Comparing Transformer-based NER approaches for analysing textual medical diagnoses

The automated analysis of medical documents has grown in research interest in recent years as a consequence of the social relevance of the thematic and the difficulties often encountered with short and very specific documents. In particular, this fervent area of research has stimulated the development of several techniques of automatic document classification, question answering, and name entity recognition (NER). Nevertheless, many open issues must be addressed to obtain results that are satisfactory for a field in which the effectiveness of predictions is a fundamental factor in order not to make mistakes that could compromise people’s lives. To this end, we focused on the name entity recognition task from medical documents and, in this work, we will discuss the results we obtained by our hybrid approach. In order to take advantage of the most relevant findings in the field of natural language processing, we decided to focus on deep neural network models. We compared several configurations of our model by varying the transformer architecture, such as BERT, RoBERTa and ELECTRA, until we obtained a configuration that we considered the best for our goals. The most promising model was used to participate in the SpRadIE task of the annual CLEF (Conference and Labs of the Evaluation Forum). The obtained results are encouraging and can be of reference for future studies on the topic

GM-CTSC at SemEval-2020 Task 1: Gaussian Mixtures Cross Temporal Similarity Clustering

This paper describes the system proposed for the SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection. We focused our approach on the detection problem. Given the semantics of words captured by temporal word embeddings in different time periods, we investigate the use of unsupervised methods to detect when the target word has gained or loosed senses. To this end, we defined a new algorithm based on Gaussian Mixture Models to cluster the target similarities computed over the two periods. We compared the proposed approach with a number of similarity-based thresholds. We found that, although the performance of the detection methods varies across the word embedding algorithms, the combination of Gaussian Mixture with Temporal Referencing resulted in our best system

Covid19/IT the digital side of Covid19: A picture from Italy with clustering and taxonomy

The Covid19 pandemic has significantly impacted on our lives, triggering a strong reaction resulting in vaccines, more effective diagnoses and therapies, policies to contain the pandemic outbreak, to name but a few. A significant contribution to their success comes from the computer science and information technology communities, both in support to other disciplines and as the primary driver of solutions for, e.g., diagnostics, social distancing, and contact tracing. In this work, we surveyed the Italian computer science and engineering community initiatives against the Covid19 pandemic. The 128 responses thus collected document the response of such a community during the first pandemic wave in Italy (February-May 2020), through several initiatives carried out by both single researchers and research groups able to promptly react to Covid19, even remotely. The data obtained by the survey are here reported, discussed and further investigated by Natural Language Processing techniques, to generate semantic clusters based on embedding representations of the surveyed activity descriptions. The resulting clusters have been then used to extend an existing Covid19 taxonomy with the classification of related research activities in computer science and information technology areas, summarizing this work contribution through a reproducible survey-to-taxonomy methodology

Semantically-Aware Retrieval of Oceanographic Phenomena Annotated on Satellite Images

Scientists in the marine domain process satellite images in order to extract information that can be used for monitoring, understanding, and forecasting of marine phenomena, such as turbidity, algal blooms and oil spills. The growing need for effective retrieval of related information has motivated the adoption of semantically aware strategies on satellite images with different spatiotemporal and spectral characteristics. A big issue of these approaches is the lack of coincidence between the information that can be extracted from the visual data and the interpretation that the same data have for a user in a given situation. In this work, we bridge this semantic gap by connecting the quantitative elements of the Earth Observation satellite images with the qualitative information, modelling this knowledge in a marine phenomena ontology and developing a question answering mechanism based on natural language that enables the retrieval of the most appropriate data for each user’s needs. The main objective of the presented methodology is to realize the content-based search of Earth Observation images related to the marine application domain on an application-specific basis that can answer queries such as “Find oil spills that occurred this year in the Adriatic Sea”

AlBERTo: Modeling Italian Social Media Language with BERT

Natural Language Processing tasks recently achieved considerable interest and progresses following the development of numerous innovative artificial intelligence models released in recent years. The increase in available computing power has made possible the application of machine learning approaches on a considerable amount of textual data, demonstrating how they can obtain very encouraging results in challenging NLP tasks by generalizing the properties of natural language directly from the data. Models such as ELMo, GPT/GPT-2, BERT, ERNIE, and RoBERTa have proved to be extremely useful in NLP tasks such as entailment, sentiment analysis, and question answering. The availability of these resources mainly in the English language motivated us towards the realization of AlBERTo, a natural language model based on BERT and trained on the Italian language. We decided to train AlBERTo from scratch on social network language, Twitter in particular, because many of the classic tasks of content analysis are oriented to data extracted from the digital sphere of users. The model was distributed to the community through a repository on GitHub and the Transformers library (Wolf et al. 2019) released by the development group huggingface.co. We have evaluated the validity of the model on the classification tasks of sentiment polarity, irony, subjectivity, and hate speech. The specifications of the model, the code developed for training and fine-tuning, and the instructions for using it in a research project are freely available

HealthAssistantBot: A Personal Health Assistant for the Italian Language

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IT-Covid19-IT: la risposta della comunità informatica italiana alla pandemia

La pandemia Covid19 ha avuto un forte impatto sulle nostre vite, anche da accademici. Ne è scaturita una reazione veemente della comunità scientifica i cui risultati sono sotto gli occhi di tutti: vaccini, terapie più puntuali ed efficaci, politiche di contenimento mirate, etc. A tutto ciò, l’informatica ha contribuito in maniera determinante, spesso con funzioni di supporto e servizio alle altre discipline, talvolta in primo piano con applicazioni specifiche, per esempio, per il distanziamento sociale ed il tracciamento dei contatti. Questo articolo prova a fare una fotografia della reazione della comunità informatica italiana alla pandemia Covid19, elaborando i dati ottenuti da un censimento condotto nel maggio 2020, a seguito della prima ondata, dalla Task Force Covid19-IT istituita allo scopo dal CINI (Consorzio Interuniversitario Nazionale per l’Informatica). I dati ottenuti dalle 131 proposte censite raccontano di una risposta decisa ed articolata della comunità, nata spontaneamente da centinaia di iniziative autonome distribuite su tutto il territorio nazionale e che deve proseguire, magari evolvendo in forme più organizzate