Fabio Rinaldi


2021

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Proceedings of the 12th International Workshop on Health Text Mining and Information Analysis
Eben Holderness | Antonio Jimeno Yepes | Alberto Lavelli | Anne-Lyse Minard | James Pustejovsky | Fabio Rinaldi
Proceedings of the 12th International Workshop on Health Text Mining and Information Analysis

2020

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SST-BERT at SemEval-2020 Task 1 : Semantic Shift Tracing by Clustering in BERT-based Embedding SpacesSST-BERT at SemEval-2020 Task 1: Semantic Shift Tracing by Clustering in BERT-based Embedding Spaces
Vani Kanjirangat | Sandra Mitrovic | Alessandro Antonucci | Fabio Rinaldi
Proceedings of the Fourteenth Workshop on Semantic Evaluation

Lexical semantic change detection (also known as semantic shift tracing) is a task of identifying words that have changed their meaning over time. Unsupervised semantic shift tracing, focal point of SemEval2020, is particularly challenging. Given the unsupervised setup, in this work, we propose to identify clusters among different occurrences of each target word, considering these as representatives of different word meanings. As such, disagreements in obtained clusters naturally allow to quantify the level of semantic shift per each target word in four target languages. To leverage this idea, clustering is performed on contextualized (BERT-based) embeddings of word occurrences. The obtained results show that our approach performs well both measured separately (per language) and overall, where we surpass all provided SemEval baselines.

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Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis
Eben Holderness | Antonio Jimeno Yepes | Alberto Lavelli | Anne-Lyse Minard | James Pustejovsky | Fabio Rinaldi
Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis

2019

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UZH@CRAFT-ST : a Sequence-labeling Approach to Concept RecognitionUZH@CRAFT-ST: a Sequence-labeling Approach to Concept Recognition
Lenz Furrer | Joseph Cornelius | Fabio Rinaldi
Proceedings of The 5th Workshop on BioNLP Open Shared Tasks

As our submission to the CRAFT shared task 2019, we present two neural approaches to concept recognition. We propose two different systems for joint named entity recognition (NER) and normalization (NEN), both of which model the task as a sequence labeling problem. Our first system is a BiLSTM network with two separate outputs for NER and NEN trained from scratch, whereas the second system is an instance of BioBERT fine-tuned on the concept-recognition task. We exploit two strategies for extending concept coverage, ontology pretraining and backoff with a dictionary lookup. Our results show that the backoff strategy effectively tackles the problem of unseen concepts, addressing a major limitation of the chosen design. In the cross-system comparison, BioBERT proves to be a strong basis for creating a concept-recognition system, although some entity types are predicted more accurately by the BiLSTM-based system.

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Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)
Eben Holderness | Antonio Jimeno Yepes | Alberto Lavelli | Anne-Lyse Minard | James Pustejovsky | Fabio Rinaldi
Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)

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Approaching SMM4H with Merged Models and Multi-task LearningSMM4H with Merged Models and Multi-task Learning
Tilia Ellendorff | Lenz Furrer | Nicola Colic | Noëmi Aepli | Fabio Rinaldi
Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task

We describe our submissions to the 4th edition of the Social Media Mining for Health Applications (SMM4H) shared task. Our team (UZH) participated in two sub-tasks : Automatic classifications of adverse effects mentions in tweets (Task 1) and Generalizable identification of personal health experience mentions (Task 4). For our submissions, we exploited ensembles based on a pre-trained language representation with a neural transformer architecture (BERT) (Tasks 1 and 4) and a CNN-BiLSTM(-CRF) network within a multi-task learning scenario (Task 1). These systems are placed on top of a carefully crafted pipeline of domain-specific preprocessing steps.

2018

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Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis
Alberto Lavelli | Anne-Lyse Minard | Fabio Rinaldi
Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis