Papers reviewed and determined not to be word norm studies. Use the flag icon to report errors or suggest re-inclusion.
16504 papers
Explaining why the same passage may have different rhetorical structures when conveyed in different languages remains an open question. Starting from a trilingual translation corpus, this paper aims to provide a new qualitative method for the comparison of rhetorical structures in different languages and to specify why translated texts may differ in their rhetorical structures. To achieve these aims we have carried out a contrastive analysis, comparing a corpus of parallel English, Spanish and Basque texts, using Rhetorical Structure Theory. We propose a method to describe the main linguistic differences among the rhetorical structures of the three languages in the two annotation stages (segmentation and rhetorical analysis). We show a new type of comparison that has important advantages with regard to the quantitative method usually employed: it provides an accurate measurement of inter-annotator agreement, and it pinpoints sources of disagreement among annotators. With the use of this new method, we show how translation strategies affect discourse structure.
Language resources are important for those working on computational methods to analyse and study languages. These resources are needed to help advancing the research in fields such as natural language processing, machine learning, information retrieval and text analysis in general. We describe the creation of useful resources for languages that currently lack them, taking resources for Arabic summarisation as a case study. We illustrate three different paradigms for creating language resources, namely: (1) using crowdsourcing to produce a small resource rapidly and relatively cheaply; (2) translating an existing gold-standard dataset, which is relatively easy but potentially of lower quality; and (3) using manual effort with appropriately skilled human participants to create a resource that is more expensive but of high quality. The last of these was used as a test collection for TAC-2011. An evaluation of the resources is also presented.
Syntactic-semantic treebank for domain ontology creationThis paper focuses on the creation of a domain treebank for the purposes of compiling a domain ontology. The domain treebank is viewed as a suitable resource for extracting of semantic relations from syntactic structures. First, the steps for ontology building are considered. Then, the processing over glossaries and standards is described with regard to their syntactic annotation. The utility of deriving semantic knowledge from the Treebank is also illustrated via the basic phrases. The idea is that the domain knowledge is represented in the domain data, but via treebanking more linguistic patterns can be extracted, which to be mapped to concepts and relations in a domain ontology.
Past research finds that people prefer to sit next to others who are similar to them in a variety of dimensions such as race, sex, and physical appearance. This preference for similarity in seating arrangements is called aggregation and is most commonly measured with the aggregation index (Campbell, Kruskal, & Wallace, Sociometry 29, 1–15, 1966). The aggregation index compares the observed dissimilarity in seating with the amount of dissimilarity that would be expected if seats were chosen randomly. However, the current closed-form equations for this method limit the ease, flexibility, and inferences that researchers have. This paper presents a new approach for studying aggregation that uses bootstrapped resampling of the seating environment to estimate the aggregation index parameters. This method, compiled as an executable program, SocialAggregation, reads a seating chart matrix provided by the researcher and automatically computes the observed number of dissimilar adjacencies, and simulates random seating preferences. The current method’s estimates not only converge with those of the original method, but it also handles a wider variety of situations and also allows for more precise hypothesis testing by directly modeling the distribution of the seating arrangements. Developing a better measure of aggregation opens new possibilities for understanding intergroup biases, and allows researchers to examine aggregation more efficiently.
Imagination inflation is where imaginative elaboration of possible childhood experiences inflates (increases) participants’ estimation that these events actually occurred, as indicated by pre- to post-manipulation ratings changes. This research primarily uses the Life Events Inventory (LEI), listing possible experiences that could have happened during childhood (Garry, Manning, Loftus, & Sherman, Psychonomic Bulletin & Review, 3, 208–214, 1996). Although imagination inflation research has spawned more than 50 investigations, no normative ratings exist on individual items contained in the LEI. To address this, we present descriptive statistics (mean, median, standard deviation, confidence interval) for 124 LEI items on occurrence (how likely is it that this experience happened to you), plausibility (how plausible is it that this event could have happened to someone), and desirability (how desirable is this experience). Occurrence and plausibility showed similar patterns of mean item ratings and were highly correlated, whereas desirability was moderately correlated with plausibility and unrelated to occurrence. These data should facilitate a more informed selection of specific LEI items to use in further research and can assist in clarifying the contributions of normative occurrence, plausibility, and desirability to imagination inflation effects.
The use of Internet panels to collect survey data is increasing because it is cost-effective, enables access to large and diverse samples quickly, takes less time than traditional methods to obtain data for analysis, and the standardization of the data collection process makes studies easy to replicate. A variety of probability-based panels have been created, including Telepanel/CentERpanel, Knowledge Networks (now GFK KnowledgePanel), the American Life Panel, the Longitudinal Internet Studies for the Social Sciences panel, and the Understanding America Study panel. Despite the advantage of having a known denominator (sampling frame), the probability-based Internet panels often have low recruitment participation rates, and some have argued that there is little practical difference between opting out of a probability sample and opting into a nonprobability (convenience) Internet panel. This article provides an overview of both probability-based and convenience panels, discussing potential benefits and cautions for each method, and summarizing the approaches used to weight panel respondents in order to better represent the underlying population. Challenges of using Internet panel data are discussed, including false answers, careless responses, giving the same answer repeatedly, getting multiple surveys from the same respondent, and panelists being members of multiple panels. More is to be learned about Internet panels generally and about Web-based data collection, as well as how to evaluate data collected using mobile devices and social-media platforms.
The intelligent tutoring system (ITS) BRCA Gist is a Web-based tutor developed using the Shareable Knowledge Objects (SKO) platform that uses latent semantic analysis to engage women in natural-language dialogues to teach about breast cancer risk. BRCA Gist appears to be the first ITS designed to assist patients’ health decision making. Two studies provide fine-grained analyses of the verbal interactions between BRCA Gist and women responding to five questions pertaining to breast cancer and genetic risk. We examined how “gist explanations” generated by participants during natural-language dialogues related to outcomes. Using reliable rubrics, scripts of the participants’ verbal interactions with BRCA Gist were rated for content and for the appropriateness of the tutor’s responses. Human researchers’ scores for the content covered by the participants were strongly correlated with the coverage scores generated by BRCA Gist, indicating that BRCA Gist accurately assesses the extent to which people respond appropriately. In Study 1, participants’ performance during the dialogues was consistently associated with learning outcomes about breast cancer risk. Study 2 was a field study with a more diverse population. Participants with an undergraduate degree or less education who were randomly assigned to BRCA Gist scored higher on tests of knowledge than those assigned to the National Cancer Institute website or than a control group. We replicated findings that the more expected content that participants included in their gist explanations, the better they performed on outcome measures. As fuzzy-trace theory suggests, encouraging people to develop and elaborate upon gist explanations appears to improve learning, comprehension, and decision making.
This paper introduces <tiger2/>, an XML format developed to serialise the object model defined by the ISO Syntactic Annotation Framework SynAF. Based on widespread best practices we adapt a popular XML format for syntactic annotation, TigerXML, with additional features to support a variety of syntactic phenomena including constituent and dependency structures, binding, and different node types such as compounds or empty elements. We also define interfaces to other formats and standards including the Morpho-syntactic Annotation Framework MAF and the ISOCat Data Category Registry. Finally a case study of the German Treebank TueBa-D/Z is presented, showcasing the handling of constituent structures, topological fields and coreference annotation in tandem.
In this article, the R package LSAfun is presented. This package enables a variety of functions and computations based on Vector Semantic Models such as Latent Semantic Analysis (LSA) Landauer, Foltz and Laham (Discourse Processes 25:259–284, 1998), which are procedures to obtain a high-dimensional vector representation for words (and documents) from a text corpus. Such representations are thought to capture the semantic meaning of a word (or document) and allow for semantic similarity comparisons between words to be calculated as the cosine of the angle between their associated vectors. LSAfun uses pre-created LSA spaces and provides functions for (a) Similarity Computations between words, word lists, and documents; (b) Neighborhood Computations, such as obtaining a word’s or document’s most similar words, (c) plotting such a neighborhood, as well as similarity structures for any word lists, in a two- or three-dimensional approximation using Multidimensional Scaling, (d) Applied Functions, such as computing the coherence of a text, answering multiple choice questions and producing generic text summaries; and (e) Composition Methods for obtaining vector representations for two-word phrases. The purpose of this package is to allow convenient access to computations based on LSA.
In the author recognition test (ART), participants are presented with a series of names and foils and are asked to indicate which ones they recognize as authors. The test is a strong predictor of reading skill, and this predictive ability is generally explained as occurring because author knowledge is likely acquired through reading or other forms of print exposure. In this large-scale study (1,012 college student participants), we used item response theory (IRT) to analyze item (author) characteristics in order to facilitate identification of the determinants of item difficulty, provide a basis for further test development, and optimize scoring of the ART. Factor analysis suggested a potential two-factor structure of the ART, differentiating between literary and popular authors. Effective and ineffective author names were identified so as to facilitate future revisions of the ART. Analyses showed that the ART is a highly significant predictor of the time spent encoding words, as measured using eyetracking during reading. The relationship between the ART and time spent reading provided a basis for implementing a higher penalty for selecting foils, rather than the standard method of ART scoring (names selected minus foils selected). The findings provide novel support for the view that the ART is a valid indicator of reading volume. Furthermore, they show that frequency data can be used to select items of appropriate difficulty, and that frequency data from corpora based on particular time periods and types of texts may allow adaptations of the test for different populations.
Multilevel linguistic features have been proposed for discourse analysis, but there have been few applications of multilevel linguistic features to readability models and also few validations of such models. Most traditional readability formulae are based on generalized linear models (GLMs; e.g., discriminant analysis and multiple regression), but these models have to comply with certain statistical assumptions about data properties and include all of the data in formulae construction without pruning the outliers in advance. The use of such readability formulae tends to produce a low text classification accuracy, while using a support vector machine (SVM) in machine learning can enhance the classification outcome. The present study constructed readability models by integrating multilevel linguistic features with SVM, which is more appropriate for text classification. Taking the Chinese language as an example, this study developed 31 linguistic features as the predicting variables at the word, semantic, syntax, and cohesion levels, with grade levels of texts as the criterion variable. The study compared four types of readability models by integrating unilevel and multilevel linguistic features with GLMs and an SVM. The results indicate that adopting a multilevel approach in readability analysis provides a better representation of the complexities of both texts and the reading comprehension process.
We compared the ability of three different contextual models of lexical semantic memory (BEAGLE, Latent Semantic Analysis, and the Topic model) and of a simple associative model (POC) to predict the properties of semantic networks derived from word association norms. None of the semantic models were able to accurately predict all of the network properties. All three contextual models over-predicted clustering in the norms, whereas the associative model under-predicted clustering. Only a hybrid model that assumed that some of the responses were based on a contextual model and others on an associative network (POC) successfully predicted all of the network properties and predicted a word's top five associates as well as or better than the better of the two constituent models. The results suggest that participants switch between a contextual representation and an associative network when generating free associations. We discuss the role that each of these representations may play in lexical semantic memory. Concordant with recent multicomponent theories of semantic memory, the associative network may encode coordinate relations between concepts (e.g., the relation between pea and bean, or between sparrow and robin), and contextual representations may be used to process information about more abstract concepts.
The objective of this study was to explore dimensions of oral language and reading and their influence on reading comprehension in a relatively understudied population-adolescent readers in 4th through 10th grades. The current study employed latent variable modeling of decoding fluency, vocabulary, syntax, and reading comprehension so as to represent these constructs with minimal error and to examine whether residual variance unaccounted for by oral language can be captured by specific factors of syntax and vocabulary. A 1-, 3-, 4-, and bifactor model were tested with 1,792 students in 18 schools in 2 large urban districts in the Southeast. Students were individually administered measures of expressive and receptive vocabulary, syntax, and decoding fluency in mid-year. At the end of the year students took the state reading test as well as a group-administered, norm-referenced test of reading comprehension. The bifactor model fit the data best in all 7 grades and explained 72% to 99% of the variance in reading comprehension. The specific factors of syntax and vocabulary explained significant unique variance in reading comprehension in 1 grade each. The decoding fluency factor was significantly correlated with the reading comprehension and oral language factors in all grades, but, in the presence of the oral language factor, was not significantly associated with the reading comprehension factor. Results support a bifactor model of lexical knowledge rather than the 3-factor model of the Simple View of Reading, with the vast amount of variance in reading comprehension explained by a general oral language factor.
Therapist language plays a critical role in influencing the overall quality of psychotherapy. Notably, it is a major contributor to the perceived level of empathy expressed by therapists, a primary measure for judging their efficacy. We explore psycholinguistics inspired features for predicting therapist empathy. These features model language which conveys information about affective and cognitive processes, which is central to the therapist expressing understanding of the patient’s perspective. We describe the dimensional features obtained based on psycholinguisitic norms, and their application to predicting empathy expressed in motivational interviewing sessions for addiction counseling. We compare these to standard lexical features (n-grams) and demonstrate that these features contain complementary information for predicting therapist empathy. The highest empathy prediction results achieved are 75.28% UAR and 0.6112 Spearman’s correlation.
To judge how much a pair of words (or texts) are semantically related is acognitive process. However, previous algorithms for computing semanticrelatedness are largely based on co-occurrences within textualwindows, and do not actively leverage cognitive human perceptions ofrelatedness. To bridge this perceptional gap, we propose to utilizefree association as signals to capture such human perceptions.However, free association, being manually evaluated,has limited lexical coverage and is inherently sparse. We propose to expand lexical coverage and overcome sparseness by constructing an association network of terms and concepts that combines signals from free association norms and five types of co-occurrences extracted from therich structures of Wikipedia. Our evaluation results validate thatsimple algorithms on this network give competitive results incomputing semantic relatedness between words and between shorttexts.
There is, today, a powerful social norm against the expression of prejudice. Hence, as shown in many discursive studies, speakers treat prejudice as an accountable matter and use various strategies (e.g., disclaimers, mitigation, denials, and reformulations) to avoid being seen as personally prejudiced. Analysts have identified this practice as a “new” form of discriminatory discourse, which allows expression of prejudice without negative identity repercussions. Relevant studies are generally undertaken from a critical perspective and focus on structural inequalities (particularly race and gender). However, speakers may also demonstrate sensitivity around unexpected issues which lack overt prejudice connotations. This article examines one such example of unexpected sensitivity to the anti-prejudice norm. It analyses how five young female academics problematise and resolve their preference for an “intelligent” romantic partner. Their preference is uncontroversial in relationship terms, but here, in the academic context, it is clearly treated as accountable and as possibly inviting negative attributions. The data show functional and lexical features of “new” discriminatory discourse. The speakers orient towards attributions of intellectual elitism and use various means to deflect these, while ultimately upholding their stated preference for an intelligent partner. The analysis demonstrates how the anti-prejudice norm extends across settings/topics and how accountability is occasioned and context specific. This has implications for how prejudice itself, as a discursive construct, may be identified and evidenced. Specifically, it might be argued that analysts only have empirical access to accountability (occasioned in specific contexts), rather than to exclusionary or prejudiced ideologies per se.
The article studies such phenomenon of mass communication as the language of advertisement. The author gives definitions to such concepts as the “language of advertisement”, “text of advertisement”, and makes an attempt to differentiate between these concepts. The article presents a review of Russian and translated foreign literature researching this issue, outlines scientific areas which study advertising. The article describes basic characteristics peculiar for advertisement texts: both linguistic and extra-linguistic. It considers functional characteristics of advertisement text, goals for its creation, and the mission it has – to influence a consumer using various psychological, linguistic, visual (graphic) and other means. The language of advertisement is considered as a specific linguistic structure that develops according to its on laws, breaking sometimes standard norms to emphasize influence on the addressee because it pursues its own non-linguistic objectives. The main communicative aim of an advertisement is to force the consumer to choose the products, goods or services advertised. The article tells about specifics of advertising texts which consists of using both verbal and non-verbal elements regardless of the type of advertisement. Linguistic peculiarities of advertisement texts are studied in the article. The author distinguishes a number of linguistic means used in creation of an advertisement that are grouped as phonetic, lexical, syntactic, morphological, stylistic and the other. The main consideration is given to the use of stylistic and lexical linguistic means. In particular, the paper gives examples of using such linguistic means in advertisements as metaphor, hyperbola, personification, repetition, idiomatic expressions, neologism, jargon, etc.
Undoubtedly the difficulty of translating culture-bound elements will be be much more challenging when the audience are children who do not have any perspective on cultural diversity of different nations. The culture-bound elements can be consists of a wide range of elements, i.e. proper names, religion terms, food and drink items and so on. Dealing with each of these items will be a real challenge when translators have this perception that most probably their audiences do not have any idea about the in hand culturebound element, and it will be their choice to present the new items to the child reader or replace it with a familiar one. With this perspective, the present textual analysis study, aims to explore the lexical choices that translator's of children's literature in Iran made, facing such elements. The present effort restricts itself to the "food and drink" items and illustrates the way that Persian translators approach these culturebound elements in a 70 years period and discusses their lexical choices following the socio-cultural norms of the time.
The Language Hoax: Why the World Looks the Same in Any Language John H. McWhorter (2014) New York: Oxford University Press. Pp. 208. ISBN 978-0-19-936158-8After works like Word on the Street: Debunking the Myth of 'Pure' Standard English and The Power of Babel: A Natural History of Language, the volume under review continues the author's work first and foremost aimed at educating the general public about issues related to language and linguistics, a highly important task as I have noted on other occasions. This time it is a manifesto, as the author states in the first sentence of the Introduction: a manifesto which takes issue with the recent rise of Neo-Whorfianism and especially its popularized version divulgated by the media. After all, the Sapir-Whorf hypothesis according to which the language we speak shapes the way we perceive the world seems quite appealing and is, at first sight, also politically correct. In McWhorter's words: 'Under Whorfianism, everybody is interesting and everybody matters' (p. xvi). At the same time, it is also dangerous as it suggests that humans are not mentally alike. McWhorter's aim is to show that the 'idea of languages as pairs of glasses does not hold water in the way that we may, understandably, wish it did' (p. xvii). According to him, language is, indeed, a lens - but not upon distinct humanities but humanity in general - and languages are fascinating in their own right.The Introduction (pp. ix-xx) introduces the reader to the questions at hand: What is (Neo-) Whorfianism all about? What are the stakes? What is this book about? To some extent, I have outlined that in the preceding.In Chapter 1, 'Studies Have Shown' (pp. 3-29), McWhorter conscientiously revises recent Neo-Whorfian research which shows that language does have an effect on thought. However, this effect cannot be deemed anything but the like of insignificant milliseconds in hitting buttons during laboratory reaction tests gauging to what extent, for instance, speakers of Russian with lexical items for both 'dark blue' and 'light blue' are more sensitive to shades of blue than speakers of languages which lack this distinction. Thence, concluding that a tribe whose languages lacks numbers is bad at maths (the case of the Brazilian Piraha extensively reported upon by the media in 2004) is akin to considering that a 'tribe without cars doesn't drive' (p. 16) or 'Legless Tribe [is] Incapable of Walking Because They Have No Words for Walk' (p. 21). Rather than language shaping the way we see the world, it is culture and language-external realities which have an impact on language, for instance in the form of specific terminology such as Japanese honorifics.In order to save Whorfianism, we might fancy 'Having It Both Ways?' which is precisely the title of Chapter 2 (pp. 30-58). Unfortunately, that doesn't work either. There just is no intrinsic necessity for language A to develop, say, evidential markers, while language B, spoken in a very similar environment, does not. As McWhorter puts it: 'Worldwide, chance is, itself, the only real pattern evident in the link between languages and what their speakers are like' (p. 45). Or that bubbles (or frills or ornaments) just happen to pop up somewhere in the soup.A central point made by the author in Chapter 3, 'An Interregnum on Culture' (pp. 59-72), is that sociohistorical conditions have affected language structure throughout human history: witness, for example, the fact that languages massively acquired at some point in history as L2 such as English, Mandarin Chinese, Persian, Swahili, and Indonesian tend to be less complex than what is the norm for human language.The author returns to the currently much-debated concept of linguistic complexity in the next chapter, 'Dissing the Chinese' (pp. 73-103). As it happens, Whorfian studies have compared a limited set of grammatical features of 'National Geographic' languages with English. For the reasons elaborated in the previous chapter, the outcome is that those languages seemingly encode reality in a more elaborate or exotic way than English. …
We examine the nature of phonological and semantic similarity\nin early language learning. We consider how the use of this\ninformation might change over the course of development. To\nthis end, we represent the lexicon as either a phonological or\nsemantic network and model the growth of this network. Constructing\nnormative vocabularies from the Communicative Development\nInventory norms, we utilize a preferential attachment\ngrowth algorithm. We predict and quantify the words\nwhich will be learned next, comparing the two network representations.\nWe consider the effect of age, total vocabulary size\nand language ability as measured through CDI percentile. Our\nfindings suggest that the semantic representation does not outperform\nthe baseline bag-of-words model, whereas the phonological\nrepresentation conditionally does. More generally, we\nshow that the network representation influences the ability of\na model to capture vocabulary growth. We further offer a\nmethod of analysis for testing representational assumptions in\nnetwork models.
How does a child's vocabulary production change over time? Past research has often focused on characterizing population statistics of vocabulary growth. In this work, we develop models that attempt to predict when a specific word will be learned by a particular child. The models are based on two qualitatively different sources of information: a representation describing the child (age, sex, and quantifiers of vocabulary skill) and a representation describing the specific words a child knows. Using longitudinal data from children aged 15-36 months collected at the University of Colorado, we constructed logistic regression models to predict each month whether a word would be learned in the coming month. Models based on either the child representation or the word representation outperform a baseline model that utilizes population acquisition norms. Although the child- and word-representation models perform comparably, an ensemble that averages the predictions of the two separate models obtains significantly higher accuracy, indicating that the two sources of information are complementary. Through the exploration of such models, we gain an understanding of the factors that influence language learning, and this understanding should inform cognitive theories of development. On a practical level, these models may support the development of interventions to boost language acquisition.
The article is devoted to the conceptual analysis of Orthodontic Terminology. According to the basic positions of Cognitive Linguistics the concepts are nominated on the basis of the ideas about them and develop in the course of practical human activity. The category of Space is the main one for all sections of Orthodontics. On the basis of this fact, the main spatual (dimentional) components were observed, which are the main components of the conceptual framework and are more often find their linguistic objectification: the place, the form, the size. These components are very important in the Orthodontics consepts formation, because of the need in accurate diagnosis and treatment methods. The article investigates the traditional ways of term formation: morphological, lexical-semantic, syntactic and mixed.
In this work we suggest a novel Text Categorization (TC) scenario, motivated by an ad-hoc industrial need to assign documents to a set of predefined categories, while labeled training data for the categories is not available. The scenario is applicable in many industrial settings and is interesting from the academic perspective. We present a new dataset geared for the main characteristics of the scenario, and utilize it to investigate the name-based TC approach, which uses the category names as its only input and does not require training data. We evaluate and analyze the performance of state-of-the-art methods for this dataset to identify the shortcomings of these methods for our scenario, and suggest ways for overcoming these shortcomings. We utilize statistical correlation measured over a target corpus for improving the state-of-the-art, and offer a different classification scheme based on the characteristics of the setting. We evaluate our improvements and adaptations and show superior performance of our suggested method.
Automatic speech recognition (ASR) technology has matured over the past few decades and has made significant impacts in a variety of fields, from assistive technologies to commercial products. However, ASR system development is a resource intensive activity and requires language resources in the form of text annotated audio recordings and pronunciation dictionaries. Unfortunately, many languages found in the developing world fall into the resource-scarce category and due to this resource scarcity the deployment of ASR systems in the developing world is severely inhibited. One approach to assist with resource-scarce ASR system development, is to select “useful” training samples which could reduce the resources needed to collect new corpora. In this work, we propose a new data selection framework which can be used to design a speech recognition corpus. We show for limited data sets, independent of language and bandwidth, the most effective strategy for data selection is frequency-matched selection and that the widely-used maximum entropy methods generally produced the least promising results. In our model, the frequency-matched selection method corresponds to a logarithmic relationship between accuracy and corpus size; we also investigated other model relationships, and found that a hyperbolic relationship (as suggested from simple asymptotic arguments in learning theory) may lead to somewhat better performance under certain conditions.
BACKGROUND: Impaired emotion regulation is increasingly recognized as a core feature of depressive disorders. Indeed, currently and previously depressed adults both report greater problems in attenuating sadness (mood repair) in daily life than healthy controls. In contrast, studies of various strategies to attenuate sad affect have mostly found that currently or previously depressed adults and controls were similarly successful at mood repair in the laboratory. But few studies have examined mood repair among depression-prone youths or the effects of trait characteristics on mood repair outcomes in the laboratory. METHODS: Adolescents, whose first episode of major depressive disorder (MDD) had onset at age 9, on average (probands), and were either in remission or depressed, and control peers, watched a sad film clip. Then, they were instructed to engage in refocusing attention (distraction) or recalling happy memories. Using affect ratings provided by the youths, we tested two developmentally informed hypotheses about whether the subject groups would be similarly able to attenuate sadness via the two mood repair strategies. We also explored if self-reported habitual (trait) mood repair influenced laboratory performance. RESULTS: Contrary to expectations, attention refocusing and recall of happy memories led to comparable mood benefits across subjects. Control adolescents reported significantly greater reductions in sadness than did depressed (Cohen's d =.48) or remitted (Cohen's d =.32) probands, regardless of mood repair strategy, while currently depressed probands remained the saddest after mood repair. Habitual mood repair styles moderated the effects of instructed (state) mood repair in the laboratory. CONCLUSIONS: Whether depressed or in remission, adolescents with MDD histories are not as efficient at mood repair in the laboratory as controls. But proband-control group differences in mood repair outcomes were modest in scope, suggesting that the abilities that subserve affect regulation have been preserved in probands to some degree. Further information about the nature of mood repair problems among youths with depression histories would help to better understand the clinical course of MDD and to design personalized interventions for depression.
Within the Spanish educational context, one of the most loyal and indisputable allies for teaching and learning foreign languages is the textbook. Several studies have been conducted on particular aspects of textbooks (Aguirre Lora, 2001; Escaño, 2013; Autor, 2012; Jiménez Catalán, 2003; Nodarse González & Mons Obermayer, 2013), but few of them carry out a holistic, diachronic and multimodal analysis. This study verifies the adequacy of textbooks for teaching and learning English as a foreign language to adults in a non-immersion context. Specifically, it analyzes the lexical level of the selected samples, the skills demanded by the activities, and the distribution of the lessons’ elements according to their semantics. The analytical tools used in the study include the lexical database English Vocabulary Profile, Bloom’s taxonomy (1956) and the principles of visual composition developed by Kress and van Leeuwen (2006). The main conclusions that can be drawn from this document are: the adequacy of the lexical level through the establishment of official standards, the common practice of lower order thinking skills rather than the higher ones, and the distribution of the elements around the horizontal axis, placing the already-known information on the left side and the new information on the right side. How to reference this article González Romero, R. (2015). Análisis holístico, diacrónico y multimodal de libros de texto de inglés como lengua extranjera: Una nueva forma de mejorar la comprensión. Foro de Educación, 13(19), 343-356. doi: http://dx.doi.org/10.14516/fde.2015.013.019.015
Presentations given at the workshop Language Comparison with Linguistic Databases (LanCLiD 2), held on 30 April 2015 at the Department of Linguistics of the Max Planck Institute for Evolutionary Anthropology (Leipzig, Germany).
International audience
Recent years have seen an increased interest in and availability of many different kinds of corpora. These range from small, but carefully annotated treebanks to large parallel corpora and very lar...
This paper analyses several points of interlingual dependency mismatch on the material of a parallel Czech-English dependency treebank. Particularly, the points of alignment mismatch between the valency frame arguments of the corresponding verbs are observed and described. The attention is drawn to the question whether such mismatches stem from the inherent semantic properties of the individual languages, or from the character of the used linguistic theory. Comments are made on the possible shifts in meaning. The authors use the findings to make predictions about possible machine translation implementation of the data.
Multiword expressions (MWEs) present particular and distinctive semantic properties, hence their automatic extraction receives special attention from the natural language processing (NLP) and corpus linguistics community, and is still an active research area. Unfortunately, the creation of necessary resources for this task is quite rigorous and many languages suffer from the lack of these; as in the case for Turkish.