An Effectiveness Metric for Ordinal Classification: Formal Properties and Experimental Results

In Ordinal Classification tasks, items have to be assigned to classes that have a relative ordering, such as positive, neutral, negative in sentiment analysis. Remarkably, the most popular evaluation metrics for ordinal classification tasks either ignore relevant information (for instance, precision/recall on each of the classes ignores their relative ordering) or assume additional information (for instance, Mean Average Error assumes absolute distances between classes). In this paper we propose a new metric for Ordinal Classification, Closeness Evaluation Measure, that is rooted on Measurement Theory and Information Theory. Our theoretical analysis and experimental results over both synthetic data and data from NLP shared tasks indicate that the proposed metric captures quality aspects from different traditional tasks simultaneously. In addition, it generalizes some popular classification (nominal scale) and error minimization (interval scale) metrics, depending on the measurement scale in which it is instantiated.Comment: To appear in Proceedings of ACL 202

Sistemas de Acceso Inteligente a la Información Biomédica: una revisión

Modern medical environment is characterized by the work overload and the lack of time. In such an environment, intelligent information access systems can undoubtedly assist the work of physicians and researchers. Nonetheless, despite their benefits, clinical information systems barely include these technologies. Several are the reasons. First, the potential of these strategies will only be achieved once health care professionals become familiar with them. Second, it is necessary to progress in the development of patient information standards, as well as in the use of an unified and controlled terminology. Even if important advances have been reached during the last decade, there is still much work to do.En un entorno como el de la medicina, caracterizado por la sobrecarga de trabajo y la escasez de tiempo, los sistemas inteligentes de acceso a la información pueden y deben utilizarse para facilitar la labor de investigadores y profesionales. Sin embargo, sorprende comprobar la escasa implantación de estos sistemas. Las razones son varias. En primer lugar, el potencial completo de estas estrategias sólo se alcanzará cuando la informática esté completamente integrada en la práctica médica. En segundo lugar, todavía es necesario avanzar en la estandarización de la estructura y el contenido de la información de los pacientes, así como en el uso de una terminología unificada y controlada. Aunque, especialmente durante la última década, los avances en ambos sentidos han sido considerables, lo cierto es que todavía queda mucho camino por recorrer

Biophysical and Biochemical Comparison of Extracellular Vesicles Produced by Infective and Non-Infective Stages of Trypanosoma cruzi

Extracellular vesicles (EVs) are small lipid vesicles released by either any prokaryotic or eukaryotic cell, or both, with a biological role in cell-to-cell communication. In this work, we characterize the proteomes and nanomechanical properties of EVs released by tissue-culture cell-derived trypomastigotes (mammalian infective stage; (TCT)) and epimastigotes (insect stage; (E)) of Trypanosoma cruzi, the etiologic agent of Chagas disease. EVs of each stage were isolated by differential centrifugation and analyzed using liquid chromatography with tandem mass spectrometry (LC-MS/MS), dynamic light scattering (DLS), nanoparticle tracking analysis (NTA), electron microscopy and atomic force microscopy (AFM). Measurements of zeta-potential were also included. Results show marked differences in the surface molecular cargos of EVs between both stages, with a noteworthy expansion of all groups of trans-sialidase proteins in trypomastigote’s EVs. In contrast, chromosomal locations of trans-sialidases of EVs of epimastigotes were dramatically reduced and restricted to subtelomeric regions, indicating a possible regulatable expression of these proteins between both stages of the parasite. Regarding mechanical properties, EVs of trypomastigotes showed higher adhesion compared to the EVs of epimastigotes. These findings demonstrate the remarkable surface remodeling throughout the life cycle of T. cruzi, which shapes the physicochemical composition of the extracellular vesicles and could have an impact in the ability of these vesicles to participate in cell communication in completely different niches of infection.ERANet programInstituto Carlos IIIMinisterio de Sanidad, Gobierno de EspañaFundación Ramón Areces funded the projects: “Research in prevention of congenital Chagas disease: parasitological, placental and immunological markers” (ERANet17/ HLH-0142 (Cochaco)ERANE-LAC HD 328/2014) and “Interactoma de las exovesículas de T. cruzi y de los inmunocomplejos que forman con las células del hospedador: implicaciones en la patología de la enfermedad de Chagas (2019)”Ministerio de Ciencia y Tecnología of the government of Spain funded the project PGC2018-099424-B-I0

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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Semi-supervised recognition of sarcastic sentences in twitter and amazon. In Proceedings of the 14th Conference on Computational Natural Language Learning (CoNLL’10). Association for Computational Linguistics, Uppsala, Sweden, 107--116.Shelly Dews, Joan Kaplan, and Ellen Winner. 1995. Why not say it directly? The social functions of irony. Discourse Processes 19, 3 (1995), 347--367.Paul Ekman. 1992. An argument for basic emotions. Cognition and Emotion 6, 3--4 (1992), 169--200.Elisabetta Fersini, Federico Alberto Pozzi, and Enza Messina. 2015. Detecting irony and sarcasm in microblogs: The role of expressive signals and ensemble classifiers. In 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA’15). IEEE Xplore Digital Library, Paris, France, 1--8.Elena Filatova. 2012. Irony and sarcasm: Corpus generation and analysis using crowdsourcing. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC’12). 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Impaired “affective theory of mind” is associated with right ventromedial prefrontal damage. Cognitive Behavioral Neurology 18, 1 (2005), 55--67.Philip J. Stone and Earl B. Hunt. 1963. A computer approach to content analysis: Studies using the general inquirer system. In Proceedings of the May 21-23, 1963, Spring Joint Computer Conference (AFIPS’63 (Spring)). ACM, New York, NY, 241--256.Emilio Sulis, Delia Irazú Hernández Farías, Paolo Rosso, Viviana Patti, and Giancarlo Ruffo. 2016. Figurative messages and affect in Twitter: Differences between #irony, #sarcasm and #not. Knowledge-Based Systems. In Press. Available online.Maite Taboada and Jack Grieve. 2004. Analyzing appraisal automatically. In Proceedings of the AAAI Spring Symposium on Exploring Attitude and Affect in Text: Theories and Applications. AAAI, Stanford, CA, 158--161.Yi-jie Tang and Hsin-Hsi Chen. 2014. Chinese irony corpus construction and ironic structure analysis. 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Wang. 2013. #Irony or #sarcasm—a quantitative and qualitative study based on twitter. In Proceedings of the 27th Pacific Asia Conference on Language, Information, and Computation (PACLIC’13). Department of English, National Chengchi University, Taipei, Taiwan, 349--356.Juanita M. Whalen, Penny M. Pexman, J. Alastair Gill, and Scott Nowson. 2013. Verbal irony use in personal blogs. Behaviour & Information Technology 32, 6 (2013), 560--569.Cynthia Whissell. 2009. Using the revised dictionary of affect in language to quantify the emotional undertones of samples of natural languages. Psychological Reports 2, 105 (2009), 509--521.Deirdre Wilson and Dan Sperber. 1992. On verbal irony. Lingua 87, 1--2 (1992), 53--76.Theresa Wilson, Janyce Wiebe, and Paul Hoffmann. 2005. Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing (HLT’05). 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Search for supersymmetry in events with large missing transverse momentum, jets, and at least one tau lepton in 20 fb−1 of √s=8 TeV proton-proton collision data with the ATLAS detector

A search for supersymmetry (SUSY) in events with large missing transverse momentum, jets, at least one hadronically decaying tau lepton and zero or one additional light leptons (electron/muon), has been performed using 20.3fb−1 of proton-proton collision data at √s= 8 TeV recorded with the ATLAS detector at the Large Hadron Collider. No excess above the Standard Model background expectation is observed in the various signal regions and 95% confidence level upper limits on the visible cross section for new phenomena are set. The results of the analysis are interpreted in several SUSY scenarios, significantly extending previous limits obtained in the same final states. In the framework of minimal gauge-mediated SUSY breaking models, values of the SUSY breaking scale Λ below 63 TeV are excluded, independently of tan β. Exclusion limits are also derived for an mSUGRA/CMSSM model, in both the R-parity-conserving and R-parity-violating case. A further interpretation is presented in a framework of natural gauge mediation, in which the gluino is assumed to be the only light coloured sparticle and gluino masses below 1090 GeV are excluded

REC: Sistema automático de dirección cinematográfica en entornos virtuales basado en emociones

Master en Investigación en Informática, Facultad de Informática, Departamento de Ingeniería del Software e Inteligencia Artificial , curso 2007-2008Depto. de Ingeniería de Software e Inteligencia Artificial (ISIA)Fac. de InformáticaTRUEunpu

Feature engineering for sentiment analysis in e-health forums.

IntroductionExploiting information in health-related social media services is of great interest for patients, researchers and medical companies. The challenge is, however, to provide easy, quick and relevant access to the vast amount of information that is available. One step towards facilitating information access to online health data is opinion mining. Even though the classification of patient opinions into positive and negative has been previously tackled, most works make use of machine learning methods and bags of words. Our first contribution is an extensive evaluation of different features, including lexical, syntactic, semantic, network-based, sentiment-based and word embeddings features to represent patient-authored texts for polarity classification. The second contribution of this work is the study of polar facts (i.e. objective information with polar connotations). Traditionally, the presence of polar facts has been neglected and research in polarity classification has been bounded to opinionated texts. We demonstrate the existence and importance of polar facts for the polarity classification of health information.Material and methodsWe annotate a set of more than 3500 posts to online health forums of breast cancer, crohn and different allergies, respectively. Each sentence in a post is manually labeled as "experience", "fact" or "opinion", and as "positive", "negative" and "neutral". Using this data, we train different machine learning algorithms and compare traditional bags of words representations with word embeddings in combination with lexical, syntactic, semantic, network-based and emotional properties of texts to automatically classify patient-authored contents into positive, negative and neutral. Beside, we experiment with a combination of textual and semantic representations by generating concept embeddings using the UMLS Metathesaurus.ResultsWe reach two main results: first, we find that it is possible to predict polarity of patient-authored contents with a very high accuracy (≈ 70 percent) using word embeddings, and that this considerably outperforms more traditional representations like bags of words; and second, when dealing with medical information, negative and positive facts (i.e. objective information) are nearly as frequent as negative and positive opinions and experiences (i.e. subjective information), and their importance for polarity classification is crucial