing on the S&P 500 and the Dow Jones Industrial Average. We utilize sentiment analysis and machine learning algorithms, including Random Forest, Loughran-McDonald (2014) Financial Sentiment Word Lists (Extended), and AFINN Lexicon, to predict stock market trends based on news sentiment. Our findings demonstrate that positive news sentiment has a more significant impact on stock prices than negative sentiment. The Random Forest model achieves the highest accuracy, while domain-specific lexicons provide valuable insights into financial news sentiment. However, predicting negative trends remains a challenge across all methods. Our research contributes to the growing knowledge of the relationship between news and stock prices and provides valuable insights for market participants and researchers.

Chinese Emotional Speech Audiometry Project (CESAP): Establishment and Validation of a New Material Set With Emotionally Neutral Disyllabic Words.

The Chinese Emotional Speech Audiometry Project (CESAP) aims to establish a new material set for Chinese speech audiometry tests, which can be used in both neutral and emotional prosody settings. As the first endeavor of CESAP, this study demonstrates the development of the material foundation and reports its validation in neutral prosody. In the development step, 40 phonetically balanced word lists consisting of 30 Chinese disyllabic words with neutral valence were first generated. In a following affective rating experiment, 35 word lists were qualified for validation based on the familiarity and valence ratings from 30 normal-hearing (NH) participants. For validation, performance-intensity functions of each word list were fitted with responses from 60 NH subjects under six presentation levels (-1, 3, 5, 7, 11, and 20 dB HL). The final material set was determined by the intelligibility scores at each decibel level and the mean slopes. First, 35 lists satisfied the criteria of phonetic balance, limited repetitions, high familiarity, and neutral valence and were selected for validation. Second, 15 lists were compiled in the final material set based on the pairwise differences in intelligibility scores and the fitted 20%-80% slopes. The established material set had high reliability and validity and was sensitive to detect intelligibility changes (50% slope: 6.20%/dB; 20%-80% slope: 5.45%/dB), with small covariance of variation for thresholds (15%), 50% slope (12%), and 20%-80% slope (12%). Our final material set of 15 word lists takes the initiative to control the emotional aspect of audiometry tests, which enriches available Mandarin speech recognition materials and warrants future assessments in emotional prosody among populations with hearing impairments. https://doi.org/10.23641/asha.25742814.

Verbal learning in frontal patients: area 9 is critical for employing semantic strategies.

Learning is a long-term memory process heavily influenced by the control processes implemented by working memory, including recognition of semantic properties of items by which subjects generate a semantic structure of engrams. The aim of this study is to investigate the verbal learning strategies of patients affected by a tumor in the left frontal lobe to highlight the role of area 9. Ten patients with frontal low-grade gliomas and ten healthy control subjects, matched for age, sex and education, were recruited and then evaluated with a two-part verbal learning test: multi-trial word list learning in free recall, and multi-trial word list learning preceded by an explicit semantic strategy cue. Frontal patients were divided into two groups: those either with frontal lesions involving or sparing area 9. In comparison to healthy control subjects, frontal patients with lesions involving area 9 memorized fewer words and displayed difficulty in using semantic strategies. When the strategy was suggested by the examiner, their performance improved, but to a lesser extent than the healthy control. Conversely, frontal patients with lesions sparing area 9 showed similar results to healthy control subjects. The results suggested that, while the identification of the categorical criterion requires the integrity of the entire dorsolateral prefrontal area, only area 9, and not the surrounding areas, could be responsible for the effective use of semantic strategies in learning tasks.

Prediction and mediation analysis for treatment responses to combined cognitive and physical training for older adults

Diminished cognitive and physical functions negatively affect the daily functions of individuals. Although combined cognitive and physical training prevents instrumental activities of daily living (IADL) disability in older adults, no predictive model or mediation analysis of IADL after combined training exists. This study aims to employ prediction and mediation analysis to identify the predictors of IADL performance and to elucidate the mediators of the association between baseline global cognition and subsequent IADL performance following combined cognitive and physical training. This study involved 177 participants aged 60 years and older who underwent combined training. Cognitive function was measured with the Montreal Cognitive Assessment (MoCA), Digit Symbol Substitution Test (DSST), Color Trails Test, Word List, and a dual task; physical function with the Timed Up and Go (TUG) test; daily function with the Lawton IADL Scale. We conducted regression analyses to identify the predictors of IADL performance, and mediation analysis to examine whether DSST and TUG mediate the relationship between MoCA and IADL. The pre-intervention DSST and TUG were significant independent predictors of post-intervention IADL. The association between the pre-intervention MoCA and post-intervention IADL was mediated by pre-intervention DSST and TUG. This study highlighted the importance of measuring and improving processing speed and functional mobility before and during interventions to enhance IADL outcomes.Trial registration: NCT03619577, 23/07/2018; NCT04689776, 29/12/2020.

We have a lot in common: Cognate words

AbstractThis study presents a list of 2022 Bulgarian‐Turkish cognate words that can be used to facilitate vocabulary acquisition and cross‐linguistic awareness. The list was created manually through a multi‐step process that involved scanning the database of an online dictionary to identify Bulgarian words that had the same or similar forms in Turkish. For each cognate word identified, its Bulgarian and Turkish forms and English meanings were recorded, and their accuracy was checked through additional sources and expert consultation. The final list of cognate words includes only those confirmed based on linguistic criteria and expert judgement. The use of cognate words in language teaching methods such as total physical response and teaching proficiency through reading and storytelling can enhance the effectiveness of these methods by helping students understand and remember new vocabulary and building their confidence in language learning. Additionally, this study highlights the potential of cognate awareness to promote peace in the world by emphasizing the commonalities among languages and cultures. By recognizing the abundance of cognates across different languages, we can reinforce the idea that we are all part of the same human family and share a common linguistic heritage. Moreover, the study demonstrates how a new and innovative multilingual education can be achieved through cognates, showing that language education does not necessarily have to be limited to bilingual instruction. The use of cognates as a tool for vocabulary acquisition and cross‐linguistic awareness can open up opportunities for multilingualism and facilitate communication across linguistic and cultural boundaries. Future research could explore the use of cognate words in other language teaching contexts and investigate the potential benefits and limitations of using cognates in language learning.

Occupational groups prediction in Turkish Twitter data by using machine learning algorithms with multinomial approach

A lot of research has been done on personality and sentiment analysis, demographic and professional aspects using user shares in social networks. In particular, information extraction and value are produced based on Twitter data. This study aims to predict the users, occupational groups, who share in Turkish on Twitter, using machine learning methods. First, occupational groups and the Twitter accounts of the occupations in these occupational groups were determined manually and the tweets shared in these accounts were scraped. All tweets were then grouped by occupation into groups of one, five and ten, creating datasets with different characteristics, each containing more than 500,000 tweets. Some datasets were preprocessed using the Zemberek library, which is used in many Turkish NLP studies, and experiments were conducted out with a total 6 datasets. During the preprocessing phase, since the ready-made stopwords lists were not considered sufficient, unnecessary word lists consisting of single and binary words were created manually. Count and TF-IDF vectorizers are used to convert textual data into numerical. Since each word represents a variable in the text classification study, new variables were created by combining double and triple word phrases (ngrams) with feature extraction. In the experiments in which 24 different models were run, instead of using all the features created, the method of “determining the optimal number of features”, which consists of the most valuable features, was used. It was found that the most successful model in the experiments using machine learning algorithms with a multinomial approach achieved 97.3% success in all calculated metrics.

From vector spaces to DRM lists: False Memory Generator, a software for automated generation of lists of stimuli inducing false memories

The formation of false memories is one of the most widely studied topics in cognitive psychology. The Deese–Roediger–McDermott (DRM) paradigm is a powerful tool for investigating false memories and revealing the cognitive mechanisms subserving their formation. In this task, participants first memorize a list of words (encoding phase) and next have to indicate whether words presented in a new list were part of the initially memorized one (recognition phase). By employing DRM lists optimized to investigate semantic effects, previous studies highlighted a crucial role of semantic processes in false memory generation, showing that new words semantically related to the studied ones tend to be more erroneously recognized (compared to new words less semantically related). Despite the strengths of the DRM task, this paradigm faces a major limitation in list construction due to its reliance on human-based association norms, posing both practical and theoretical concerns. To address these issues, we developed the False Memory Generator (FMG), an automated and data-driven tool for generating DRM lists, which exploits similarity relationships between items populating a vector space. Here, we present FMG and demonstrate the validity of the lists generated in successfully replicating well-known semantic effects on false memory production. FMG potentially has broad applications by allowing for testing false memory production in domains that go well beyond the current possibilities, as it can be in principle applied to any vector space encoding properties related to word referents (e.g., lexical, orthographic, phonological, sensory, affective, etc.) or other type of stimuli (e.g., images, sounds, etc.).

Abstract PO2-19-01: A Single-Arm Prospective Study of the Neurocognitive Changes Occurring in Breast Cancer Patients on Aromatase Inhibitors. Is There a Difference Between Objective and Perceived Neurocognition?

Abstract Background: Estrogen (ER) and Progesterone (PR) receptors are rich in regions of the brain involved in cognitive functions, like the hippocampus and cerebral cortex. Animal models have shown that estradiol regulates neurocognition and plays an important role in the normal brain development. Hormone positive (HR+) breast cancers (BC) comprise 70% of the disease and the standard of care in the post-menopausal group is the use of Aromatase Inhibitors (AI) for at least 5 years. While “chemo brain” is a well-recognized term, neurocognitive changes from estrogen inhibition by AI have not been clearly defined. As neurocognition is multi-faceted, information on specific aspects affected by AI use is unclear. We hope to answer this through our trial. Design: The study is a single arm, self-controlled prospective observational cohort study in which, post-menopausal BC patients who are to be started on AIs will undergo a battery of neurocognitive tests just before starting AI, at 6 months and at 12 months after being on AI. The changes at 6 and 12 months will be compared to baseline. The tests will involve objective [Hopkins verbal learning test revised (HVLT), Controlled Oral Word Association Test from Multilingual Aphasia Examination, Trial Making Test A and B, and Recall and Recognition of Word List encoded from the HVLT] and subjective/patient-reported [The Functional Assessment of Cancer Therapy - Cognitive Function (FACT-Cog)] components. Tests for anxiety [Generalized Anxiety Disorder (GAD)–7], depression [Patient Health Questionnaire (PHQ)-9] and working memory (Working Memory Tasks from the Wechsler Adult Intelligence Scale) will also be assessed. While the objective tests involve providing patients with specific instructions and asking them to complete the tasks, the FACT Cog questionnaire uses a self-reported 37-item questionnaire enabling assessment of their perceived symptoms. The tests have been summarized in the table. Eligibility criteria: ER/PR+ post-menopausal BC and DCIS patients who are English speaking and are planning to start AIs will be included. Patients who have received hormonal therapy or chemotherapy in the past, those who are scheduled to receive chemotherapy, those who have undergone Whole brain Radiation therapy (RT), those with a premenopausal status, and those having comorbid conditions that could affect cognitive functioning (like any form of dementia or brain metastasis) will be excluded. Specific aims: Our aim is to see if there are progressive neurocognitive changes compared to an age-appropriate baseline and to see if changes to both objective and perceived cognitive changes occur over the course of AI use, in post-menopausal HR+ BC patients. Statistical methods: The T-test and McNemer test will be used. The normal values provided from the test may be used as a standard to justify normal or abnormal values from the collected data. Therefore, a percentage of normal (or abnormal) subjects in each time point can be derived and compared using the McNemar's test. Present accrual and target accrual: 4 patients have been accrued as of June 2023 with a target accrual of 80 patients [80% power to detect a significant change, at a p of 0.05]. Contact information for people with a specific interest in the trial: Dr Abirami Sivapiragasam (abisiva2019@yahoo.com), Dr Sam Benjamin (benjamis@upstate.edu), Dr Prashanth Ashok Kumar (ashokkup@upstate.edu), Amy Bubb (bubbam@upstate.edu). Summary of the neurocognitive tests employed in the trial. Citation Format: Prashanth Ashok Kumar, Erinn McDowell, Amy Bubb, Danning Huang, Susan Sperry, Sam Benjamin, Abirami Sivapiragasam. A Single-Arm Prospective Study of the Neurocognitive Changes Occurring in Breast Cancer Patients on Aromatase Inhibitors. Is There a Difference Between Objective and Perceived Neurocognition? [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-19-01.

Creating and validating a corpus-based English academic word list for physics

Abstract In this study a Physics Research Articles Word List (PRAWL) is built, which includes the most extensively used English vocabulary in the genre of physics research articles outside the most frequent general-purpose vocabulary. PRAWL can be of great use in English for Physics teaching and learning. This discipline-specific academic word list was produced from a corpus of 1.25 million running words, consisting of 200 physics research articles. PRAWL contains 929 words and has a 17.48% coverage in the corpus it was derived from. Together with 2,000 high-frequency general-purpose words, as well as words bearing no or minimal learning load, PRAWL reaches a coverage close to 95% in this corpus. We validate PRAWL in three independent corpora and also discuss the pedagogical implications and recommendations regarding its use. The list is intended for students of physics who do not have a command of English which is sufficiently adequate for reading research articles in it without outside support.

Detection of Language Lateralization Using Spectral Analysis of EEG.

Language lateralization relies on expensive equipment and can be difficult to tolerate. We assessed if lateralized brain responses to a language task can be detected with spectral analysis of electroencephalography (EEG). Twenty right-handed, neurotypical adults (28 ± 10 years; five males) performed a verb generation task and two control tasks (word listening and repetition). We measured changes in EEG activity elicited by tasks (the event-related spectral perturbation [ERSP]) in the theta, alpha, beta, and gamma frequency bands in two language (superior temporal and inferior frontal [ST and IF]) and one control (occipital [Occ]) region bilaterally. We tested whether language tasks elicited (1) changes in spectral power from baseline (significant ERSP) at any region or (2) asymmetric ERSPs between matched left and right regions. Left IF beta power (-0.37±0.53, t = -3.12, P = 0.006) and gamma power in all regions decreased during verb generation. Asymmetric ERSPs (right > left) occurred between the (1) IF regions in the beta band (right vs. left difference of 0.23±0.37, t(19) = -2.80, P = 0.0114) and (2) ST regions in the alpha band (right vs. left difference of 0.48±0.63, t(19) = -3.36, P = 0.003). No changes from baseline or hemispheric asymmetries were noted in language regions during control tasks. On the individual level, 16 (80%) participants showed decreased left IF beta power from baseline, and 16 showed ST alpha asymmetry. Eighteen participants (90%) showed one of these two findings. Spectral EEG analysis detects lateralized responses during language tasks in frontal and temporal regions. Spectral EEG analysis could be developed into a readily available language lateralization modality.

A need analysis of the high-frequency words (HFW) dictionary for the Indonesian language for foreigners (ILF)-1 learning

This is a preliminary study of research development for creating a dictionary of high-frequency words (HFW). Thus, this study tries to analyze the need of the ILF learners as the users of a dictionary of HFW for their ILF learning. A list of high-frequency words should be used in ILF learning so that the learning objectives can be achieved effectively. Unfortunately, the word list is not yet available. The ILF learners, especially those at the beginner level, still have difficulty using the Great Dictionary of the Indonesian Language (KBBI). They prefer using translation apps to obtain the meaning of a word, even though these apps do not always help. Employing a descriptive survey, this study involved a total of 183 ILF teachers and learners. The data were gathered through Google-form questionnaires. In addition, interviews were administered with selected ILF teachers who had a teaching experience of more than 15 years. The findings revealed that the teachers and learners had different beliefs on the vocabulary forms needed for making a dictionary of HFW. The learners still did not understand the concepts of high-frequency words. According to the teachers, HFW was more needed for ILF learning. Meanwhile, for the learners, general words were more needed. Besides, digital dictionaries were more in demand than printed dictionaries. Bilingual dictionaries were more needed than monolingual dictionaries. Both the teachers and learners believed that the dictionary of HFW should comprise sentence examples. They both agreed that the dictionary of HFW should provide information about the use of words in sentence examples, affixes, synonymous phrases, antonyms, word classes, and phonetics. Therefore, the results of this research analysis can be used as a basis for developing a dictionary that fits the needs of ILF-1 learners.

Enhancing Science Vocabulary and Content Knowledge of Thai EFL students through Content and Language Integrated Learning (CLIL) and English Science Textbooks Word Lists (ESTWL)

The development of subject-specific word lists to assist EFL learners is becoming popular, as is the utilization of CLIL to teach English in academic areas of science. The purpose of this research was to investigate the effects of the instruction of English Science Textbooks Word Lists (ESTWL) and the CLIL approach on the Science Vocabulary Knowledge and Science Content Knowledge of Thai EFL students. A mixed-methods research design was employed to gather both quantitative and qualitative data using ESTWL-CLIL lesson plans, a Science Vocabulary Knowledge test, and a Science Content Knowledge test. All research instruments were validated by both content and language experts. The target groups were 3 levels of secondary school students studying at a large secondary school in Bangkok. The research findings were as follows: 1) the mean scores of the students in the three groups increased by a statistical significance of 0.05 for both vocabulary knowledge and science content knowledge after the implementation of the intervention, and 2) the qualitative results indicated that all students perceived the instruction of ESTWL and the CLIL approach to be positive. The study suggested a vocabulary pedagogy for science and language teachers for use in CLIL environments.

Applied Machine Learning Predictive Analytics to SQL Injection Attack Detection and Prevention

Abstract: The back-end database is pivotal to the storage of the massive size of big data Internet exchanges stemming from cloud-hosted web applications to Internet of Things (IoT) smart devices. Structured Query Language (SQL) Injection Attack (SQLIA) remains an intruder’s exploit of choice on vulnerable web applications to pilfer confidential data from the database with potentially damaging consequences. The existing solutions of mostly signature approaches were all before the recent challenges of big data mining and at such lacks the functionality and ability to cope with new signatures concealed in web requests. An alternative Machine Learning (ML) predictive analytics provides a functional and scalable mining to big data in detection and prevention of SQLIA. Unfortunately, lack of availability of readymade robust corpus or data set with patterns and historical data items to train a classifier are issues well known in SQLIA research. In this project, we explore the generation of data set containing extraction from known attack patterns including SQL tokens and symbols present at injection points. Also, as a test case, we build a web application that expects dictionary word list as vector variables to demonstrate massive quantities of learning data. The data set is pre-processed, labelled and feature hashing for supervised learning. The trained classifier to be deployed as a web service that is consumed in a custom dot NET application implementing a web proxy Application Programming Interface (API) to intercept and accurately predict SQLIA in web requests thereby preventing malicious web requests from reaching the protected back-end database. This project demonstrates a full proof of concept implementation of an ML predictive analytics and deployment of resultant web service that accurately predicts and prevents SQLIA with empirical evaluations presented in Confusion Matrix (CM) and Receiver Operating Curve (ROC).

Exploring Kawayan Dialect Lexicon (Wordlist) in Southern Negros, Philippines

The extent of lexical research has resulted in the development of word lists, which have to rely on consideration of code-glosses and their alternative words to understand their meaning and the status of these forms. This research tries to describe the Kawayan dialect lexicon from a list of 278 words that are more easily traced to the Bisayan family and appear to be Western Bisayan by utilizing and manipulating an Excel Pivot Table to trace timestamps to extract words from informants (native speakers). The research was submitted for cross-checking between coders to be reviewed and checked for validity and reliability as a protocol for verifying the accuracy of data collection from native speakers. This word list is essential in providing information about the genetic relationship of the Kawayan dialect to the Filipino language group. Additionally, digging deeper into how this research is presented and disseminated to its intended readers and scholars is productive and eye-opening. Since this is a pioneering investigation and documentation of the Kawayan dialect, although it may be annoying, at least it started as one of the distinctive features of other dialects yet to be included in the list of Filipino dialects. Researchers today urge us to explore and investigate this dialect before it disappears.

Evaluating Hearing Status and Word Recognition Ability in the Hmong Population Using Four Validated Monosyllabic White Hmong Dialect Word Recognition Tests.

The study's aims were (a) to evaluate hearing status and (b) word recognition ability of Hmong speakers using four validated monosyllabic word recognition tests in the White Hmong dialect and (c) to assess the relationship between the participant's language and the average word recognition percent correct scores, adjusting for age, gender, and degree of hearing loss. Participants listened to two randomly assigned validated Hmong word lists (male/female talker) for each ear. Pure-tone air- and bone-conduction thresholds as well as word recognition ability were measured. Descriptive statistics were calculated to analyze the percent correct of word lists and classify hearing status. A nonparametric regression analysis was used to assess the relationship between the participant's language and the average word recognition percent correct scores, adjusting for age, gender, and degree of hearing loss. Forty-eight Hmong (25 females, 23 males; Mage = 44.4) participated in this study. Thirty-three participants had hearing loss in at least one ear, and 15 had hearing within normal limits bilaterally. Participants with normal, mild, or steeply sloping hearing loss reached an average word recognition score of > 94% on Hmong lists by both male and female talkers. Participants with moderate-to-severe hearing loss scored 68% on average for the male talker Hmong lists and 60% on average for the female talker Hmong lists. Gender was significantly positively associated with average word recognition percent correct on the female word lists (b = -0.224, p = .047) but not statistically significant for the male word lists (b = 7.579, p = .141). Findings provide support for the use of the four Hmong word lists in clinical settings.

Peningkatan Kemampuan Siswa dalam Membedakan Bahasa Baku dan Bahasa Tidak Baku Berdasarkan EYD

The ability to differentiate standard language from non-standard language is an important aspect in learning Indonesian. This research aims to examine the effectiveness of using Enhanced Spelling (EYD) in improving students' ability to differentiate between standard and non-standard language. The method used is a literature study by examining in depth the EYD guidelines published by the Language Development and Development Agency, as well as analyzing scientific journals related to the application of EYD in learning. The results of the study show that the EYD guidelines provide a list of standard and non-standard words arranged alphabetically as well as clear and systematic rules for writing standard language. Several previous studies revealed that intensive use of word lists and rules can increase students' mastery of standard vocabulary and understanding of the differences between standard and non-standard language. However, the obstacle in implementing EYD in the field is the teacher's lack of understanding of the existing rules. Therefore, intensive training and socialization is needed for teachers so that the use of EYD can be optimal in improving students' ability to differentiate between standard and non-standard languages. This research concludes that the EYD guide is a very effective source for improving these abilities by providing a clear list of words and standard language rules as a guide for teachers and students.

A machine-based corpus optimization method for extracting domain-oriented technical words: an example of COVID-19 corpus data

Although natural language processing (NLP) refers to a process involving the development of algorithms or computational models that empower machines to understand, interpret, and generate human language, machines are still unable to fully grasp the meanings behind words. Specifically, they cannot assist humans in categorizing words with general or technical purposes without predefined standards or baselines. Empirically, prior researches have relied on inefficient manual tasks to exclude these words when extracting technical words (i.e., terminology or terms used within a specific field or domain of expertise) for obtaining domain information from the target corpus. Therefore, to enhance the efficiency of extracting domain-oriented technical words in corpus analysis, this paper proposes a machine-based corpus optimization method that compiles an advanced general-purpose word list (AGWL) to serve as the exclusion baseline for the machine to extract domain-oriented technical words. To validate the proposed method, this paper utilizes 52 COVID-19 research articles as the target corpus and an empirical example. After compared to traditional methods, the proposed method offers significant contributions: (1) it can automatically eliminate the most common function words in corpus data; (2) through a machine-driven process, it removes general-purpose words with high frequency and dispersion rates –57% of word types belonging to general-purpose words, constituting 90% of the total words in the target corpus. This results in 43% of word types representing domain-oriented technical words that makes up 10% of the total words in the target corpus are able to be extracted. This allows future researchers to focus exclusively on the remaining 43% of word types in the optimized word list (OWL), enhancing the efficiency of corpus analysis for extracting domain knowledge. (3) The proposed method establishes a set of standard operation procedure (SOP) that can be duplicated and generally applied to optimize any corpus data.

Preliminary Data from the Small World of Singlish Words Project: Examining Responses to Common Singlish Words

We report data from the Small World of Singlish Words (SWOSW) Project, where word associations are obtained for a list of common Singlish words. Singapore English, colloquially known as Singlish, is a dialect of English spoken in Singapore. Free association data on these words were collected from a young adult population of university undergraduates using an online survey platform. This data enables the construction of semantic networks, allowing one to examine the semantic structure of individual words in the Singlish lexicon, as well as to compare differences in semantic structure across groups of participants.

Italian Fast Speech Reception Threshold Test: A New Method to Investigate Adult Auditory Impairment in Noise

Introduction: Purpose of our study was to compare two competing methods of performing bisyllabic word speech audiometry for the detection of the 50% speech reception threshold in noise (SRT50). Methods: Classic method is performed submitting multiple word lists at a fixed signal-to-noise ratio. A newer Fast method – Italian Fast Speech Reception Threshold 50 (IFastSRT50) – is performed by means of program software with a single list of bisyllabic words and noise intensity shifting. Results: Means comparison between SRT50 Classic and IFastSRT50 shows a slight significant correlation (r = 0.263; p = 0.044) and a wide significant difference: SRT50 Classic = −2.763 dB (SD = 4.1) and IFastSRT50 = −7.803 dB (SD = 2.1) (p < 0.0001). There is a high difference between the test execution time means (SRT50 Classic = 11 min, IFastSRT50 = 2 min; p < 0.0001). The correlation between test results and execution times was higher for SRT50 Classic than IFastSRT50. Conclusion: IFastSRT50 test is a reliable method to quickly investigate signal-to-noise ratio needed to obtain 50% of recognition scores with bisyllabic words; it allows less execution time than SRT50 Classic method and can avoid patient fatigue and other limitations of different speech discrimination tests in noise as sentences based ones.