Suchergebnisse

Il contributo si sofferma sugli studi di Rosaldo Ordano relativi alle origini dell'università di Vercelli nel medioevo. Nei suoi saggi Ordano ha sottolineato in particolare le condizioni politiche ed economiche del comune di Vercelli, nelle quali sarebbero da cercare per principali motivazioni che avrebbero spinto la città alla fondazione di uno Studium generale (1228).

73 92 52 1 ; S ; This is the author's version of a work that was accepted for publication in Information Processing and Management. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Information Processing and Management 52 (2016) 73–92. DOI 10.1016/j.ipm.2015.06.003. [EN] In this paper, we investigate the impact of emotions on author profiling, concretely identifying age and gender. Firstly, we propose the EmoGraph method for modelling the way people use the language to express themselves on the basis of an emotion-labelled graph. We apply this representation model for identifying gender and age in the Spanish partition of the PAN-AP-13 corpus, obtaining comparable results to the best performing systems of the PAN Lab of CLEF. © 2015 Elsevier B.V. All rights reserved. The work of the first author was partially funded by Autoritas Consulting SA and by Spanish Ministry of Economics under grant ECOPORTUNITY IPT-2012-1220-430000. The work of the second author was carried out in the framework of the WIQ-EI IRSES project (Grant No. 269180) within the FP 7 Marie Curie, the DIANA APPLICATIONS: Finding Hidden Knowledge in Texts: Applications (TIN2012-38603-C02-01) project and the VLC/CAMPUS Microcluster on Multimodal Interaction in Intelligent Systems. A special mention to Maria Dolores Rangel Pardo for her linguistic contribution to this investigation. Rangel-Pardo, FM.; Rosso, P. (2016). On the Impact of Emotions on Author Profiling. Information Processing and Management. 52(1):73-92. https://doi.org/10.1016/j.ipm.2015.06.003

223 226 51 ; Senia ; DIANA is a coordinated Project involving the research group of Ingeniería del Lenguaje Natural y Reconocimiento de Formas (ELiRF) of the Universitat Politècnica de València and the research group of Centre de Llenguatge i Computació (CliC) of the Universitat de Barcelona. This is an R&D project (TIN2012-38603) funded by the Spanish Ministry of Economy and Competitiveness. Paolo Rosso coordinates the DIANA project and leads the subproject DIANA-Applications and M. Antònia Marti leads the DIANA-Constructions subproject. ; DIANA es un proyecto coordinado en el que participan el grupo de Ingeniería del Lenguaje Natural y Reconocimiento de Formas (ELiRF) de la Universitat Politècnica de València y el grupo Centre de Llenguatge i Computació (CLiC) de la Universitat de Barcelona. Se trata de un proyecto del programa de I+D (TIN2012-38603) financiado por el Ministerio de Economía y Competitividad. Paolo Rosso coordina el proyecto DIANA y lidera el subproyecto DIANA-Applications y M. Antònia Martí lidera el subproyecto DIANA-Constructions. Rosso, P.; Martí, A.; Taulé, M. (2013). DIANA: Análisis del discurso para la comprensión del conocimiento. Procesamiento del Lenguaje Natural. 51:223-226. http://hdl.handle.net/10251/38329

[EN] Interest has grown around the classification of stance that users assume within online debates in recent years. Stance has been usually addressed by considering users posts in isolation, while social studies highlight that social communities may contribute to influence users¿ opinion. Furthermore, stance should be studied in a diachronic perspective, since it could help to shed light on users¿ opinion shift dynamics that can be recorded during the debate. We analyzed the political discussion in UK about the BREXIT referendum on Twitter, proposing a novel approach and annotation schema for stance detection, with the main aim of investigating the role of features related to social network community and diachronic stance evolution. Classification experiments show that such features provide very useful clues for detecting stance. ; The work of P. Rosso was partially funded by the Spanish MICINN under the research projects MISMIS-FAKEnHATE on Misinformation and Miscommunication in social media: FAKE news and HATE speech(PGC2018-096212-B-C31) and PROMETEO/2019/121 (DeepPattern) of the Generalitat Valenciana. The work of V. Patti and G. Ruffo was partially funded by Progetto di Ateneo/CSP 2016 Immigrants, Hate and Prejudice in Social Media (S1618 L2 BOSC 01). ; Lai, M.; Patti, V.; Ruffo, G.; Rosso, P. (2020). #Brexit: Leave or Remain? The Role of User's Community and Diachronic Evolution on Stance Detection. Journal of Intelligent & Fuzzy Systems. 39(2):2341-2352. https://doi.org/10.3233/JIFS-179895 ; S ; 2341 ; 2352 ; 39 ; 2 ; Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008. doi:10.1088/1742-5468/2008/10/p10008 ; Deitrick, W., & Hu, W. (2013). Mutually Enhancing Community Detection and Sentiment Analysis on Twitter Networks. Journal of Data Analysis and Information Processing, 01(03), 19-29. doi:10.4236/jdaip.2013.13004 ; Duranti A. and Goodwin C. , ...

[EN] We present a corpus of Spanish tweets of 15 Twitter accounts of politicians of the main five parties (PSOE, PP, Cs, UP and VOX) covering the campaign of the Spanish election of 10th November 2019 (10N Spanish Election). We perform a semi-automatic annotation of domainspecific topics using a mixture of keyword-based and supervised techniques. In this preliminary study we extracted the tweets of few politicians of each party with the aim to analyse their official communication strategy. Moreover, we analyse sentiments and emotions employed in the tweets. Although the limited size of the Twitter corpus due to the very short time span, we hope to provide with some first insights on the communication dynamics of social network accounts of these five Spanish political parties. ; The work of the authors from the Universitat Politecnica de Valencia was funded by the Spanish MICINN under the research project MISMISFAKEnHATE on Misinformation and Miscommunication in social media: FAKE news and HATE speech (PGC2018-096212-B-C31). ; Sánchez-Junquera, J.; Ponzetto, SP.; Rosso, P. (2020). A Twitter Political Corpus of the 2019 10N Spanish Election. Springer. 41-49. https://doi.org/10.1007/978-3-030-58323-1_4 ; S ; 41 ; 49 ; Abercrombie, G., Nanni, F., Batista-Navarro, R., Ponzetto, S.P.: Policy preference detection in parliamentary debate motions. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), Hong Kong, China, pp. 249–259. Association for Computational Linguistics, November 2019 ; Ekman, P., et al.: Universals and cultural differences in the judgments of facial expressions of emotion. J. Pers. Soc. Psychol. 53(4), 712 (1987) ; Gao, W., Sebastiani, F.: Tweet sentiment: from classification to quantification. In: 2015 IEEE/ACM International Conference on ASONAM, pp. 97–104. IEEE (2015) ; Glavaš, G., Nanni, F., Ponzetto, S.P.: Computational analysis of political texts: bridging research efforts across communities. In: Proceedings of the 57th Annual Meeting of the Association for ...

© 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/10.1145/2930663 ; S ; 19:1 ; 19:24 ; 16 ; 3 ; Rob Abbott, Marilyn Walker, Pranav Anand, Jean E. Fox Tree, Robeson Bowmani, and Joseph King. 2011. How can you say such things?!?: Recognizing disagreement in informal political argument. In Proceedings of the Workshop on Languages in Social Media (LSM'11). Association for Computational ...

[EN] We present an approach to detect fake news in Twitter at the account level using a neural recurrent model and a variety of different semantic and stylistic features. Our method extracts a set of features from the timelines of news Twitter accounts by reading their posts as chunks, rather than dealing with each tweet independently. We show the experimental benefits of modeling latent stylistic signatures of mixed fake and real news with a sequential model over a wide range of strong baselines ; The work of Paolo Rosso was partially funded by the Spanish MICINN under the research project MISMIS-FAKEnHATE on Misinformation and Miscommunication in social media: FAKE news and HATE speech (PGC2018-096212-B-C31) ; Ghanem, BHH.; Ponzetto, SP.; Rosso, P. (2020). FacTweet: Profiling Fake News Twitter Accounts. Springer. 35-45. https://doi.org/10.1007/978-3-030-59430-5_3 ; S ; 35 ; 45 ; Aker, A., Kevin, V., Bontcheva, K.: Credibility and transparency of news sources: data collection and feature analysis. arXiv (2019) ; Aker, A., Kevin, V., Bontcheva, K.: Predicting news source credibility. arXiv (2019) ; Badawy, A., Lerman, K., Ferrara, E.: Who falls for online political manipulation? In: Companion Proceedings of the 2019 World Wide Web Conference, pp. 162–168. ACM (2019) ; Baly, R., Karadzhov, G., Alexandrov, D., Glass, J., Nakov, P.: Predicting factuality of reporting and bias of news media sources. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 3528–3539 (2018) ; Baly, R., Karadzhov, G., Saleh, A., Glass, J., Nakov, P.: Multi-task ordinal regression for jointly predicting the trustworthiness and the leading political ideology of news media. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 2109–2116 (2019) ; Boyd, R.L., et al.: Characterizing the Internet Research Agency's Social Media Operations During the 2016 US ...

[EN] Question classification (QC) is a prime constituent of an automated question answering system. The work presented here demonstrates that a combination of multiple models achieves better classification performance than those obtained with existing individual models for the QC task in Bengali. We have exploited state-of-the-art multiple model combination techniques, i.e., ensemble, stacking and voting, to increase QC accuracy. Lexical, syntactic and semantic features of Bengali questions are used for four well-known classifiers, namely Naive Bayes, kernel Naive Bayes, Rule Induction and Decision Tree, which serve as our base learners. Single-layer question-class taxonomy with 8 coarse-grained classes is extended to two-layer taxonomy by adding 69 fine-grained classes. We carried out the experiments both on single-layer and two-layer taxonomies. Experimental results confirmed that classifier combination approaches outperform single-classifier classification approaches by 4.02% for coarse-grained question classes. Overall, the stacking approach produces the best results for fine-grained classification and achieves 87.79% of accuracy. The approach presented here could be used in other Indo-Aryan or Indic languages to develop a question answering system. ; Somnath Banerjee and Sudip Kumar Naskar are supported by Digital India Corporation (formerly Media Lab Asia), MeitY, Government of India, under the Visvesvaraya Ph.D. Scheme for Electronics and IT. The work of Paolo Rosso was partially funded by the Spanish MICINN under the research project PGC2018-096212-B-C31. ; Banerjee, S.; Kumar Naskar, S.; Rosso, P.; Bndyopadhyay, S. (2019). Classifier combination approach for question classification for Bengali question answering system. Sadhana. 44(12):1-14. https://doi.org/10.1007/s12046-019-1224-8 ; S ; 1 ; 14 ; 44 ; 12 ; Jurafsky D and Martin J H 2014 Speech and language processing. Pearson, London ; Martin J H and Jurafsky D 2000 Speech and language processing, international edition 710 ; Voorhees E M 2002 Overview ...

[EN] In the last decade, social media gained a very significant role in public debates, and despite the many intrinsic difficulties of analyzing data streaming from on-line platforms that are poisoned by bots, trolls, and low-quality information, it is undeniable that such data can still be used to test the public opinion and overall mood and to investigate how individuals communicate with each other. With the aim of analyzing the debate in Twitter on the 2016 referendum on the reform of the Italian Constitution, we created an Italian annotated corpus for stance detection for automatically estimating the stance of a relevant number of users. We take into account a diachronic perspective to shed lights on users' opinion dynamics. Furthermore, different types of social network communities, based on friendships, retweets, quotes, and replies were investigated, in order to analyze the communication among users with similar and divergent viewpoints. We observe particular aspects of users' behavior. First, our analysis suggests that users tend to be less explicit in expressing their stances after the outcome of the vote; simultaneously, users who exhibit a high number of cross-stance relations tend to become less polarized or to adopt a more neutral style in the following phase of the debate. Second, despite social media networks are generally aggregated in homogeneous communities, we highlight that the structure of the network can strongly change when different types of social relations are considered. In particular, networks defined by means of reply-to messages exhibit inverse homophily by stance, and users use more often replies for expressing diverging opinions, instead of other forms of communication. Interestingly, we also observe that the political polarization increases forthcoming the election and decreases after the election day. ; The work of Viviana Patti and Giancarlo Ruffo was partially funded by the Fondazione CRT under research project the Hate Speech and Social Media (2016.0688), and the "Progetto di ...

[EN] Patriarchal behavior, such as other social habits, has been transferred online, appearing as misogynistic and sexist comments, posts or tweets. This online hate speech against women has serious consequences in real life, and recently, various legal cases have arisen against social platforms that scarcely block the spread of hate messages towards individuals. In this difficult context, this paper presents an approach that is able to detect the two sides of patriarchal behavior, misogyny and sexism, analyzing three collections of English tweets, and obtaining promising results. ; The work of Simona Frenda and Paolo Rosso was partially funded by the Spanish MINECO under the research project SomEMBED (TIN2015-71147-C2-1-P). We also thank the support of CONACYT-Mexico (project FC-2410). ; Frenda, S.; Ghanem, B.; Montes-Y-Gómez, M.; Rosso, P. (2019). Online Hate Speech against Women: Automatic Identification of Misogyny and Sexism on Twitter. Journal of Intelligent & Fuzzy Systems. 36(5):4743-4752. https://doi.org/10.3233/JIFS-179023 ; S ; 4743 ; 4752 ; 36 ; 5 ; Anzovino M. , Fersini E. and Rosso P. , Automatic Identification and Classification of Misogynistic Language on Twitter, Proc 23rd International Conference on Applications of Natural Language to Information Systems, NLDB-2018, Springer-Verlag, LNCS 10859, 2018, pp. 57–64. ; Burnap P. and Williams M.L. , Hate speech, machine classification and statistical modelling of information flows on Twitter: Interpretation and communication for policy decision making, Internet, Policy and Politics, Oxford, UK, 2014. ; Burnap, P., Rana, O. F., Avis, N., Williams, M., Housley, W., Edwards, A., … Sloan, L. (2015). Detecting tension in online communities with computational Twitter analysis. Technological Forecasting and Social Change, 95, 96-108. doi:10.1016/j.techfore.2013.04.013 ; Chen Y. , Zhou Y. , Zhu S. and Xu H. , Detecting offensive language in social media to protect adolescent online safety, Privacy, Security, Risk and Trust (PASSAT), 2012 International ...

1 20 124 ; S ; [EN] In the last decade, social media gained a very significant role in public debates, and despite the many intrinsic difficulties of analyzing data streaming from on-line platforms that are poisoned by bots, trolls, and low-quality information, it is undeniable that such data can still be used to test the public opinion and overall mood and to investigate how individuals communicate with each other. With the aim of analyzing the debate in Twitter on the 2016 referendum on the reform of the Italian Constitution, we created an Italian annotated corpus for stance detection for automatically estimating the stance of a relevant number of users. We take into account a diachronic perspective to shed lights on users' opinion dynamics. Furthermore, different types of social network communities, based on friendships, retweets, quotes, and replies were investigated, in order to analyze the communication among users with similar and divergent viewpoints. We observe particular aspects of users' behavior. First, our analysis suggests that users tend to be less explicit in expressing their stances after the outcome of the vote; simultaneously, users who exhibit a high number of cross-stance relations tend to become less polarized or to adopt a more neutral style in the following phase of the debate. Second, despite social media networks are generally aggregated in homogeneous communities, we highlight that the structure of the network can strongly change when different types of social relations are considered. In particular, networks defined by means of reply-to messages exhibit inverse homophily by stance, and users use more often replies for expressing diverging opinions, instead of other forms of communication. Interestingly, we also observe that the political polarization increases forthcoming the election and decreases after the election day. The work of Viviana Patti and Giancarlo Ruffo was partially funded by the Fondazione CRT under research project the Hate Speech and Social Media (2016.0688), and the ...

[EN] This overview paper describes the first shared task on irony detection for the Arabic language. The task consists of a binary classification of tweets as ironic or not using a dataset composed of 5,030 Arabic tweets about different political issues and events related to the Middle East and the Maghreb. Tweets in our dataset are written in Modern Standard Arabic but also in different Arabic language varieties including Egypt, Gulf, Levantine and Maghrebi dialects. Eighteen teams registered to the task among which ten submitted their runs. The methods of participants ranged from feature-based to neural networks using either classical machine learning techniques or ensemble methods. The best performing system achieved F-score value of 0.844, showing that classical feature-based models outperform the neural ones. ; This publication was made possible by NPRP grant 9-175-1-033 from the Qatar National Research Fund (a member of Qatar Foundation). The findings achieved herein are solely the responsibility of the last author. The work of Paolo Rosso was also partially funded by Generalitat Valenciana under grant PROMETEO/2019/121. ; Ghanem, B.; Karoui, J.; Benamara, F.; Moriceau, V.; Rosso, P. (2019). IDAT@FIRE2019: Overview of the Track on Irony Detection in Arabic Tweets. CEUR-WS.org. 380-390. http://hdl.handle.net/10251/180744 ; S ; 380 ; 390

[EN] Commendable amount of work has been attempted in the field of Sentiment Analysis or Opinion Mining from natural language texts and Twitter texts. One of the main goals in such tasks is to assign polarities (positive or negative) to a piece of text. But, at the same time, one of the important as well as difficult issues is how to assign the degree of positivity or negativity to certain texts. The answer becomes more complex when we perform a similar task on figurative language texts collected from Twitter. Figurative language devices such as irony and sarcasm contain an intentional secondary or extended meaning hidden within the expressions. In this paper we present a novel approach to identify the degree of the sentiment (fine grained in an 11-point scale) for the figurative language texts. We used several semantic features such as sentiment and intensifiers as well as we introduced sentiment abruptness, which measures the variation of sentiment from positive to negative or vice versa. We trained our systems at multiple levels to achieve the maximum cosine similarity of 0.823 and minimum mean square error of 2.170. ; The work reported in this paper is supported by a grant from the project "CLIA System Phase II" funded by Department of Electronics and Information Technology (DeitY), Ministry of Communications and Information Technology (MCIT), Government of India. The work of the fourth author is also supported by the SomEMBED TIN2015-71147-C2-1-P MINECO research project and by the Generalitat Valenciana under the grant ALMAPATER (PrometeoII/2014/030). ; Gopal Patra, B.; Mazumda, S.; Das, D.; Rosso, P.; Bandyopadhyay, S. (2018). A Multilevel Approach to Sentiment Analysis of Figurative Language in Twitter. Lecture Notes in Computer Science. 9624:281-291. https://doi.org/10.1007/978-3-319-75487-1_22 ; S ; 281 ; 291 ; 9624 ; Ghosh, A., Li, G., Veale, T., Rosso, P., Shutova, E., Reyes, A., Barnden, J.: Semeval-2015 task 11: sentiment analysis of figurative language in Twitter. In: 9th International Workshop on ...

281 291 9624 ; S ; [EN] Commendable amount of work has been attempted in the field of Sentiment Analysis or Opinion Mining from natural language texts and Twitter texts. One of the main goals in such tasks is to assign polarities (positive or negative) to a piece of text. But, at the same time, one of the important as well as difficult issues is how to assign the degree of positivity or negativity to certain texts. The answer becomes more complex when we perform a similar task on figurative language texts collected from Twitter. Figurative language devices such as irony and sarcasm contain an intentional secondary or extended meaning hidden within the expressions. In this paper we present a novel approach to identify the degree of the sentiment (fine grained in an 11-point scale) for the figurative language texts. We used several semantic features such as sentiment and intensifiers as well as we introduced sentiment abruptness, which measures the variation of sentiment from positive to negative or vice versa. We trained our systems at multiple levels to achieve the maximum cosine similarity of 0.823 and minimum mean square error of 2.170. The work reported in this paper is supported by a grant from the project "CLIA System Phase II" funded by Department of Electronics and Information Technology (DeitY), Ministry of Communications and Information Technology (MCIT), Government of India. The work of the fourth author is also supported by the SomEMBED TIN2015-71147-C2-1-P MINECO research project and by the Generalitat Valenciana under the grant ALMAPATER (PrometeoII/2014/030). Gopal Patra, B.; Mazumda, S.; Das, D.; Rosso, P.; Bandyopadhyay, S. (2018). A Multilevel Approach to Sentiment Analysis of Figurative Language in Twitter. Lecture Notes in Computer Science. 9624:281-291. https://doi.org/10.1007/978-3-319-75487-1_22 Ghosh, A., Li, G., Veale, T., Rosso, P., Shutova, E., Reyes, A., Barnden, J.: Semeval-2015 task 11: sentiment analysis of figurative language in Twitter. In: 9th International Workshop on Semantic ...

Stereotypes about immigrants are a type of social bias increasingly present in the human interaction in social networks and political speeches. This challenging task is being studied by computational linguistics because of the rise of hate messages, offensive language, and discrimination that many people receive. In this work, we propose to identify stereotypes about immigrants using two different explainable approaches: a deep learning model based on Transformers; and a text masking technique that has been recognized by its capabilities to deliver good and human-understandable results. Finally, we show the suitability of the two models for the task and offer some examples of their advantages in terms of explainability. ; Los estereotipos sobre inmigrantes son un tipo de sesgo social cada vez más presente en la interacción humana en redes sociales y en los discursos políticos. Esta desafiante tarea está siendo estudiada por la lingüística computacional debido al aumento de los mensajes de odio, el lenguaje ofensivo, y la discriminación que reciben muchas personas. En este trabajo, nos proponemos identificar estereotipos sobre inmigrantes utilizando dos enfoques diametralmente opuestos prestando atención a la explicabilidad de los mismos: un modelo de aprendizaje profundo basado en Transformers; y una técnica de enmascaramiento de texto que ha sido reconocida por su capacidad para ofrecer buenos resultados a la vez que comprensibles para los humanos. Finalmente, mostramos la idoneidad de los dos modelos para la tarea, y ofrecemos algunos ejemplos de sus ventajas en términos de explicabilidad. ; The work of the authors from the Universitat Politècnica of València was funded by the Spanish Ministry of Science and Innovation under the research project MISMIS-FAKEnHATE on MISinformation and MIScommunication in social media: FAKE news and HATE speech (PGC2018-096212-B-C31). Experiments were carried out on the GPU cluster at PRHLT thanks to the PROMETEO/2019/121 (DeepPattern) research project funded by the Generalitat Valenciana.