Proceedings of the Eighth Italian Conference on Computational Linguistics CliC-it 2021

The eighth edition of the Italian Conference on Computational Linguistics (CLiC-it 2021) was held at Università degli Studi di Milano-Bicocca from 26th to 28th January 2022. After the edition of 2020, which was held in fully virtual mode due to the health emergency related to Covid-19, CLiC-it 2021 represented the first moment for the Italian research community of Computational Linguistics to meet in person after more than one year of full/partial lockdown

Post-LS3 Experimental Options in ECN3

The Experimental Cavern North 3 (ECN3) is an underground experimental cavern on the CERN Pr\'evessin site. ECN3 currently hosts the NA62 experiment, with a physics programme devoted to rare kaon decays and searches of hidden particles approved until Long Shutdown 3 (LS3). Several options are proposed on the longer term in order to make best use of the worldwide unique potential of the high-intensity/high-energy proton beam extracted from the Super Proton Synchrotron (SPS) in ECN3. The current status of their study by the CERN Physics Beyond Colliders (PBC) Study Group is presented, including considerations on beam requirements and upgrades, detector R&D and construction, schedules and cost, as well as physics potential within the CERN and worldwide landscape.Comment: 113 pages, 39 figure

Understanding Semantic Implicit Learning through distributional linguistic patterns: A computational perspective

The research presented in this PhD dissertation provides a computational perspective on Semantic Implicit Learning (SIL). It puts forward the idea that SIL does not depend on semantic knowledge as classically conceived but upon semantic-like knowledge gained through distributional analysis of massive linguistic input. Using methods borrowed from the machine learning and artificial intelligence literature, we construct computational models, which can simulate the performance observed during behavioural tasks of semantic implicit learning in a human-like way. We link this methodology to the current literature on implicit learning, arguing that this behaviour is a necessary by-product of efficient language processing. Chapter 1 introduces the computational problem posed by implicit learning in general, and semantic implicit learning, in particular, as well as the computational framework, used to tackle them. Chapter 2 introduces distributional semantics models as a way to learn semantic-like representations from exposure to linguistic input. Chapter 3 reports two studies on large datasets of semantic priming which seek to identify the computational model of semantic knowledge that best fits the data under conditions that resemble SIL tasks. We find that a model which acquires semantic-like knowledge gained through distributional analysis of massive linguistic input provides the best fit to the data. Chapter 4 generalises the results of the previous two studies by looking at the performance of the same models in languages other than English. Chapter 5 applies the results of the two previous Chapters on eight datasets of semantic implicit learning. Crucially, these datasets use various semantic manipulations and speakers of different L1s enabling us to test the predictions of different models of semantics. Chapter 6 examines more closely two assumptions which we have taken for granted throughout this thesis. Firstly, we test whether a simpler model based on phonological information can explain the generalisation patterns observed in the tasks. Secondly, we examine whether our definition of the computational problem in Chapter 5 is reasonable. Chapter 7 summarises and discusses the implications for implicit language learning and computational models of cognition. Furthermore, we offer one more study that seeks to bridge the literature on distributional models of semantics to `deeper' models of semantics by learning semantic relations. There are two main contributions of this dissertation to the general field of implicit learning research. Firstly, we highlight the superiority of distributional models of semantics in modelling unconscious semantic knowledge. Secondly, we question whether `deep' semantic knowledge is needed to achieve above chance performance in SIIL tasks. We show how a simple model that learns through distributional analysis of the patterns found in the linguistic input can match the behavioural results in different languages. Furthermore, we link these models to more general problems faced in psycholinguistics such as language processing and learning of semantic relations.Alexandros Onassis Foundatio

The Language of Weblogs: A study of genre and individual differences

Institute for Communicating and Collaborative SystemsThis thesis describes a linguistic investigation of individual differences in online personal diaries, or 'blogs.' There is substantial evidence of gender differences in language (Lakoff, 1975), and to a lesser extent linguistic projection of personality (Pennebaker & King, 1999). Recent work has investigated these latter differences in the area of computer-mediated communication (CMC), specifically e-mail (Gill, 2004). This thesis employs a number of analytic techniques, both top-down (dictionary-based) and bottom-up (data-driven), in order to explore personality and gender differences in the language of blogs. A corpus was constructed by asking authors to submit a month of text and complete a sociobiographic questionnaire. The corpus consists of over 400,000 words and five-factor personality data (Buchanan, 2001) for 71 subjects. The thesis begins by framing blogs in the context of other genres, both CMC and traditional, in order to show both the distinctiveness and representativeness of the genre. Top-down content analysis techniques are then employed to investigate the relationship between personality and linguistic features. A number of features correlate with each trait, but upon regression, very little variance is explained. Bottom-up techniques are more successful. The corpus was stratified into high, low and neutral personality groups to identify distinctive collocations for each. Returning to the raw personality scores, it becomes clear that even a small amount of n-gram context helps account for much more variance in personality. A measure of contextuality (Heylighen & Dewaele, 2002) shows that authors considered high in Agreeableness pay more attention to differences between their extra-linguistic context and that of their audience. Attention turns to gender, where similar methods are applied to investigate gender differences in language. Many previous findings are confirmed in the blog corpus. In addition, women are found to write more in their blogs than men. More generally, using the British National Corpus, it is shown that women are more contextual, except in the least contextual of genres (academic writing) where there is no difference. The study concludes by confirming that both gender and personality are projected by language in blogs; furthermore, approaches which take the context of language features into account can be used to detect more variation than those which do not

AIUCD 2022 - Proceedings

L’undicesima edizione del Convegno Nazionale dell’AIUCD-Associazione di Informatica Umanistica ha per titolo Culture digitali. Intersezioni: filosofia, arti, media. Nel titolo è presente, in maniera esplicita, la richiesta di una riflessione, metodologica e teorica, sull’interrelazione tra tecnologie digitali, scienze dell’informazione, discipline filosofiche, mondo delle arti e cultural studies

Dark Matter and how to find it: a search for low-mass leptophobic Dark Matter mediators and the development of mass-decorrelated jet taggers with the ATLAS experiment

A search for low-mass leptophobic Dark Matter (DM) mediator particles in 36 fb-1 of pp collision data at √s = 13 TeV collected by the ATLAS experiment is presented. The search is performed in final states where the mediator decay into a quark pair is reconstructed as a single, large-radius jet produced in association with a photon or a jet. No deviations from the Standard Model expectation are observed, and limits are placed on the production cross-section of leptophobic mediator particles and their coupling to quarks for mediator masses between 100 and 220 GeV. At the time of publication, this result constituted the lowest limits on leptophobic DM mediator masses for high-mass DM particles reported by ATLAS. Adversarial neural networks (ANN) are presented as a way to train jet taggers which decorrelates them from the invariant mass of the jet. An extensive study of five different approaches to constructing mass-decorrelated jet taggers is presented. The ANN tagger is found to provide the largest QCD multijet rejection at similar levels of mass-decorrelation