Gram-positive anaerobe <i>cocci </i>(GPAC) are underrepresented in the microbiome of filaggrin-deficient human skin

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Irony Detection in Twitter: The Role of Affective Content

© ACM 2016. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM Transactions on Internet Technology, Vol. 16. http://dx.doi.org/10.1145/2930663.[EN] Irony has been proven to be pervasive in social media, posing a challenge to sentiment analysis systems. It is a creative linguistic phenomenon where affect-related aspects play a key role. In this work, we address the problem of detecting irony in tweets, casting it as a classification problem. We propose a novel model that explores the use of affective features based on a wide range of lexical resources available for English, reflecting different facets of affect. Classification experiments over different corpora show that affective information helps in distinguishing among ironic and nonironic tweets. Our model outperforms the state of the art in almost all cases.The National Council for Science and Technology (CONACyT Mexico) has funded the research work of Delia Irazu Hernandez Farias (Grant No. 218109/313683 CVU-369616). The work of Viviana Patti was partially carried out at the Universitat Politecnica de Valencia within the framework of a fellowship of the University of Turin cofunded by Fondazione CRT (World Wide Style Program 2). The work of Paolo Rosso has been partially funded by the SomEMBED TIN2015-71147-C2-1-P MINECO research project and by the Generalitat Valenciana under the grant ALMAMATER (PrometeoII/2014/030).Hernandez-Farias, DI.; Patti, V.; Rosso, P. (2016). Irony Detection in Twitter: The Role of Affective Content. ACM Transactions on Internet Technology. 16(3):19:1-19:24. https://doi.org/10.1145/2930663S19:119:24163Rob Abbott, Marilyn Walker, Pranav Anand, Jean E. Fox Tree, Robeson Bowmani, and Joseph King. 2011. 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When Correction Turns Positive:Processing Corrective Prosody in Dutch

Current research on spoken language does not provide a consistent picture as to whether prosody, the melody and rhythm of speech, conveys a specific meaning. Perception studies show that English listeners assign meaning to prosodic patterns, and, for instance, associate some accents with contrast, whereas Dutch listeners behave more controversially. In two ERP studies we tested how Dutch listeners process words carrying two types of accents, which either provided new information (new information accents) or corrected information (corrective accents), both in single sentences (experiment 1) and after corrective and new information questions (experiment 2). In both experiments corrective accents elicited a sustained positivity as compared to new information accents, which started earlier in context than in single sentences. The positivity was not modulated by the nature of the preceding question, suggesting that the underlying neural mechanism likely reflects the construction of an interpretation to the accented word, either by identifying an alternative in context or by inferring it when no context is present. Our experimental results provide strong evidence for inferential processes related to prosodic contours in Dutch

Attention allocation in a language with post-focal prominences

Accentuation influences selective attention and the depth of semantic processing during online speech comprehension. We investigated the processing of semantically congruent and incongruent words in a language that presents cues to prosodic prominences in the region of the utterance occurring after the focussed information (the post-focal region). This language is Italian, in particular the variety spoken in Bari. In this variety, questions have a compressed, post-focal accent, whereas in statements there is a low-level pitch in this position. Using event-related potentials, we investigated the processing of congruent and incongruent target words with two prosodic realizations (focussed with accentuation, post-focal realization) and in two-sentence modalities (statement, question). Results indicate an N400 congruence effect that was modulated by position (focal, post-focal) and modality (statement, question): processing was deeper for questions in narrow focus than in post-focal position, while statements showed simila

Human beta-defensin 3 is up-regulated in cutaneous leprosy type 1 reactions.

BACKGROUND: Leprosy, a chronic granulomatous disease affecting the skin and nerves, is caused by Mycobacterium leprae (M. leprae). The type of leprosy developed depends upon the host immune response. Type 1 reactions (T1Rs), that complicate borderline and lepromatous leprosy, are due to an increase in cell-mediated immunity and manifest as nerve damage and skin inflammation. Owing to the increase in inflammation in the skin of patients with T1Rs, we sought to investigate the activation of the innate immune system during reactionary events. Specifically, we investigated the expression levels of human beta-defensins (hBDs) 2 and 3 in the skin of patients with T1Rs, in keratinocytes, and in macrophages stimulated with M. leprae and corticosteroids. RESULTS: Skin biopsies from twenty-three patients with Type 1 reactions were found to have higher transcript levels of hBD3 as compared to fifteen leprosy patients without Type 1 reactions, as measured by qPCR. Moreover, we observed that keratinocytes but not macrophages up-regulated hBD2 and hBD3 in response to M. leprae stimulation in vitro. Corticosteroid treatment of patients with T1Rs caused a suppression of hBD2 and hBD3 in skin biopsies, as measured by qPCR. In vitro, corticosteroids suppressed M. leprae-dependent induction of hBD2 and hBD3 in keratinocytes. CONCLUSIONS: This study demonstrates that hBD3 is induced in leprosy Type 1 Reactions and suppressed by corticosteroids. Furthermore, our findings demonstrate that keratinocytes are responsive to M. leprae and lend support for additional studies on keratinocyte innate immunity in leprosy and T1Rs. TRIAL REGISTRATION: Controlled-Trials.com ISRCTN31894035

Introduction to the special issue on Language in Social Media: Exploiting discourse and other contextual information

International audienceSocial media content is changing the way people interact with each other and share information, personal messages, and opinions about situations, objects, and past experiences. Most social media texts are short online conversational posts or comments that do not contain enough information for natural language processing (NLP) tools, as they are often accompanied by non-linguistic contextual information, including meta-data (e.g., the user’s profile, the social network of the user, and their interactions with other users). Exploiting such different types of context and their interactions makes the automatic processing of social media texts a challenging research task. Indeed, simply applying traditional text mining tools is clearly sub-optimal, as, typically, these tools take into account neither the interactive dimension nor the particular nature of this data, which shares properties with both spoken and written language. This special issue contributes to a deeper understanding of the role of these interactions to process social media data from a new perspective in discourse interpretation. This introduction first provides the necessary background to understand what context is from both the linguistic and computational linguistic perspectives, then presents the most recent context-based approaches to NLP for social media. We conclude with an overview of the papers accepted in this special issue, highlighting what we believe are the future directions in processing social media texts