257 research outputs found
Exploiting massively parallel architectures for the solution of diffusion and propagation problems
No full textMany problems in several fields like physics, chemistry, biology and engineering lack an analytical solution able to provide a satisfactory phenomena description. Then a numerical solution becomes the only viable alternative. The use of massively parallel architectures often allows one to obtain in an easy way a comprehensive picture of the behaviour of the solution. We present here a computational model applied to two different physical problems; our work demonstrates the effectiveness of the approach and its extensibility to many classes of problems in different fields
Compiling bilingual dictionaries: AI-Assisted translation of Italian Multiword Expressions into English and French
No full textA computational analysis of transcribed speech of people living with dementia: The Anchise 2022 Corpus
No full textIntroduction: Automatic linguistic analysis can provide cost-effective, valuable clues to the diag
nosis of cognitive difficulties and to therapeutic practice, and hence impact positively on well
being. In this work, we analyzed transcribed conversations between elderly individuals living
with dementia and healthcare professionals. The material came from the Anchise 2022 Corpus, a
large collection of transcripts of conversations in Italian recorded in naturalistic conditions. The
aim of the work was to test the effectiveness of a number of automatic analyzes in finding cor
relations with the progression of dementia in individuals with cognitive decline as measured by
the Mini-Mental State Examination (MMSE) score, which is the only psychometric-clinical in
formation available on the participants in the conversations. Healthy controls (HC) were not
considered in this study, nor does the corpus itself include HCs. The main innovation and strength
of the work consists in the high ecological validity of the language analyzed (most of the literature
to date concerns controlled language experiments); in the use of Italian (there is little corpora for
Italian); in the size of the analyzed data (more than 200 conversations were considered); in the
adoption of a wide range of NLP methods, that span from traditional morphosyntactic investi
gation to deep linguistic models for conducting analyzes such as through perplexity, sentiment
(polarity) and emotions.
Methods: Analyzing real-world interactions not designed with computational analysis in mind,
such as is the case of the Anchise Corpus, is particularly challenging. To achieve the research
goals, a wide variety of tools were employed. These included traditional morphosyntactic analysis
based on digital linguistic biomarkers (DLBs), transformer-based language models, sentiment and
emotion analysis, and perplexity metrics. Analyzes were conducted both on the continuous range
of MMSE values and on the severe/moderate/mild categorization suggested by AIFA (Italian
Medicines Agency) guidelines, based on MMSE threshold values.
Results and discussion: Correlations between MMSE and individual DLBs were weak, up to 0.19 for
positive, and -0.21 for negative correlation values. Nevertheless, some correlations were statis
tically significant and consistent with the literature, suggesting that people with a greater degree
of impairment tend to show a reduced vocabulary, to have anomia, to adopt a more informal
linguist register, and to display a simplified use of verbs, with a decrease in the use of participles, gerunds, subjunctive moods, modal verbs, as well as a flattening in the use of the tenses towards
the present to the detriment of the past. The -0.26 inverse correlation between perplexity and
MMSE suggests that perplexity captures slightly more specific linguistic information, which can
complement the MMSE scores. In the categorization tasks, the classifier based on DLBs achieved
an F1 score of 0.79 for binary classification between SEVERE and MILD, and 0.61 for multi-label
categorization. Sentiment and emotion analyzes showed inverse trends for joy while MMSE scores
suggested that less impaired individuals were less joyful, or more “negative”, than others.
Considering the real-world context, this is consistent with the hypothesis of a gradual reduction in
awareness in individuals affected by dementia. Finally, integrating various profiles of analysis has
been proved to be effective in offering a wider picture of linguistic and communication deficits, as
well as more precise data regarding the progression of dementia
EliCoDe at MultiGED2023: fine-tuning XLM-RoBERTa for multilingual grammatical error detection
No full textIn this paper we describe the participation of our team, ELICODE, to the first shared, task on Multilingual Grammatical Error Detection, MultiGED, organised within the workshop series on Natural Language Processing for Computer-Assisted Language Learning (NLP4CALL). The multilingual shared task includes five languages: Czech, English, German, Italian and Swedish. The shared task is tackled as a binary classification task at token level aiming at identifying correct or incorrect tokens in the provided sentences. The submitted system is a token classifier based on XLMRoBERTa language model. We fine-tuned five different models—one per each language in the shared task. We devised two different experimental settings: first, we trained the models only on the provided training set, using the development set to select the model achieving the best performance across the training epochs; second, we trained each model jointly on training and development sets for 10 epochs, retaining the 10-epoch fine-tuned model. Our submitted systems, evaluated using F0.5 score, achieved the best performance in all evaluated test sets, except for the English REALEC data set (second classified). Code and models are publicly available at https://github.com/davidecolla/EliCoDe
Easy to Complete, Hard to Choose: Investigating LLM Performance on the ProverbIT Benchmark
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Semantic Coherence Dataset: Speech transcripts
No full textThe Semantic Coherence Dataset has been designed to experiment with semantic coherence metrics. More specifically, the dataset has been built to the ends of testing whether probabilistic measures, such as perplexity, provide stable scores to analyze spoken language. Perplexity, which was originally conceived as an information-theoretic measure to assess the probabilistic inference properties of language models, has recently been proven to be an appropriate tool to categorize speech transcripts based on semantic coherence accounts. More specifically, perplexity has been successfully employed to discriminate subjects suffering from Alzheimer Disease and healthy controls. Collected data include speech transcripts, intended to investigate semantic coherence at different levels: data are thus arranged into two classes, to investigate intra-subject semantic coherence, and inter-subject semantic coherence. In the former case transcripts from a single speaker can be employed to train and test language models and to explore whether the perplexity metric provides stable scores in assessing talks from that speaker, while allowing to distinguish between two different forms of speech, political rallies and interviews. In the latter case, models can be trained by employing transcripts from a given speaker, and then used to measure how stable the perplexity metric is when computed using the model from that user and transcripts from different users. Transcripts were extracted from talks lasting almost 13 hours (overall 12:45:17 and 120,326 tokens) for the former class; and almost 30 hours (29:47:34 and 252,270 tokens) for the latter one. Data herein can be reused to perform analyses on measures built on top of language models, and more in general on measures that are aimed at exploring the linguistic features of text documents
Map-based visualization of 2D/3D spatial data via stylization and tuning of information emphasis
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