Using language in social media posts to study the network dynamics of depression longitudinally.

Network theory of mental illness posits that causal interactions between symptoms give rise to mental health disorders. Increasing evidence suggests that depression network connectivity may be a risk factor for transitioning and sustaining a depressive state. Here we analysed social media (Twitter) data from 946 participants who retrospectively self-reported the dates of any depressive episodes in the past 12 months and current depressive symptom severity. We construct personalised, within-subject, networks based on depression-related linguistic features. We show an association existed between current depression severity and 8 out of 9 text features examined. Individuals with greater depression severity had higher overall network connectivity between depression-relevant linguistic features than those with lesser severity. We observed within-subject changes in overall network connectivity associated with the dates of a self-reported depressive episode. The connectivity within personalized networks of depression-associated linguistic features may change dynamically with changes in current depression symptoms.

Improving fake news classification using dependency grammar.

Fake news is a complex problem that leads to different approaches used to identify them. In our paper, we focus on identifying fake news using its content. The used dataset containing fake and real news was pre-processed using syntactic analysis. Dependency grammar methods were used for the sentences of the dataset and based on them the importance of each word within the sentence was determined. This information about the importance of words in sentences was utilized to create the input vectors for classifications. The paper aims to find out whether it is possible to use the dependency grammar to improve the classification of fake news. We compared these methods with the TfIdf method. The results show that it is possible to use the dependency grammar information with acceptable accuracy for the classification of fake news. An important finding is that the dependency grammar can improve existing techniques. We have improved the traditional TfIdf technique in our experiment.

Designing a hybrid dimension reduction for improving the performance of Amharic news document classification.

The volume of Amharic digital documents has grown rapidly in recent years. As a result, automatic document categorization is highly essential. In this paper, we present a novel dimension reduction approach for improving classification accuracy by combining feature selection and feature extraction. The new dimension reduction method utilizes Information Gain (IG), Chi-square test (CHI), and Document Frequency (DF) to select important features and Principal Component Analysis (PCA) to refine the features that have been selected. We evaluate the proposed dimension reduction method with a dataset containing 9 news categories. Our experimental results verified that the proposed dimension reduction method outperforms other methods. Classification accuracy with the new dimension reduction is 92.60%, which is 13.48%, 16.51% and 10.19% higher than with IG, CHI, and DF respectively. Further work is required since classification accuracy still decreases as we reduce the feature size to save computational time.

Comparing prehistoric constructed languages: world-building and its role in understanding prehistoric languages.

In this paper, we compare the languages each of the authors invented as prehistoric languages for popular culture media. Schreyer's language, Beama, was created for the film Alpha (2018), while Adger's language, Tan!aa Kawawa ki, was created for a television series on how early hominins spread throughout the world (the series was green-lit but then cancelled). We argue that though this creative process may seem far removed from classical research paradigms on language evolution, it can provide some insight into how disparate research on the possible properties of prehistoric languages can be brought together to illustrate how these languages might have worked as whole linguistic systems within these imagined worlds, as well as in prehistory. This article is part of the theme issue 'Reconstructing prehistoric languages'.

The history of number words in the world's languages-what have we learnt so far?

For over 100 years, researchers from various disciplines have been enthralled and occupied by the study of number words. This article discusses implications for the study of deep history and human evolution that arise from this body of work. Phylogenetic modelling shows that low-limit number words are preserved across thousands of years, a pattern consistently observed in several language families. Cross-linguistic frequencies of use and experimental studies also point to widespread homogeneity in the use of number words. Yet linguistic typology and field documentation reports caution against positing a privileged linguistic category for number words, showing a wealth of variation in how number words are encoded across the world. In contrast with low-limit numbers, the higher numbers are characterized by a rapid and morphologically consistent pattern of expansion, and behave like grammatical phrasal units, following language-internal rules. Taken together, the evidence suggests that numbers are at the cross-roads of language history. For languages that do have productive and consistent number systems, numerals one to five are among the most reliable available linguistic fossils of deep history, defying change yet still bearing the marks of the past, while higher numbers emerge as innovative tools looking to the future, derived using language-internal patterns and created to meet the needs of modern speakers. This article is part of the theme issue 'Reconstructing prehistoric languages'.

A reinforcement-learning approach to efficient communication.

We present a multi-agent computational approach to partitioning semantic spaces using reinforcement-learning (RL). Two agents communicate using a finite linguistic vocabulary in order to convey a concept. This is tested in the color domain, and a natural reinforcement learning mechanism is shown to converge to a scheme that achieves a near-optimal trade-off of simplicity versus communication efficiency. Results are presented both on the communication efficiency as well as on analyses of the resulting partitions of the color space. The effect of varying environmental factors such as noise is also studied. These results suggest that RL offers a powerful and flexible computational framework that can contribute to the development of communication schemes for color names that are near-optimal in an information-theoretic sense and may shape color-naming systems across languages. Our approach is not specific to color and can be used to explore cross-language variation in other semantic domains.

Hahahahaha, Duuuuude, Yeeessss!: A two-parameter characterization of stretchable words and the dynamics of mistypings and misspellings.

Stretched words like 'heellllp' or 'heyyyyy' are a regular feature of spoken language, often used to emphasize or exaggerate the underlying meaning of the root word. While stretched words are rarely found in formal written language and dictionaries, they are prevalent within social media. In this paper, we examine the frequency distributions of 'stretchable words' found in roughly 100 billion tweets authored over an 8 year period. We introduce two central parameters, 'balance' and 'stretch', that capture their main characteristics, and explore their dynamics by creating visual tools we call 'balance plots' and 'spelling trees'. We discuss how the tools and methods we develop here could be used to study the statistical patterns of mistypings and misspellings and be used as a basis for other linguistic research involving stretchable words, along with the potential applications in augmenting dictionaries, improving language processing, and in any area where sequence construction matters, such as genetics.

The language of well-being: Tracking fluctuations in emotion experience through everyday speech.

The words that people use have been found to reflect stable psychological traits, but less is known about the extent to which everyday fluctuations in spoken language reflect transient psychological states. We explored within-person associations between spoken words and self-reported state emotion among 185 participants who wore the Electronically Activated Recorder (EAR; an unobtrusive audio recording device) and completed experience sampling reports of their positive and negative emotions 4 times per day for 7 days (1,579 observations). We examined language using the Linguistic Inquiry and Word Count program (LIWC; theoretically created dictionaries) and open-vocabulary themes (clusters of data-driven semantically-related words). Although some studies give the impression that LIWC's positive and negative emotion dictionaries can be used as indicators of emotion experience, we found that when computed on spoken language, LIWC emotion scores were not significantly associated with self-reports of state emotion experience. Exploration of other categories of language variables suggests a number of hypotheses about substantive everyday correlates of momentary positive and negative emotion that can be tested in future studies. These findings (a) suggest that LIWC positive and negative emotion dictionaries may not capture self-reported subjective emotion experience when applied to everyday speech, (b) emphasize the importance of establishing the validity of language-based measures within one's target domain, (c) demonstrate the potential for developing new hypotheses about personality processes from the open-ended words that are used in everyday speech, and (d) extend perspectives on intraindividual variability to the domain of spoken language. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Dombi power partitioned Heronian mean operators of q-rung orthopair fuzzy numbers for multiple attribute group decision making.

In this paper, a set of Dombi power partitioned Heronian mean operators of q-rung orthopair fuzzy numbers (qROFNs) are presented, and a multiple attribute group decision making (MAGDM) method based on these operators is proposed. First, the operational rules of qROFNs based on the Dombi t-conorm and t-norm are introduced. A q-rung orthopair fuzzy Dombi partitioned Heronian mean (qROFDPHM) operator and its weighted form are then established in accordance with these rules. To reduce the negative effect of unreasonable attribute values on the aggregation results of these operators, a q-rung orthopair fuzzy Dombi power partitioned Heronian mean operator and its weighted form are constructed by combining qROFDPHM operator with the power average operator. A method to solve MAGDM problems based on qROFNs and the constructed operators is designed. Finally, a practical example is described, and experiments and comparisons are performed to demonstrate the feasibility and effectiveness of the proposed method. The demonstration results show that the method is feasible, effective, and flexible; has satisfying expressiveness; and can consider all the interrelationships among different attributes and reduce the negative influence of biased attribute values.

Online Processing of Subject-Verb-Object Order in a Diverse Sample of Mandarin-Exposed Preschool Children with Autism Spectrum Disorder.

Grammatical comprehension remains a strength in English-exposed young children with autism spectrum disorder (ASD), yet limited research has investigated how preschool children with ASD process grammatical structures in real time, in any language. Using the eye-movement measures of Intermodal Preferential Looking, we assessed online processing of subject-verb-object (SVO) order in seventy 2- to 5-year-old children with ASD exposed to Mandarin Chinese across the spectrum, whose vocabulary production scores were dramatically delayed compared with the typical controls. With this Mandarin-exposed sample, we tested the extent to which children with ASD require (a) highly consistent input and/or (b) good discourse/pragmatics for acquiring grammatical structures. Children viewed side-by-side videos depicting reversible actions (e.g., a bird pushing a horse vs. a horse pushing a bird), and heard an audio matching only one of those actions; their eyegaze to each video was coded and analyzed. Both typically developing children and children with ASD demonstrated comprehension of SVO word order, suggesting that core grammatical structures such as basic word order may be preserved in children with ASD across languages despite radical differences in language environment, social/pragmatic abilities, and neurological organization. However, children with ASD were less efficient in online sentence processing than typical children, and the efficiency of their online sentence processing was related to their standardized language assessment scores. Of note is that across both Mandarin Chinese and English, some proportion of minimally verbal children with ASD exhibited SVO comprehension despite their profoundly impaired expressive language skills. Autism Res 2019, 12: 1829-1844. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Grammar is a strength in the language comprehension of young English learners with autism spectrum disorder (ASD). Eye-movement data from a diverse sample of Chinese preschoolers with ASD indicated similar grammatical strength of basic word order in Chinese (e.g., to understand sentences like "The bird is pushing the horse"). Moreover, children's proficiency of sentence processing was related to their language assessment scores. Across languages, such knowledge is even spared in some minimally verbal children with ASD.

The geographical configuration of a language area influences linguistic diversity.

Like the transfer of genetic variation through gene flow, language changes constantly as a result of its use in human interaction. Contact between speakers is most likely to happen when they are close in space, time, and social setting. Here, we investigated the role of geographical configuration in this process by studying linguistic diversity in Japan, which comprises a large connected mainland (less isolation, more potential contact) and smaller island clusters of the Ryukyuan archipelago (more isolation, less potential contact). We quantified linguistic diversity using dialectometric methods, and performed regression analyses to assess the extent to which distance in space and time predict contemporary linguistic diversity. We found that language diversity in general increases as geographic distance increases and as time passes-as with biodiversity. Moreover, we found that (I) for mainland languages, linguistic diversity is most strongly related to geographic distance-a so-called isolation-by-distance pattern, and that (II) for island languages, linguistic diversity reflects the time since varieties separated and diverged-an isolation-by-colonisation pattern. Together, these results confirm previous findings that (linguistic) diversity is shaped by distance, but also goes beyond this by demonstrating the critical role of geographic configuration.

Minimalistic Approach to Coreference Resolution in Lithuanian Medical Records.

Coreference resolution is a challenging part of natural language processing (NLP) with applications in machine translation, semantic search and other information retrieval, and decision support systems. Coreference resolution requires linguistic preprocessing and rich language resources for automatically identifying and resolving such expressions. Many rarer and under-resourced languages (such as Lithuanian) lack the required language resources and tools. We present a method for coreference resolution in Lithuanian language and its application for processing e-health records from a hospital reception. Our novelty is the ability to process coreferences with minimal linguistic resources, which is important in linguistic applications for rare and endangered languages. The experimental results show that coreference resolution is applicable to the development of NLP-powered online healthcare services in Lithuania.

A Probabilistic and Multi-Objective Analysis of Lexicase Selection and ε-Lexicase Selection.

Lexicase selection is a parent selection method that considers training cases individually, rather than in aggregate, when performing parent selection. Whereas previous work has demonstrated the ability of lexicase selection to solve difficult problems in program synthesis and symbolic regression, the central goal of this article is to develop the theoretical underpinnings that explain its performance. To this end, we derive an analytical formula that gives the expected probabilities of selection under lexicase selection, given a population and its behavior. In addition, we expand upon the relation of lexicase selection to many-objective optimization methods to describe the behavior of lexicase selection, which is to select individuals on the boundaries of Pareto fronts in high-dimensional space. We show analytically why lexicase selection performs more poorly for certain sizes of population and training cases, and show why it has been shown to perform more poorly in continuous error spaces. To address this last concern, we propose new variants of ε-lexicase selection, a method that modifies the pass condition in lexicase selection to allow near-elite individuals to pass cases, thereby improving selection performance with continuous errors. We show that ε-lexicase outperforms several diversity-maintenance strategies on a number of real-world and synthetic regression problems.

Intuitionistic Linguistic Multiple Attribute Decision-Making with Induced Aggregation Operator and Its Application to Low Carbon Supplier Selection.

The main focus of this paper is to investigate the multiple attribute decision making (MADM) method under intuitionistic linguistic (IL) environment, based on induced aggregation operators and analyze possibilities for its application in low carbon supplier selection. More specifically, a new aggregation operator, called intuitionistic linguistic weighted induced ordered weighted averaging (ILWIOWA), is introduced to facilitate the IL information. Some of its desired properties are explored. A further generalization of the ILWIOWA, called intuitionistic linguistic generalized weighted induced ordered weighted averaging (ILGWIOWA), operator is developed. Furthermore, by employing the proposed operators, a MADM approach based on intuitionistic linguistic information is presented. Finally, an illustrative example concerning low carbon supplier selection and comparative analyses are conducted to demonstrate the effectiveness and practicality of the proposed approach.

Effect of chunk strength on the performance of children with developmental dyslexia on artificial grammar learning task may be related to complexity.

There's a long held view that chunks play a crucial role in artificial grammar learning performance. We compared chunk strength influences on performance, in high and low topological entropy (a measure of complexity) grammar systems, with dyslexic children, age-matched and reading-level-matched control participants. Findings show that age-matched control participants' performance reflected equivalent influence of chunk strength in the two topological entropy conditions, as typically found in artificial grammar learning experiments. By contrast, dyslexic children and reading-level-matched controls' performance reflected knowledge of chunk strength only under the low topological entropy condition. In the low topological entropy grammar system, they appeared completely unable to utilize chunk strength to make appropriate test item selections. In line with previous research, this study suggests that for typically developing children, it is the chunks that are attended during artificial grammar learning and create a foundation on which implicit associative learning mechanisms operate, and these chunks are unitized to different strengths. However, for children with dyslexia, it is complexity that may influence the subsequent memorability of chunks, independently of their strength.

The Potential of Automatic Word Comparison for Historical Linguistics.

The amount of data from languages spoken all over the world is rapidly increasing. Traditional manual methods in historical linguistics need to face the challenges brought by this influx of data. Automatic approaches to word comparison could provide invaluable help to pre-analyze data which can be later enhanced by experts. In this way, computational approaches can take care of the repetitive and schematic tasks leaving experts to concentrate on answering interesting questions. Here we test the potential of automatic methods to detect etymologically related words (cognates) in cross-linguistic data. Using a newly compiled database of expert cognate judgments across five different language families, we compare how well different automatic approaches distinguish related from unrelated words. Our results show that automatic methods can identify cognates with a very high degree of accuracy, reaching 89% for the best-performing method Infomap. We identify the specific strengths and weaknesses of these different methods and point to major challenges for future approaches. Current automatic approaches for cognate detection-although not perfect-could become an important component of future research in historical linguistics.

Performance of children with developmental dyslexia on high and low topological entropy artificial grammar learning task.

Graph complexity as measured by topological entropy has been previously shown to affect performance on artificial grammar learning tasks among typically developing children. The aim of this study was to examine the effect of graph complexity on implicit sequential learning among children with developmental dyslexia. Our goal was to determine whether children's performance depends on the complexity level of the grammar system learned. We conducted two artificial grammar learning experiments that compared performance of children with developmental dyslexia with that of age- and reading level-matched controls. Experiment 1 was a high topological entropy artificial grammar learning task that aimed to establish implicit learning phenomena in children with developmental dyslexia using previously published experimental conditions. Experiment 2 is a lower topological entropy variant of that task. Results indicated that given a high topological entropy grammar system, children with developmental dyslexia who were similar to the reading age-matched control group had substantial difficulty in performing the task as compared to typically developing children, who exhibited intact implicit learning of the grammar. On the other hand, when tested on a lower topological entropy grammar system, all groups performed above chance level, indicating that children with developmental dyslexia were able to identify rules from a given grammar system. The results reinforced the significance of graph complexity when experimenting with artificial grammar learning tasks, particularly with dyslexic participants.

Partner Pronoun Use, Communal Coping, and Abstinence during Couple-Focused Intervention for Problematic Alcohol Use.

Communal coping-a process in which romantic partners view a problem as ours rather than yours or mine, and take collaborative action to address it -has emerged as an important predictor of health and treatment outcomes. In a study of partners' pronoun use prior to and during couple-focused alcohol interventions, we examined first-person plural (we-talk) and singular (I-talk) pronouns as linguistic markers of communal coping and behavioral predictors of treatment outcome. Thirty-three couples in which one partner abused alcohol were selected from a randomized control trial (N = 63) of couple-focused Cognitive-Behavioral or Family Systems Therapy if they had unambiguously successful or unsuccessful treatment outcomes (i.e., patient maintained abstinence for 30 days prior to treatment termination or had more than one heavy drinking day in the same period). Pronoun measures for each partner were obtained via computerized text analysis from transcripts of partners' speech, derived from a videotaped pretreatment interaction task and three subsequent therapy sessions. Spouse we-talk during the intervention (accounting for pretreatment we-talk), as an index of communal orientation, uniquely predicted successful treatment outcomes. In contrast, both patient and spouse I-talk during the intervention (accounting for pretreatment I-talk), as a marker of individualistic orientation, uniquely predicted unsuccessful outcomes, especially when distinguishing active and passive (I vs. me/my) pronoun forms. Results strengthen evidence for the prognostic significance of spouse behavior for patient health outcomes and for communal coping (indexed via pronoun use) as a potential mechanism of change in couple-focused interventions for health problems.

Sampling Assumptions Affect Use of Indirect Negative Evidence in Language Learning.

A classic debate in cognitive science revolves around understanding how children learn complex linguistic patterns, such as restrictions on verb alternations and contractions, without negative evidence. Recently, probabilistic models of language learning have been applied to this problem, framing it as a statistical inference from a random sample of sentences. These probabilistic models predict that learners should be sensitive to the way in which sentences are sampled. There are two main types of sampling assumptions that can operate in language learning: strong and weak sampling. Strong sampling, as assumed by probabilistic models, assumes the learning input is drawn from a distribution of grammatical samples from the underlying language and aims to learn this distribution. Thus, under strong sampling, the absence of a sentence construction from the input provides evidence that it has low or zero probability of grammaticality. Weak sampling does not make assumptions about the distribution from which the input is drawn, and thus the absence of a construction from the input as not used as evidence of its ungrammaticality. We demonstrate in a series of artificial language learning experiments that adults can produce behavior consistent with both sets of sampling assumptions, depending on how the learning problem is presented. These results suggest that people use information about the way in which linguistic input is sampled to guide their learning.

A word-count approach to analyze linguistic patterns in the reflective writings of medical students.

BACKGROUND: Teaching reflection and administering reflective writing assignments to students are widely practiced and discussed in medical education and health professional education. However, little is known about how medical students use language to construct their narratives. Exploring students' linguistic patterns in their reflective writings can facilitate understanding the scope and facets of their reflections and their representational or communication approaches to share their experiences. Moreover, research findings regarding gender differences in language use are inconsistent. Therefore, we attempted to examine how females and males differ in their use of words in reflective writing within our research circumstance to detect the unique and gender-specific approaches to learning and their applications. METHODS: We analyzed the linguistic profiles of psychological process categories in the reflective writings of medical students and examined the difference in word usage between male and female medical students. During the first year of a clinical rotation, 60 fifth-year medical students wrote reflective narratives regarding pediatric patients and the psychosocial challenges faced by the patients and their family members. The narratives were analyzed using the Chinese version of Linguistic Inquiry and Word Count (CLIWC), a text analysis software program. Multivariate procedures were applied for statistical analysis. RESULTS: Cognitive words were most pervasive, averaging 22.16%, whereas perceptual words (2.86%) were least pervasive. Female students used more words related to positive emotions and sadness than did male students. The male students exceeded the female students only in the space category. The major limitation of this study is that CLIWC cannot directly acquire contextual text meanings; therefore, depending on the research topic, further qualitative study of the given texts might be necessary. CONCLUSIONS: To enhance students' empathy toward the psychosocial issues faced by patients and their family members, students should be encouraged to explore the domain of psychological processes by identifying and expressing their affective and perceptual experiences. Researchers in future studies should use outcome measures such as self-awareness or empathy to determine the overall effectiveness of reflective writing and how changes in linguistic patterns affect such outcomes.