Identifying linguistic correlates of power

Previous work on social power modelling from linguistic cues has been limited by the range of available data. We introduce a new corpus of dialogues, generated in a controlled experimental setting where participant roles were manipulated to generate a perceived difference in social power. Initial results demonstrate successful differentiation of upwards, downwards, and level communications, using a classifier built on a small set of stylistic features

All mixed up? Finding the optimal feature set for general readability prediction and its application to English and Dutch

Readability research has a long and rich tradition, but there has been too little focus on general readability prediction without targeting a specific audience or text genre. Moreover, though NLP-inspired research has focused on adding more complex readability features there is still no consensus on which features contribute most to the prediction. In this article, we investigate in close detail the feasibility of constructing a readability prediction system for English and Dutch generic text using supervised machine learning. Based on readability assessments by both experts and a crowd, we implement different types of text characteristics ranging from easy-to-compute superficial text characteristics to features requiring a deep linguistic processing, resulting in ten different feature groups. Both a regression and classification setup are investigated reflecting the two possible readability prediction tasks: scoring individual texts or comparing two texts. We show that going beyond correlation calculations for readability optimization using a wrapper-based genetic algorithm optimization approach is a promising task which provides considerable insights in which feature combinations contribute to the overall readability prediction. Since we also have gold standard information available for those features requiring deep processing we are able to investigate the true upper bound of our Dutch system. Interestingly, we will observe that the performance of our fully-automatic readability prediction pipeline is on par with the pipeline using golden deep syntactic and semantic information

Political Text Scaling Meets Computational Semantics

During the last fifteen years, automatic text scaling has become one of the key tools of the Text as Data community in political science. Prominent text scaling algorithms, however, rely on the assumption that latent positions can be captured just by leveraging the information about word frequencies in documents under study. We challenge this traditional view and present a new, semantically aware text scaling algorithm, SemScale, which combines recent developments in the area of computational linguistics with unsupervised graph-based clustering. We conduct an extensive quantitative analysis over a collection of speeches from the European Parliament in five different languages and from two different legislative terms, and show that a scaling approach relying on semantic document representations is often better at capturing known underlying political dimensions than the established frequency-based (i.e., symbolic) scaling method. We further validate our findings through a series of experiments focused on text preprocessing and feature selection, document representation, scaling of party manifestos, and a supervised extension of our algorithm. To catalyze further research on this new branch of text scaling methods, we release a Python implementation of SemScale with all included data sets and evaluation procedures.Comment: Updated version - accepted for Transactions on Data Science (TDS

Beyond the clinic? Eluding a medical diagnosis of anorexia through narrative

The persistence and recurrence of anorexia nervosa poses a clinical challenge, and provides support for critiques of oppressive and injurious facets of society inscribed on women’s bodies. This essay illustrates how a phenomenological, linguistic anthropological approach fruitfully traverses clinical and cultural perspectives by directing attention beyond the embodied experience of patients diagnosed with anorexia nervosa to those who are not clinically diagnosed. Extending a model of illness and recovery as entailing sufferers’ emplotting of past, present, and imagined future selves, I argue that women’s accounts of their experiences do not simply reflect lived reality, but actually propel health-relevant states of being by enlivening and creating these realities in the process of their telling. In indexical interaction with public and clinical discourses, narratives’ grammar, lexicon, and plot structures modify subjects’ experiences and interpretations of the events and feelings recounted. This article builds on the insight that linear narratives of “full recovery” that adopt a clinical and feminist voice can help tellers stay recovered, whereas for those “struggling to recover,” a genre of contingent, uncertain, sideshadowing narratives alternatively renders recovery an elusive and ambivalently desired object. This essay then identifies a third narrative genre, eluding a diagnosis, which combines elements of the first two genres to paradoxically keep its teller simultaneously sheltered from, and invisible to the well-meaning clutches of medical care, leaving her suffering, yet free, to starve. This focus on narrative genres illustrates the utility of linguistic analyses for discerning and interpreting distress in subclinical populations.First author draf

Computational Sociolinguistics: A Survey

Language is a social phenomenon and variation is inherent to its social nature. Recently, there has been a surge of interest within the computational linguistics (CL) community in the social dimension of language. In this article we present a survey of the emerging field of "Computational Sociolinguistics" that reflects this increased interest. We aim to provide a comprehensive overview of CL research on sociolinguistic themes, featuring topics such as the relation between language and social identity, language use in social interaction and multilingual communication. Moreover, we demonstrate the potential for synergy between the research communities involved, by showing how the large-scale data-driven methods that are widely used in CL can complement existing sociolinguistic studies, and how sociolinguistics can inform and challenge the methods and assumptions employed in CL studies. We hope to convey the possible benefits of a closer collaboration between the two communities and conclude with a discussion of open challenges.Comment: To appear in Computational Linguistics. Accepted for publication: 18th February, 201

Chained activation of the motor system during language understanding

Two experiments were carried out to investigate whether and how one important characteristic of the motor system, that is its goal-directed organization in motor chains, is reflected in language processing. This possibility stems from the embodied theory of language, according to which the linguistic system re-uses the structures of the motor system. The participants were presented with nouns of common tools preceded by a pair of verbs expressing grasping or observational motor chains (i.e., grasp-to-move, grasp-to-use, look-at-to-grasp, and look-at-to-stare). They decided whether the tool mentioned in the sentence was the same as that displayed in a picture presented shortly after. A primacy of the grasp-to-use motor chain over the other motor chains in priming the participants' performance was observed in both the experiments. More interestingly, we found that the motor information evoked by the noun was modulated by the specific motor-chain expressed by the preceding verbs. Specifically, with the grasping chain aimed at using the tool, the functional motor information prevailed over the volumetric information, and vice versa with the grasping chain aimed at moving the tool (Experiment 2). Instead, the functional and volumetric information were balanced for those motor chains that comprise at least an observational act (Experiment 1). Overall our results are in keeping with the embodied theory of language and suggest that understanding sentences expressing an action directed toward a tool drives a chained activation of the motor system

The translation of food-related culture-specific items in the COVALT corpus: A study of techniques and factors

This article aims to analyse the translation of food-related culturespecific items (CSI) in the English–Catalan subcorpus of the Valencian Corpus of Translated Literature (COVALT). This general aim can be broken down into two specific aims: to find out what techniques prevail in the translation of these cultural items, and to determine what factors influence the choice of specific techniques. Corpus analysis is carried out by means of the Corpus Query Processor. The theoretical framework deals with the definition and scope of the concept of CSI, the classifications of techniques put forward in the literature for the translation of CSI, and the position of food- and drink-related elements within the broader category of CSI. Analysis of the results yielded by the corpus shows that neutralising techniques prevail over foreignising and domesticating ones, with the latter coming last in descending order. The most prominent factors identified are nonexistence of the source text (ST) item in the target culture, different degrees of institutionalisation, the ST item having been imported into the target culture, and different degrees of granularity. Correlations between techniques and factors are never very strong, but some are strong enough to deserve further attention

Narrative Language as an Expression of Individual and Group Identity

Scientific Narrative Psychology integrates quantitative methodologies into the study of identity. Its methodology, Narrative Categorical Analysis, and its toolkit, NarrCat, were both originally developed by the Hungarian Narrative Psychology Group. NarrCat is for machine-made transformation of sentences in self-narratives into psychologically relevant, statistically processable narrative categories. The main body of this flexible and comprehensive system is formed by Psycho-Thematic modules, such as Agency, Evaluation, Emotion, Cognition, Spatiality, and Temporality. The Relational Modules include Social References, Semantic Role Labeling (SRL), and Negation. Certain elements can be combined into Hypermodules, such as Psychological Perspective and Spatio-Temporal Perspective, which allow for even more complex, higher level exploration of composite psychological processes. Using up-to-date developments of corpus linguistics and Natural Language Processing (NLP), a unique feature of NarrCat is its capacity of SRL. The structure of NarrCat, as well as the empirical results in group identity research, is discussed

The development of emotion recognition from facial expressions and non-linguistic vocalizations during childhood

Sensitivity to facial and vocal emotion is fundamental to children's social competence. Previous research has focused on children's facial emotion recognition, and few studies have investigated non-linguistic vocal emotion processing in childhood. We compared facial and vocal emotion recognition and processing biases in 4- to 11-year-olds and adults. Eighty-eight 4- to 11-year-olds and 21 adults participated. Participants viewed/listened to faces and voices (angry, happy, and sad) at three intensity levels (50%, 75%, and 100%). Non-linguistic tones were used. For each modality, participants completed an emotion identification task. Accuracy and bias for each emotion and modality were compared across 4- to 5-, 6- to 9- and 10- to 11-year-olds and adults. The results showed that children's emotion recognition improved with age; preschoolers were less accurate than other groups. Facial emotion recognition reached adult levels by 11 years, whereas vocal emotion recognition continued to develop in late childhood. Response bias decreased with age. For both modalities, sadness recognition was delayed across development relative to anger and happiness. The results demonstrate that developmental trajectories of emotion processing differ as a function of emotion type and stimulus modality. In addition, vocal emotion processing showed a more protracted developmental trajectory, compared to facial emotion processing. The results have important implications for programmes aiming to improve children's socio-emotional competence