Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018)

Peer reviewe

Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018 : 10-12 December 2018, Torino

On behalf of the Program Committee, a very warm welcome to the Fifth Italian Conference on Computational Linguistics (CLiC-­‐it 2018). This edition of the conference is held in Torino. The conference is locally organised by the University of Torino and hosted into its prestigious main lecture hall “Cavallerizza Reale”. The CLiC-­‐it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after five years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges

AmericasNLI: Machine translation and natural language inference systems for Indigenous languages of the Americas

Little attention has been paid to the development of human language technology for truly low-resource languages—i.e., languages with limited amounts of digitally available text data, such as Indigenous languages. However, it has been shown that pretrained multilingual models are able to perform crosslingual transfer in a zero-shot setting even for low-resource languages which are unseen during pretraining. Yet, prior work evaluating performance on unseen languages has largely been limited to shallow token-level tasks. It remains unclear if zero-shot learning of deeper semantic tasks is possible for unseen languages. To explore this question, we present AmericasNLI, a natural language inference dataset covering 10 Indigenous languages of the Americas. We conduct experiments with pretrained models, exploring zero-shot learning in combination with model adaptation. Furthermore, as AmericasNLI is a multiway parallel dataset, we use it to benchmark the performance of different machine translation models for those languages. Finally, using a standard transformer model, we explore translation-based approaches for natural language inference. We find that the zero-shot performance of pretrained models without adaptation is poor for all languages in AmericasNLI, but model adaptation via continued pretraining results in improvements. All machine translation models are rather weak, but, surprisingly, translation-based approaches to natural language inference outperform all other models on that task

Advances in Spectral Learning with Applications to Text Analysis and Brain Imaging

Spectral learning algorithms are becoming increasingly popular in data-rich domains, driven in part by recent advances in large scale randomized SVD, and in spectral estimation of Hidden Markov Models. Extensions of these methods lead to statistical estimation algorithms which are not only fast, scalable, and useful on real data sets, but are also provably correct. Following this line of research, we make two contributions. First, we propose a set of spectral algorithms for text analysis and natural language processing. In particular, we propose fast and scalable spectral algorithms for learning word embeddings -- low dimensional real vectors (called Eigenwords) that capture the “meaning” of words from their context. Second, we show how similar spectral methods can be applied to analyzing brain images. State-of-the-art approaches to learning word embeddings are slow to train or lack theoretical grounding; We propose three spectral algorithms that overcome these limitations. All three algorithms harness the multi-view nature of text data i.e. the left and right context of each word, and share three characteristics: 1). They are fast to train and are scalable. 2). They have strong theoretical properties. 3). They can induce context-specific embeddings i.e. different embedding for “river bank” or “Bank of America”. \end{enumerate} They also have lower sample complexity and hence higher statistical power for rare words. We provide theory which establishes relationships between these algorithms and optimality criteria for the estimates they provide. We also perform thorough qualitative and quantitative evaluation of Eigenwords and demonstrate their superior performance over state-of-the-art approaches. Next, we turn to the task of using spectral learning methods for brain imaging data. Methods like Sparse Principal Component Analysis (SPCA), Non-negative Matrix Factorization (NMF) and Independent Component Analysis (ICA) have been used to obtain state-of-the-art accuracies in a variety of problems in machine learning. However, their usage in brain imaging, though increasing, is limited by the fact that they are used as out-of-the-box techniques and are seldom tailored to the domain specific constraints and knowledge pertaining to medical imaging, which leads to difficulties in interpretation of results. In order to address the above shortcomings, we propose Eigenanatomy (EANAT), a general framework for sparse matrix factorization. Its goal is to statistically learn the boundaries of and connections between brain regions by weighing both the data and prior neuroanatomical knowledge. Although EANAT incorporates some neuroanatomical prior knowledge in the form of connectedness and smoothness constraints, it can still be difficult for clinicians to interpret the results in specific domains where network-specific hypotheses exist. We thus extend EANAT and present a novel framework for prior-constrained sparse decomposition of matrices derived from brain imaging data, called Prior Based Eigenanatomy (p-Eigen). We formulate our solution in terms of a prior-constrained l1 penalized (sparse) principal component analysis. Experimental evaluation confirms that p-Eigen extracts biologically-relevant, patient-specific functional parcels and that it significantly aids classification of Mild Cognitive Impairment when compared to state-of-the-art competing approaches

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)

Eesti keele üldvaldkonna tekstide laia kattuvusega automaatne sündmusanalüüs

Seoses tekstide suuremahulise digitaliseerimisega ning digitaalse tekstiloome järjest laiema levikuga on tohutul hulgal loomuliku keele tekste muutunud ja muutumas masinloetavaks. Masinloetavus omab potentsiaali muuta tekstimassiivid inimeste jaoks lihtsamini hallatavaks, nt lubada rakendusi nagu automaatne sisukokkuvõtete tegemine ja tekstide põhjal küsimustele vastamine, ent paraku ei ulatu praegused automaatanalüüsi võimalused tekstide sisu tegeliku mõistmiseni. Oletatakse, tekstide sisu mõistvale automaatanalüüsile viib meid lähemale sündmusanalüüs – kuna paljud tekstid on narratiivse ülesehitusega, tõlgendatavad kui „sündmuste kirjeldused”, peaks tekstidest sündmuste eraldamine ja formaalsel kujul esitamine pakkuma alust mitmete „teksti mõistmist” nõudvate keeletehnoloogia rakenduste loomisel. Käesolevas väitekirjas uuritakse, kuivõrd saab eestikeelsete tekstide sündmusanalüüsi käsitleda kui avatud sündmuste hulka ja üldvaldkonna tekste hõlmavat automaatse lingvistilise analüüsi ülesannet. Probleemile lähenetakse eesti keele automaatanalüüsi kontekstis uudsest, sündmuste ajasemantikale keskenduvast perspektiivist. Töös kohandatakse eesti keelele TimeML märgendusraamistik ja luuakse raamistikule toetuv automaatne ajaväljendite tuvastaja ning ajasemantilise märgendusega (sündmusviidete, ajaväljendite ning ajaseoste märgendusega) tekstikorpus; analüüsitakse korpuse põhjal inimmärgendajate kooskõla sündmusviidete ja ajaseoste määramisel ning lõpuks uuritakse võimalusi ajasemantika-keskse sündmusanalüüsi laiendamiseks geneeriliseks sündmusanalüüsiks sündmust väljendavate keelendite samaviitelisuse lahendamise näitel. Töö pakub suuniseid tekstide ajasemantika ja sündmusstruktuuri märgenduse edasiarendamiseks tulevikus ning töös loodud keeleressurssid võimaldavad nii konkreetsete lõpp-rakenduste (nt automaatne ajaküsimustele vastamine) katsetamist kui ka automaatsete märgendustööriistade edasiarendamist.  Due to massive scale digitalisation processes and a switch from traditional means of written communication to digital written communication, vast amounts of human language texts are becoming machine-readable. Machine-readability holds a potential for easing human effort on searching and organising large text collections, allowing applications such as automatic text summarisation and question answering. However, current tools for automatic text analysis do not reach for text understanding required for making these applications generic. It is hypothesised that automatic analysis of events in texts leads us closer to the goal, as many texts can be interpreted as stories/narratives that are decomposable into events. This thesis explores event analysis as broad-coverage and general domain automatic language analysis problem in Estonian, and provides an investigation starting from time-oriented event analysis and tending towards generic event analysis. We adapt TimeML framework to Estonian, and create an automatic temporal expression tagger and a news corpus manually annotated for temporal semantics (event mentions, temporal expressions, and temporal relations) for the language; we analyse consistency of human annotation of event mentions and temporal relations, and, finally, provide a preliminary study on event coreference resolution in Estonian news. The current work also makes suggestions on how future research can improve Estonian event and temporal semantic annotation, and the language resources developed in this work will allow future experimentation with end-user applications (such as automatic answering of temporal questions) as well as provide a basis for developing automatic semantic analysis tools

Knowledge Extraction from Textual Resources through Semantic Web Tools and Advanced Machine Learning Algorithms for Applications in Various Domains

Nowadays there is a tremendous amount of unstructured data, often represented by texts, which is created and stored in variety of forms in many domains such as patients' health records, social networks comments, scientific publications, and so on. This volume of data represents an invaluable source of knowledge, but unfortunately it is challenging its mining for machines. At the same time, novel tools as well as advanced methodologies have been introduced in several domains, improving the efficacy and the efficiency of data-based services. Following this trend, this thesis shows how to parse data from text with Semantic Web based tools, feed data into Machine Learning methodologies, and produce services or resources to facilitate the execution of some tasks. More precisely, the use of Semantic Web technologies powered by Machine Learning algorithms has been investigated in the Healthcare and E-Learning domains through not yet experimented methodologies. Furthermore, this thesis investigates the use of some state-of-the-art tools to move data from texts to graphs for representing the knowledge contained in scientific literature. Finally, the use of a Semantic Web ontology and novel heuristics to detect insights from biological data in form of graph are presented. The thesis contributes to the scientific literature in terms of results and resources. Most of the material presented in this thesis derives from research papers published in international journals or conference proceedings

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