1,721,011 research outputs found
Il racconto della malattia, atti delle sessioni parallele del Convegno internazionale di studi “Il racconto della malattia” (L’Aquila, 19-21 febbraio 2021)
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Discussion points raised by Francesco Cutugno and Maria Di Maro
Full text linkThe paper deals with the process of grounding in dialogue systems, modelled in terms of factual knowledge of the world, knowledge concerning the user, and the hypothesis of mental knowledge state of the user, i.e., theory of mind. The difficulty of describing and modelling this pragmatic process in conversational agents emerges here in the necessity to refer to and integrate other cognitive theories. Specifically, considering that there are diverse types of shared sets of knowledge, the quest..
TextWiller @ SardiStance, HaSpeede2: Text or Con-text? A Smart Use of Social Network Data in Predicting Polarization
No full textIn this contribution we describe the system(i.e. a statistical model) used to participatein Evalita conference 2020, SardiStance(Tasks A and B) and Haspeede2 (TasksA and B). We first developed a classifierby extracting features from the texts andthe social network of users. Then, wefit the data through an extreme gradientboosting, with cross-validation tuning ofthe hyper-parameters. A key factor for agood performance in SardiStance Task Bwas the features extraction by using Mul-tidimensional Scaling of the distance ma-trix (minimum path, undirected graph) ap-plied on each network. The second sys-tem exploits the same features above, butit trains and performs predictions in two-steps.The performances proved to belower than those of the single-step model
UniBO @ AMI: A Multi-Class Approach to Misogyny and Aggressiveness Identification on Twitter Posts Using AlBERTo
Full text linkWe describe our participation in the EVALITA 2020 (Basile et al., 2020) shared task on Automatic Misogyny Identification. We focus on task A —Misogyny and Aggressive Behaviour Identification— which aims at detecting whether a tweet in Italian is misogynous and, if so, whether it is aggressive. Rather than building two different models, one for misogyny and one for aggressiveness identification, we handle the problem as one single multi-label classification task, considering three classes: non-misogynous, non-aggressive misogynous, and aggressive misogynous. Our three-class supervised model, built on top of AlBERTo, obtains an overall F1 score of 0.7438 on the task test set (F1 = 0.8102 for the misogyny and F1 = 0.6774 for the aggressiveness task), which outperforms the top submitted model (F1 = 0.7406)
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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