Representation and Inference for Open-Domain Question Answering: Strength and Limits of two Italian Semantic Lexicons

La ricerca descritta nella tesi è stata dedicata alla costruzione di un prototipo di sistema di Question Answering per la lingua italiana. Il prototipo è stato utilizzato come ambiente di valutazione dell’utilità dell’informazione codificata in due lessici semantici computazionali, ItalWordNet e SIMPLE-CLIPS. Il fine è quello di metter in evidenza ipunti di forza e ilimiti della rappresentazione dell’informazione proposta dai due lessici

A network-based approach for discourse analysis from Laclau and Mouffe’s perspectives

[EN] The current study provides the possibility of merging Laclau and Mouffe’s theory of discourse analysis with network theory to specify an alternative bedstead for studying discourse via a semi-automatic algorithm. To do so, first, considering the text as the discourse of complex system, a semi-automatic algorithm is implemented to transform the interacting linguistic components into a network which is depicted as a graph of vertices connected by edges. Then, some of the graph statistics, e.g. degree, weighted degree, eigenvector centrality, etc., are identified for characterizing the nodes as moments, nodal points, and/or nodal point of identity. Finally, the articulation of the discourse based on the above-mentioned components is studied. The results indicate that the approach is strong enough to pave a way for studying the articulation of the discourse from an alternative view, especially based on Laclau and Mouffe’s theory of discourse analysis.Haditaghi, J.; Hassasskhah, J.; Sorahi, MA. (2020). A network-based approach for discourse analysis from Laclau and Mouffe’s perspectives. Journal of Computer-Assisted Linguistic Research. 4(1):1-22. https://doi.org/10.4995/jclr.2020.12105OJS12241Degree, In-Degree, Out-Degree, Weighted Degree, Weighted In-Degree, Weighted Out-Degree. Nebih. Accessed November 26 2017. https://portal.nebih.gov.hu/documents/10182/521653/Algorithms.pdf/6397efcc-6106-4f9e-8057-e7e63858e816Features. Gephi website, Last Modified 1 26, accessed September 8 2018. https://gephi.org/features/Baez, John. 2016. John Baez's Stuff. Department of mathematics: University of California. Accessed November 25 2017. www.math.ucr.edu/home/baez/econ.pdfBahaziq, Afnan. 2016. "Cohesive Devices in Written Discourse: A Discourse Analysis of a Student's Essay Writing." English Language Teaching 9 (7):112-119. https://doi.org/10.5539/elt.v9n7p112Bastian, Mathieu, Sebastien Heymann, and Mathieu Jacomy. 2009. "Gephi: an open source software for exploring and manipulating networks." Third international AAAI conference on weblogs and social media.Bennett, Tony, Lawrence Grossberg, and Meaghan Morris. 2013. New keywords: A revised vocabulary of culture and society. Malden, Oxford and Carlton: BlackwellBird, Steven, Edward Loper, and Ewan Klein. 2009. "Natural language toolkit." J URL http://www.nltk.orgBloor, Thomas, and Meriel Bloor. 2013. The functional analysis of English. New York: Routledge. https://doi.org/10.4324/9780203538098Bridgman, Todd. 2007. "Freedom and autonomy in the university enterprise." Journal of Organizational Change Management 20 (4):478-490. https://doi.org/10.1108/09534810710760036Cameron, Lynne, and Diane Larsen-Freeman. 2007. "Complex systems and applied linguistics." International journal of applied linguistics 17 (2):226-239. https://doi.org/10.1111/j.1473-4192.2007.00148.xCherven, Ken. 2015. Mastering Gephi network visualization: Packt Publishing Ltd.Contu, Alessia, Christopher Grey, and Anders Örtenblad. 2003. "Against learning." Human relations 56 (8):931-952. https://doi.org/10.1177/00187267030568002Crowcroft, Jon. 2016. Introduction to network theory. Cambridge University: Faculty of Computer science and technology. Accessed November 25.Culler, J.D. 2002. Structuralist Poetics: Structuralism, Linguistics and the Study of Literature. London and New York: Routledge.Dabirimehr, Amir, and Malihe Tabatabai Fatmi. 2014. "LACLAU and Mouffe's Theory of Discourse." Journal of Novel Applied Sciences 3 (11):1284.De Saussure, Ferdinand. 1989. Cours de linguistique générale: Édition critique. Vol. 1: Otto Harrassowitz Verlag.Five Graces Group (Beckner, Clay, Richard Blythe, Joan Bybee, Morten H Christiansen, William Croft, Nick C Ellis, John Holland, Jinyun Ke, and Diane Larsen Freeman). 2009. "Language is a complex adaptive system: Position paper." Language learning 59:1-26. https://doi.org/10.1111/j.1467-9922.2009.00533.xFortunato, Santo, and Marc Barthelemy. 2007. "Resolution limit in community detection." Proceedings of the National Academy of Sciences 104 (1):36-41. https://doi.org/10.1073/pnas.0605965104Khokhar, Devangana. 2015. Gephi cookbook. Birmingham and Mumbai: Packt Publishing Ltd.Laclau, Ernesto. 2005. On populist reason. London: Verso.Laclau, Ernesto, and Chantal Mouffe. 2001. Hegemony and socialist strategy: Towards a radical democratic politics. London: Verso.Liu, Jianyi, and Jinghua Wang. 2007. "Keyword extraction using language network." Natural Language Processing and Knowledge Engineering, 2007. NLP-KE 2007. International Conference on. https://doi.org/10.1109/NLPKE.2007.4368023McKinney, Wes. 2010. "Data structures for statistical computing in python." Proceedings of the 9th Python in Science Conference. https://doi.org/10.25080/Majora-92bf1922-00aMcSweeney, Patrick J. 2009. "Gephi network statistics." Google Summer of Code:1-8.Mihalcea, Rada, and Dragomir Radev. 2011. Graph-based natural language processing and information retrieval: Cambridge university press.Mitchell, Melanie. 2009. Complexity: A guided tour. New York: Oxford University Press.Morgan, Marcyliena H. 2014. Speech communities: Cambridge University Press.Mubarak, Lihadh Abdul Ameer. 2014. "Finding out the Cohesive Devices Most Frequently Used by Iraqi EFL University Students in Their Monthly Examination Sheets." Basic Education College Magazine For Educational and Humanities Sciences (17):225-234.Mumford, Lewis. 1968. The urban prospect. New York: Harcourt, Brace & World.Newman, Mark EJ. 2006. "Modularity and community structure in networks." Proceedings of the national academy of sciences 103 (23):8577-8582. https://doi.org/10.1073/pnas.0601602103Paltridge, Brian. 2006. Discourse analysis: An introduction. New York: Bloomsbury Publishing.Paranyushkin, Dmitry. 2011. "Identifying the pathways for meaning circulation using text network analysis." Berlin: Nodus Labs. http://noduslabs. com/research/pathways-meaning-circulation-text-network-analysis.Totet, Matthieu. 2013. "Let's Play Gephi: Understand Degree, Weighted Degree & Betweeness centrality." Last Modified December 16. http://matthieu-totet.fr/Koumin/2013/12/16/understand-degree-weighted-degree-betweeness-centrality/.Turcotte-Summers, Jonathan. 2015. ""Students" as Nodal Point of Identity: Analyzing the Discourses of Quebec's News Media and Students in the 2012 Printemps Érable." PhD, Department of Education, Concordia University.Urry, John, and Jonas Larsen. 2011. The tourist gaze 3.0. London and Los Angeles: Sage. https://doi.org/10.4135/9781446251904Walton, Sara, and Bronwyn Boon. 2014. "Engaging with a Laclau & Mouffe informed discourse analysis: a proposed framework." Qualitative Research in Organizations and Management: An International Journal 9 (4):351-370. https://doi.org/10.1108/QROM-10-2012-1106Weston, Steve, and Hadley Wickham. 2014. "itertools: Iterator Tools." R package version 0.1 3.Yadav, Chandra Shekhar, Aditi Sharan, and Manju Lata Joshi. 2014. "Semantic graph based approach for text mining." 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT). https://doi.org/10.1109/ICICICT.2014.678134

JURI SAYS:An Automatic Judgement Prediction System for the European Court of Human Rights

In this paper we present the web platform JURI SAYS that automatically predicts decisions of the European Court of Human Rights based on communicated cases, which are published by the court early in the proceedings and are often available many years before the final decision is made. Our system therefore predicts future judgements of the court. The platform is available at jurisays.com and shows the predictions compared to the actual decisions of the court. It is automatically updated every month by including the prediction for the new cases. Additionally, the system highlights the sentences and paragraphs that are most important for the prediction (i.e. violation vs. no violation of human rights)

Attribution: a computational approach

Our society is overwhelmed with an ever growing amount of information. Effective management of this information requires novel ways to filter and select the most relevant pieces of information. Some of this information can be associated with the source or sources expressing it. Sources and their relation to what they express affect information and whether we perceive it as relevant, biased or truthful. In news texts in particular, it is common practice to report third-party statements and opinions. Recognizing relations of attribution is therefore a necessary step toward detecting statements and opinions of specific sources and selecting and evaluating information on the basis of its source. The automatic identification of Attribution Relations has applications in numerous research areas. Quotation and opinion extraction, discourse and factuality have all partly addressed the annotation and identification of Attribution Relations. However, disjoint efforts have provided a partial and partly inaccurate picture of attribution. Moreover, these research efforts have generated small or incomplete resources, thus limiting the applicability of machine learning approaches. Existing approaches to extract Attribution Relations have focused on rule-based models, which are limited both in coverage and precision. This thesis presents a computational approach to attribution that recasts attribution extraction as the identification of the attributed text, its source and the lexical cue linking them in a relation. Drawing on preliminary data-driven investigation, I present a comprehensive lexicalised approach to attribution and further refine and test a previously defined annotation scheme. The scheme has been used to create a corpus annotated with Attribution Relations, with the goal of contributing a large and complete resource than can lay the foundations for future attribution studies. Based on this resource, I developed a system for the automatic extraction of attribution relations that surpasses traditional syntactic pattern-based approaches. The system is a pipeline of classification and sequence labelling models that identify and link each of the components of an attribution relation. The results show concrete opportunities for attribution-based applications

Populating Legal Ontologies using Information Extraction based on Semantic Role Labeling and Text Similarity

This thesis seeks to address the problem of the 'resource consumption bottleneck' of creating (legal) semantic technologies manually. It builds on research in legal theory, ontologies and natural language processing in order to semi-automatically normalise legislative text, extract definitions and structured norms, and link normative provisions to recitals. The output is intended to help make laws more accessible, understandable, and searchable in a legal document management system. Key contributions are: - an analysis of legislation and structured norms in legal ontologies and compliance systems in order to determine the kind of information that individuals and organisations require from legislation to understand their rights and duties; - an analysis of the semantic and structural challenges of legislative text for machine understanding; - a rule-based normalisation module to transform legislative text into regular sentences to facilitate natural language processing; - a Semantic Role Labeling based information extraction module to extract definitions and norms from legislation and represent them as structured norms in legal ontologies; - an analysis of the impact of recitals on the interpretation of legislative norms; - a Cosine Similarity based text similarity module to link recitals to relevant normative provisions; - a description of important challenges that have emerged from this research which may prove useful for future work in the extraction and linking of information from legislative text

Spectators’ aesthetic experiences of sound and movement in dance performance

In this paper we present a study of spectators’ aesthetic experiences of sound and movement in live dance performance. A multidisciplinary team comprising a choreographer, neuroscientists and qualitative researchers investigated the effects of different sound scores on dance spectators. What would be the impact of auditory stimulation on kinesthetic experience and/or aesthetic appreciation of the dance? What would be the effect of removing music altogether, so that spectators watched dance while hearing only the performers’ breathing and footfalls? We investigated audience experience through qualitative research, using post-performance focus groups, while a separately conducted functional brain imaging (fMRI) study measured the synchrony in brain activity across spectators when they watched dance with sound or breathing only. When audiences watched dance accompanied by music the fMRI data revealed evidence of greater intersubject synchronisation in a brain region consistent with complex auditory processing. The audience research found that some spectators derived pleasure from finding convergences between two complex stimuli (dance and music). The removal of music and the resulting audibility of the performers’ breathing had a significant impact on spectators’ aesthetic experience. The fMRI analysis showed increased synchronisation among observers, suggesting greater influence of the body when interpreting the dance stimuli. The audience research found evidence of similar corporeally focused experience. The paper discusses possible connections between the findings of our different approaches, and considers the implications of this study for interdisciplinary research collaborations between arts and sciences

Holistic recommender systems for software engineering

The knowledge possessed by developers is often not sufficient to overcome a programming problem. Short of talking to teammates, when available, developers often gather additional knowledge from development artifacts (e.g., project documentation), as well as online resources. The web has become an essential component in the modern developer’s daily life, providing a plethora of information from sources like forums, tutorials, Q&A websites, API documentation, and even video tutorials. Recommender Systems for Software Engineering (RSSE) provide developers with assistance to navigate the information space, automatically suggest useful items, and reduce the time required to locate the needed information. Current RSSEs consider development artifacts as containers of homogeneous information in form of pure text. However, text is a means to represent heterogeneous information provided by, for example, natural language, source code, interchange formats (e.g., XML, JSON), and stack traces. Interpreting the information from a pure textual point of view misses the intrinsic heterogeneity of the artifacts, thus leading to a reductionist approach. We propose the concept of Holistic Recommender Systems for Software Engineering (H-RSSE), i.e., RSSEs that go beyond the textual interpretation of the information contained in development artifacts. Our thesis is that modeling and aggregating information in a holistic fashion enables novel and advanced analyses of development artifacts. To validate our thesis we developed a framework to extract, model and analyze information contained in development artifacts in a reusable meta- information model. We show how RSSEs benefit from a meta-information model, since it enables customized and novel analyses built on top of our framework. The information can be thus reinterpreted from an holistic point of view, preserving its multi-dimensionality, and opening the path towards the concept of holistic recommender systems for software engineering