50 research outputs found
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”
“Contro L’Odio”: A Platform for Detecting, Monitoring and Visualizing Hate Speech against Immigrants in Italian Social Media
The paper describes the Web platform built within the project “Contro l’Odio”, for monitoring and contrasting discrimination and hate speech against immigrants in Italy. It applies a combination of computational linguistics techniques for hate speech detection and data visualization tools on data drawn from Twitter.It allows users to access a huge amount of information through interactive maps, also tuning their view, e.g. visualizing the most viral tweets and interactively reducing the inherent complexity of data. Educational courses for high school students have been developed which are centered on the platform and focused on the deconstruction of negative stereotypes against immigrants, Rom and religious minorities, and on the creation of positive narratives. The data collected and analyzed by the platform are also currently used for benchmarking activities within an evaluation campaign, and for paving the way to new projects against hate