Linguistic Clues Research Articles

Testing the Labeling Effect in Autistic Children.

Our objective was to test the labeling effect in autistic children. The effect has been robustly tested in typically developing (TD) individuals. TD children expect that any two objects that receive the same linguistic label will have similar properties, which suggests that they generate concepts based on acts of labeling. The labeling effect has not been tested on autistic children, who may not be equally attuned to the relevance of linguistic clues or may not generalize as swiftly as TD children. We reproduced Graham et al.,'s (Frontiers in Psychology 10.3389/fpsyg.2012.00586, 2013) design on 30 autistic children of different ages. Participants were divided into two groups depending on whether objects presented to them were named alike or differently (Same or Distinct Label between-individuals condition). The dependent variable was the number of target actions the child performed on an object, depending on whether that object made the same sound as a previously shown test object. We did not reproduce results similar to those reported in Graham et al., (Frontiers in Psychology 10.3389/fpsyg.2012.00586, 2013). Children in the Same Label group did not perform significantly more actions than children in the Distinct Label group when the objects that were handed to the children did not make the same sound as the test object. Autistic children do not seem to be sensitive to the labeling effect to the same extent as TD children. If these results are confirmed, intervention programs for autistic children should consider trainings on this way of generating concepts shared by their linguistic community.

Detection of sarcasm in tweets using hybrid machine learning method

<p>When one wants to express the contrary of what they mean, especially when insulting someone, sarcasm is used. One of the challenges associated with the control and management of content on social networking sites such as Twitter and other social media sites, is the recognition of sarcasm. Because sarcasm is purposefully conveyed through ambiguous word choice, even for humans it can be challenging to identify. Existing methods for automatically detecting sarcasm rely heavily on lexical as well as linguistic clues the majority of the time. On the other hand, these methods have not yielded a large or even a minor enhancement in terms of the correctness of the mood. The purpose of this study is to increase the accuracy of sentiment analysis by proposing a system that is both reliable and effective at identifying sarcasm. In this investigation, three different types of features are focussed: lexical, sarcastic, and context features. These feature sets are utilised in the process of categorising tweets as either sarcastic or not sarcastic. This research presents a sarcastic feature set coupled with an efficient hybrid machine learning approach, which ultimately results in improved accuracy. The results of the experiments reveal that the recommended hybrid machine learning method achieves 97.3% accuracy for sarcastic feature sets which is better when compared to existing machine learning techniques namely k-nearest neighbor, random forest, support vector machine and decision tree for the selection of the appropriate features.<strong></strong></p>

Literature Review on Multicultural Business Communication

This thorough literature assessment digs into the complex world of multicultural corporate communication and examines the many factors that affect interactions in different cultural situations. Through systematic gathering of data from various published articles related to the topic, the review begins by putting good multicultural communication in the perspective of the current worldwide business environment. It sheds light on how cultural differences affect communication dynamics by analyzing subtle linguistic clues, nonverbal cues, and underlying cultural norms that influence cross-cultural interactions. Critical examinations of multicultural corporate communication’s difficulties and impediments highlight the dangers of misunderstanding, stereotyping, and communication breakdowns. In addition to promoting the development of cultural sensitivity, using efficient communication tools, and encouraging relationship-building and trust across cultures, the review highlights techniques to improve cross-cultural communication. The review examines the changing role of technology, intercultural leadership, and the ethical ramifications of multicultural corporate contacts to anticipate future developments in this field. This study offers helpful guidance for practitioners and organizations attempting to negotiate the challenging environment of multicultural corporate communication by summarizing the significant results and implications of the research. To address the changing opportunities and difficulties in this vital subject, the study ends with a plea for continued research and innovation

한국어 계승어 화자의 발화오류와 사용 특징 연구

This study investigated the types of speech errors by Korean heritage speakers. A total of 238 speech errors were obtained from 14 groups of Korean heritage speakers in their twenties or thirties living in the USA, and we they were classified according to their linguistic components and forms of errors. The rates and types of the errors were compared with those of native speaker groups. The overall results led us to the claim that their errors were made not randomly, but in systematic ways, which reflected their knowledge of Korean. Such comparisons provide some linguistic clues for the process of language production and development. Considering the lack of studies on Korean speech errors made by native Koreans and heritage speakers, this research provides valuable data and analyses for researchers of speech error patterns, speech production modeling, language development, and/or heritage speakers’ linguistic system.

Discourse analysis based credibility checks to online reviews using deep learning based discourse markers

E-commerce strongly relies on marketing strategies that utilize user given feedback on services provided. Hotels and other hospitabilty outlets such as restaurants enlist user feedback about their services in their online reservation systems, thereby helping thier prospective customers in the decision making process. The credibility of such reviews is of paramount significance and any compromise of the process can lead to unwanted consequences both for customers and businesses. As such, the need for a method by which deceptive reviews are identified is evident to protect community and businesses alike. Deceptive reviews typically exhibit distinctive linguistic clues which can be detected, raising a flag about the credibilty of the user who posted them . Specifically, discursive features that show in such texts can be a useful tool in checking the credibility of reviews. Manually analysing the discourse and different rhetorical structures can be a tedious and timeconsuming process allowing some deceptive reviews to remain publically available. However, coupled with artificial intelligence tools such as deep learning approaches, discourse analysis could be performed in a manner that is both efficient and timely. The proposed study used a balanced publically available deceptive and truthful reviews dataset to design a discourse analysis based credibility check scheme with high accuracy. The multi-context of text is extracted via multi-n-gram windows (named as Discourse markers) applied in a proposed deep multi-channel convolutional neural network (MC-CNN). The hold-out approach is applied with 6 various splits of data on the proposed method MC-CNN to validate the performance. From various experiments, we report the best F1-score 87% has been achieved that not only defended the deep learning based discourse analysis but also defended the hypothesis made on deceptive and truthful reviews by proposed study.

Linguistic clues suggest that the Indonesian colonizers directly sailed to Madagascar

The Malagasy language belongs to the Austronesian Family and it is particularly close to some of the languages spoken in Indonesia, a fact that was first noticed at the beginning of the XVIIth century. The link to a precise Indonesian language is due to Dahl who, in 1951, firmly established a striking kinship with Maanyan, spoken in the South-East of Kalimantan. The introgression of Bantu terms is extremely limited, on the contrary the genetic makeup of the Malagasy people is African and Indonesian with comparable proportions. While genetics and linguistics agree that the colonization of Madagascar by Indonesian sailors took place in the second half of the first millennium, they disagree concerning the role of East-Africa in this event. Here we show that the dichotomy emerges because linguistics uses qualitative arguments where genetics has a consolidated tradition in the use of quantitative methods. After having collected the largest and most complete existing dataset for Malagasy, covering the entire island (207-terms Swadesh lists of 60 different dialects), we adopt new quantitative tools that allow us to confirm the genetics point of view that Indonesian sailors directly colonized Madagascar, without the East-African stopover conjectured in various studies in linguistics. The key point of our approach is the analysis of the geographical distribution of the degree of Bantu languages contamination of Malagasy dialects.

Leveraging execution traces to enhance traceability links recovery in BPMN models

Traceability Links Recovery has been a topic of interest for many years, resulting in techniques that perform traceability based on the linguistic clues of the software artifacts under study. However, BPMN models tend to present an overall lack of linguistic clues when compared to code-based artifacts or code generation models. Hence, TLR becomes a harder task when performed among requirements and BPMN models.

The Indication of Obedience in the Holy Qur’an - A Linguistic Approach

The study is based on studying the term (Obedience), clarifying its semantic concept in the Qur’anic usage, rooting it linguistically, and revealing the confusion in determining its significance in the Holy Qur’an, as many commentators have held it beyond its central significance in arrogance and arrogance. In the various contexts of its use, and the research paves the way for this by shedding light on the lexical significance of the article (Obey), and rooting its connotations, then it begins to market the evidence for the authenticity of the indication of (Obedience) on submission and obedience in all the resources of its use, especially in the verse of prayer on the prophet (peace and blessings be upon him and his family), the difference in it for purposes that may distance itself from the explanatory function, which is to reveal the significance of the Qur’anic vocabulary with pure linguistic clues.

Spatially oriented convolutional neural network for spatial relation extraction from natural language texts

AbstractSpatial relation extraction (e.g., topological relations, directional relations, and distance relations) from natural language descriptions is a fundamental but challenging task in several practical applications. Current state‐of‐the‐art methods rely on rule‐based metrics, either those specifically developed for extracting spatial relations or those integrated in methods that combine multiple metrics. However, these methods all rely on developed rules and do not effectively capture the characteristics of natural language spatial relations because the descriptions may be heterogeneous and vague and may be context sparse. In this article, we present a spatially oriented piecewise convolutional neural network (SP‐CNN) that is specifically designed with these linguistic issues in mind. Our method extends a general piecewise convolutional neural network with a set of improvements designed to tackle the task of spatial relation extraction. We also propose an automated workflow for generating training datasets by integrating new sentences with those in a knowledge base, based on string similarity and semantic similarity, and then transforming the sentences into training data. We exploit a spatially oriented channel that uses prior human knowledge to automatically match words and understand the linguistic clues to spatial relations, finally leading to an extraction decision. We present both the qualitative and quantitative performance of the proposed methodology using a large dataset collected from Wikipedia. The experimental results demonstrate that the SP‐CNN, with its supervised machine learning, can significantly outperform current state‐of‐the‐art methods on constructed datasets.

A Study of Zero Anaphora Resolution in Chinese Discourse: From the Perspective of Psycholinguistics.

Anaphora is the main linguistic means to establish discourse coherence, and anaphora resolution is the psychological process to maintain this coherence. Chinese discourse is characterized with providing multiplicity of linguistic clues to readers by employing various referential apparatuses such as pronoun anaphora, zero anaphora, and so on. As a way of avoiding repeated reference to an object that is mentioned beforehand, zero anaphora is frequently employed in discourse. The production and resolution of zero anaphora largely concerns some constraints underlying psychological mechanisms. We particularly focus on zero anaphora resolution in the present study to try to discover some specific aspects of the underlying mechanism, hoping to find out some factors unique to the resolution process. We designed the first two experiments to probe into the psychological reality when participants were presented with sentences containing either pronoun anaphora or zero anaphora or both under discourse condition with topic continuity in Experiment 1a and topic discontinuity in Experiment 1b. We did not find any significant difference in the reaction time between zero anaphora resolution and pronoun anaphora resolution, indicating that zero anaphora possibly works within the processing mechanism on which pronoun anaphora resolution depends. However, we found significantly longer time in reading the first sentence in any of the discourse, showing that the first-mention effect exists in anaphora resolution. We further explored the time course of zero anaphora resolution by measuring the reaction time during the period when participants read sentences that varied according to the location where zero anaphora occurred under two conditions: topic continuity (Experiment 2a) vs. topic discontinuity (Experiment 2b). The strategies of searching for the referential information were found divergent: the exhaustive searching strategy was adopted when the topics within a discourse were kept continuous and the heuristic searching strategy was employed when the topics were discontinuous. The design of Experiment 5 took the factor of voice type and situation consistency into consideration, investigating in what way do those factors influence the resolution of zero anaphora. The voice type, according to the results, plays a significant role for its exclusively close relationship with the first-mention effect.

Disinformation: analysis and identification.

We present an extensive study on disinformation, which is defined as information that is false and misleading and intentionally shared to cause harm. Through this work, we aim to answer the following questions:Can we automatically and accurately classify a news article as containing disinformation?What characteristics of disinformation differentiate it from other types of benign information? We conduct this study in the context of two significant events: the US elections of 2016 and the 2020 COVID pandemic. We build a series of classifiers to (i) examine linguistic clues exhibited by different types of fake news articles, (ii) analyze “clickbaityness” of disinformation headlines, and (iii) finally, perform fine-grained, veracity-based article classification through a natural language inference (NLI) module for automated disinformation verification; this utilizes a manually curated set of evidence sources. For the latter, we built a new dataset that is annotated with generic, veracity-based labels and ground truth evidence supporting each label. The veracity labels were formulated based on examining standards used by reputable fact-checking organizations. We show that disinformation derives features from both propaganda and mainstream news, making it more challenging to detect. However, there is significant potential for automating the fact-checking process to incorporate the degree of veracity. We provide error analysis that illustrates the challenges involved in the automated fact-checking task and identifies factors that may improve this process in future work. Finally, we also describe the implementation of a web app that extracts important entities and actions from a given article and searches the web to gather evidence from credible sources. The evidence articles are then used to generate a veracity label that can assist manual fact-checkers engaged in combating disinformation.

Between, Behind, and Out of Sight

Between, Behind, and Out of Sight

Developing and validating a visual questionnaire for the study of impersonalisation strategies: A design thinking approach

This article reports on an interdisciplinary, collaborative project grounded in linguistic theory on impersonalisation and visual communication theory on wordless visual narratives. The aims of this practice-based research project are to develop an alternative to existing methods of studying impersonalisation strategies through interdisciplinary collaboration and to test its usefulness for identifying not only the range of possible linguistic strategies of impersonalisation, but also the preferred strategies for specific impersonal uses. The group of 18 collaborators consisted of two linguists, one graphic design lecturer and 15 final-year graphic design students. The students were responsible for designing visual prompts for the questionnaire that 1) should not limit the possible linguistic answers, 2) must be able to accommodate any impersonalisation strategy, 3) should not contain any limiting linguistic clues, and 4) had to comply with the criteria on the basis of which the 12 impersonal uses are distinguished from one another. The questionnaire was piloted in two phases. After each phase, the students adjusted and refined their visual prompts. A validation phase indicated that the finalised questionnaire fulfils its functions, since at least 16 different impersonalisation strategies can be distinguished, and preferable strategies for each impersonal use can be identified.

ORGANIZATION OF STUDENTS’ INDEPENDENT ENGLISH LANGUAGE LEARNING IN THE COURSE OF PREPARATION TO SUBTEST OF LICENSING EXAMINATION «KROK 1. DENTISTRY»

Over two decades Ukrainian system of medical professional training has been providing a single pathway for primary licensure of all graduates of medical schools by licensing examinations «Krok 1», «Krok 2» and «Krok 3». Since their inceptions, the examinations provide the valid and reliable independent assessment of medical examinations in order to demonstrate they have knowledge and clinical skills for the unsupervised general practice of medicine, and thus, to grant the primary license to practice medicine. The aim of this study is to analyze ways of the organization of student’s self-learning at the Department of Foreign Languages with Latin and Medical Terminology, Ukrainian Medical Stomatological Academy, which will promote the confidence in own English language command and ensure demonstrating high performance of English subtest «Krok 1». Independent learning using additional texts should help students, concerning with previously studied elements of terminology, lexis and grammar, to figure out faster phrases and constructions in texts based on the contex and extra linguistic clues.
 The analysis of the factors influencing the students’ performance when doing the Krok tests, we have found the guided language exposure and scheduled amount of time for independent work according to the curriculum can help to overcome typical linguistic obstacles. Moreover, the English language instructors elaborated the questionnaire and surveyed the students to identify self-learning routine peculiarities, resources commonly used for self-training to licensing examination, which are the most effective from their point of view. The study included 155 third year dental students (2019-2020 academic year). The students actively use mobile applications from the playmarket, namely K-test, which they can use for independent practice at individual pace. Computer technologies can significantly improve the efficiency of foreign language teaching and learning and help us in organizing independent work of the students. According to result obtained, testkrokorg.ua internet resource ranks the second position. 19.35% of students chose this site as it gives an opportunity to check the knowledge, to get graded and to get the right answers. 17.41% of the respondents used testcentr.org.ua by reviewing and uploading Krok test booklets. 11.61% of the students used social network groups, which post materials, news and results to licensing examinations. 9.67% of students are found to prefer booklets; methodical instructions for students’ training that are on the site of our department. 3.2% of the responders suggest that lifehacks in YouTube contains the experience of bloggers and due to them the youth can learn new things. The obtained results have shown that the computer appliances are of great recourses for independent learning when used purposefully and guided by the instructors.

Vocabulary Learning Strategies Among Undergraduate ESL Learners in Tertiary Education

Vocabulary learning strategies (VLSs) play a significant role in assisting language learners in the development of language learning. A quantitative study was conducted to explore VLSs employed by undergraduate ESL learners in a public university. Data were collected from a group of 197 ESL learners from three faculties–Accountancy, Applied Science, and Computer and Mathematical Science–in the university. An adopted VLSs questionnaire by Gu & Johnson (1996) was used to answer two research questions: (1) to find the most and least preferred strategies and 2) to identify if there is any significant difference between the ESL learners in three different faculties–Accountancy, Applied Science, and Computer and Mathematical Science - in terms of their vocabulary learning strategies preference. The results were gathered using descriptive analysis and one-way ANOVA followed by a post-hoc test. The results of the study revealed that note taking, memory and guessing using linguistic clues were the three most preferred strategies while guessing using background knowledge and activation were the least preferred ones. In addition, the data also unveiled that there was a significant difference between Accountancy students and Applied Science students as well as Computer and Mathematical Science students both in guessing strategies. It is hoped that the findings of this study can shed some light to the teaching and learning process whereby the educators could employ the best VLSs to assist students in vocabulary learning while learners could be aware of strategies that suit them best.

Comparing Deep-Learning Architectures and Traditional Machine-Learning Approaches for Satire Identification in Spanish Tweets

Automatic satire identification can help to identify texts in which the intended meaning differs from the literal meaning, improving tasks such as sentiment analysis, fake news detection or natural-language user interfaces. Typically, satire identification is performed by training a supervised classifier for finding linguistic clues that can determine whether a text is satirical or not. For this, the state-of-the-art relies on neural networks fed with word embeddings that are capable of learning interesting characteristics regarding the way humans communicate. However, as far as our knowledge goes, there are no comprehensive studies that evaluate these techniques in Spanish in the satire identification domain. Consequently, in this work we evaluate several deep-learning architectures with Spanish pre-trained word-embeddings and compare the results with strong baselines based on term-counting features. This evaluation is performed with two datasets that contain satirical and non-satirical tweets written in two Spanish variants: European Spanish and Mexican Spanish. Our experimentation revealed that term-counting features achieved similar results to deep-learning approaches based on word-embeddings, both outperforming previous results based on linguistic features. Our results suggest that term-counting features and traditional machine learning models provide competitive results regarding automatic satire identification, slightly outperforming state-of-the-art models.

From Flaming to Incited Crime: Recognising Cyberbullying on Chinese WeChat Account

Cyberbullying involves the use of ICTs (information and communication technologies) to carry out an act or a series of acts intended to harm victims emotionally [12, p. 288]. As a crucial type of cyberbullying, flaming is also intended for emotional harm to victims [10, p. 419]. WeChat subscription accounts, as one kind of the most popular social media in China, are a perfect venue for flaming. WeChat account writers are permitted to post their attitudes which may contain flaming language under the veil of legitimate account. Sometimes such flaming language may further incite crimes or moral wrongs. However, the potential harmful effect and the underlying risk of incitement become invisible and have been paid little attention. In order to reveal the flaming nature of WeChat accounts, twenty-six suspect articles from a Chinese Wechat subscription account are analysed from the perspectives of corpus linguistics and pragmatics. As a result, linguistic clues to flaming (cyberbullying) are recognised, and related legal issues concerning internet incitement are addressed. Specifically, it is found that: firstly, the linguistic manifestations of the invisible flaming could be revealed in terms of keywords, semantic prosodies and speech acts; secondly, the flamer is very good at formulating some emotionally harmful keywords and semantic prosodies into his attitudes and imposing them upon the readers; thirdly, language crime of incitement requires only the inciter’s illocutionary act, i.e., intention, rather than the outcome of the incitement. These results suggest that linguistic approach helps in penetrating and recognising flaming (cyberbullying) articles in Chinese social media for protecting the mass audience from being fallen victims. Besides, the results also provide some implications on social media laws and regulations in mainland China.

Effects of Chinese word structure on object categorization in Chinese–English bilinguals

ABSTRACTWe investigated how verbal labels affect object categorization in bilinguals. In English, most nouns do not provide linguistic clues to their categories (an exception issunflower), whereas in Chinese, some nouns provide category information morphologically (e.g., 鸵鸟-ostrichand 知更鸟-robinhave the morpheme鸟-birdin their Chinese names), while some nouns do not (e.g., 企鹅-penguinand 鸽子-pigeon). We examined the effect of Chinese word structure on bilinguals’ categorization processes in two ERP experiments. Chinese–English bilinguals and English monolinguals judged the membership of atypical (e.g.,ostrich,penguin) vs. typical (e.g.,robin,pigeon) pictorial (Experiment 1) and English word (Experiment 2) exemplars of categories (e.g.,bird). English monolinguals showed typicality effects in RT data, and in the N300 and N400 of ERP data, regardless of whether the object name had a category cue in Chinese. In contrast, Chinese–English bilinguals showed a larger typicality effect for objects without category cues in their name than objects with cues, even when they were tested in English. These results demonstrate that linguistic information in bilinguals’ L1 has an effect on their L2 categorization processes. The findings are explained using the label-feedback hypothesis.

Adjective Learning by Spanish-Speakers Children and Adults. The Impact of Morphology and Semantic Information

The purpose of this research was to explore the kind of information Spanish-speaking 3-year-old children and adults use when learning adjectives in a joint picturebook reading situation. The impact of two linguistic clues was studied; a morphological clue (adjective suffix) and a semantic clue (descriptive information concerning the property). Results show that for children the description was decisive to map the new adjective with the property; for adults, instead, the presence of the suffix was crucial. These results illustrate a developmental shift in the sort of clues that shapes adjective learning.

Chinese shallow semantic parsing based on multilevel linguistic clues

With rapid development of artificial intelligence and Chinese information processing technology, research related to natural language processing have reached the level of semantic understanding gradually, while Chinese Shallow Semantic Parsing is the key technique in the semantic understanding field. In this paper, a further improvement is conducted on the basic model of Chinese semantic role labeling for linear classification based on conditional random fields. In this paper, a method of combination of linguistic clues, combining with the existing linear sequence labeling algorithm and integrating some multilevel linguistic clues, such as morphology is related to syntax, in the model training to reconstruct and improve the Chinese semantic role labeling model of linear sequence. Through the experimental comparison and linguistic assistant analysis, this paper puts forward a targeted improvement method to significantly improve the accuracy of model labeling and proves that the integration of related linguistic clues in the semantic role labeling model based on linear sequence can improve the effect of model labeling.