EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020

Welcome to EVALITA 2020! EVALITA is the evaluation campaign of Natural Language Processing and Speech Tools for Italian. EVALITA is an initiative of the Italian Association for Computational Linguistics (AILC, http://www.ai-lc.it) and it is endorsed by the Italian Association for Artificial Intelligence (AIxIA, http://www.aixia.it) and the Italian Association for Speech Sciences (AISV, http://www.aisv.it)

ePlanning: an Ontology-based System for Building Individualized Education Plans for Students with Special Educational Needs

This paper presents the results of a two years research project aimed atadopting semantic web technology to draft the IEP (IndividualizedEducation Plan) for pupils with special educational needs in school. Itincludes a report of lessons learned through the collaborative building ofan ontology in a concrete and multidisciplinary context, as well as indeveloping an ontology-based decision support system

To re-rank or to re-query: Can visual analytics solve this dilemma?

Evaluation has a crucial role in (IR) since it allows for identifying possible points of failure of an IR approach, thus addressing them to improve its effectiveness. Developing tools to support researchers and analysts when analyzing results and investigating strategies to improve IR system performance can help make the analysis easier and more effective. In this paper we discuss a VA-based approach to support the analyst when deciding whether or not to investigate re-ranking to improve the system effectiveness measured after a retrieval run. Our approach is based on effectiveness measures that exploit graded relevance judgements and it provides both a principled and intuitive way to support analysis. A prototype is described and exploited to discuss some case studies based on TREC data. © 2011 Springer-Verlag

Interactive Analysis and Exploration of Experimental Evaluation Results

This paper proposes a methodology based on discounted cumulated gain measures and visual analytics techniques in order to improve the analysis and understanding of IR experimental evaluation results. The proposed methodology is geared to favour a natural and eective interaction of the researchers and developers with the experimental data and it is demonstrated by developing an innovative application based on Apple iPad

To Re-rank or to Re-query: Can Visual Analytics Solve This Dilemma?

Evaluation has a crucial role in Information Retrieval (IR) since it allows possible point of failures of an IR approach to be identi ed and addressed thus improving the predictive capability of such approach. Developing tools to support users when analyzing results and investigating strategies to improve IR system performance can help make the analysis easier and more eective. In this paper we discuss a Visual Analytics-based approach to support the user when deciding whether or not to perform re-ranking to improve the system eectiveness measured after a retrieval run. The proposed approach is based on eectiveness measures that exploit graded relevance judgements and provide both a principled and intuitive way to support the user. A prototype is described and exploited to discuss some case studies based on TREC data

ePlanning: an Ontology-based System for Building Individualized Education Plans for Students with Special Educational Needs

This paper presents the results of a two years research project aimed at adopting semantic web technology to draft the IEP (Individualized Education Plan) for pupils with special educational needs in school. It includes a report of lessons learned through the collaborative building of an ontology in a concrete and multidisciplinary context, as well as in developing an ontology-based decision support system

When scientific experts come to be media stars: An evolutionary model tested by analysing coronavirus media coverage across Italian newspapers

The article aims to understand the process through which scientific experts gain and maintain remarkable media visibility. It has been analysed a corpus of 213,875 articles published by the eight most important Italian newspapers across the Covid-19 pandemic in 2020 and 2021. By exploring this process along the different phases of the management of the emergency in Italy, it was observed that some scientific experts achieve high media visibility—and sometimes notwithstanding their low academic reputation–thus becoming a sort of “media star”. Scientific literature about the relationship between experts and media is considerable, nonetheless we found a lack of theoretical models able to analyse under which conditions experts are able to enter and to remain prominent in the media sphere. A Media Experts Evolutionary Model (MEEM) is proposed in order to analyze the main conditions under which experts can acquire visibility and how they can “survive” in media arena. We proceeded by analysing visibility of experts during SARS-CoV-2 pandemic and considering both their individual credentials previously acquired and the media environment processes of selection; MEEM acts hence as a combination of these two levels. Regarding the credentials, we accounted for i) institutional role/position, ii) previous media visibility, and iii) matches between scientific credentials and media competence. In our analysis, we collected evidence that high visibility in newspapers can be seen as evolutionary in the sense that some profiles—i.e. a particular configuration of credentials—are more adapt to specific media environments

When scientific experts come to be media stars: An evolutionary model tested by analysing coronavirus media coverage across Italian newspapers.

The article aims to understand the process through which scientific experts gain and maintain remarkable media visibility. It has been analysed a corpus of 213,875 articles published by the eight most important Italian newspapers across the Covid-19 pandemic in 2020 and 2021. By exploring this process along the different phases of the management of the emergency in Italy, it was observed that some scientific experts achieve high media visibility-and sometimes notwithstanding their low academic reputation-thus becoming a sort of "media star". Scientific literature about the relationship between experts and media is considerable, nonetheless we found a lack of theoretical models able to analyse under which conditions experts are able to enter and to remain prominent in the media sphere. A Media Experts Evolutionary Model (MEEM) is proposed in order to analyze the main conditions under which experts can acquire visibility and how they can "survive" in media arena. We proceeded by analysing visibility of experts during SARS-CoV-2 pandemic and considering both their individual credentials previously acquired and the media environment processes of selection; MEEM acts hence as a combination of these two levels. Regarding the credentials, we accounted for i) institutional role/position, ii) previous media visibility, and iii) matches between scientific credentials and media competence. In our analysis, we collected evidence that high visibility in newspapers can be seen as evolutionary in the sense that some profiles-i.e. a particular configuration of credentials-are more adapt to specific media environments

Heatmap share for experts across time, 24 months’ time span, January 1^st, 2020 –December 31^st, 2021 (B2 COVID EXPERT SUBSET, N = 25,550).

Heatmap share for experts across time, 24 months’ time span, January 1st, 2020 –December 31st, 2021 (B2 COVID EXPERT SUBSET, N = 25,550).</p