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Found 19 papers, 3 papers with code
Approaching SMM4H with auto-regressive language models and back-translation
We describe our submissions to the 6th edition of the Social Media Mining for Health Applications (SMM4H) shared task.
mattica@SMM4H’22: Leveraging sentiment for stance & premise joint learning
This paper describes our submissions to the Social Media Mining for Health Applications (SMM4H) shared task 2022.
Annotating the Pandemic: Named Entity Recognition and Normalisation in COVID-19 Literature
In our approach, we are using a dictionary-based system for its high recall in conjunction with two models based on BioBERT for their accuracy.
COVID-19 Twitter Monitor: Aggregating and Visualizing COVID-19 Related Trends in Social Media
Social media platforms offer extensive information about the development of the COVID-19 pandemic and the current state of public health.
Boosting Radiology Report Generation by Infusing Comparison Prior
To tackle this issue, we propose a novel approach that leverages a rule-based labeler to extract comparison prior information from radiology reports.
Enhancing Biomedical Relation Extraction with Transformer Models using Shortest Dependency Path Features and Triplet Information
We propose utilizing the Shortest Dependency Path (SDP) features for constructing data samples by pruning out noisy information and selecting the most representative samples for model learning.
Ranked #9 on
Relation Extraction
on CDR
Negation typology and general representation models for cross-lingual zero-shot negation scope resolution in Russian, French, and Spanish.
Negation is a linguistic universal that poses difficulties for cognitive and computational processing.
SST-BERT at SemEval-2020 Task 1: Semantic Shift Tracing by Clustering in BERT-based Embedding Spaces
Lexical semantic change detection (also known as semantic shift tracing) is a task of identifying words that have changed their meaning over time.
MultiGBS: A multi-layer graph approach to biomedical summarization
Automatic text summarization methods generate a shorter version of the input text to assist the reader in gaining a quick yet informative gist.
Parallel sequence tagging for concept recognition
In all 20 annotation sets of the concept-annotation task, our system outperforms the pipeline system reported as a baseline in the CRAFT shared task 2019.
UZH@CRAFT-ST: a Sequence-labeling Approach to Concept Recognition
As our submission to the CRAFT shared task 2019, we present two neural approaches to concept recognition.
Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review
There is a notable use of relatively simple methods, such as shallow classifiers (or combination with rule-based methods), due to the interpretability of predictions, which still represents a significant issue for more complex methods.
Approaching SMM4H with Merged Models and Multi-task Learning
We describe our submissions to the 4th edition of the Social Media Mining for Health Applications (SMM4H) shared task.
UZH@SMM4H: System Descriptions
Our team at the University of Z{\"u}rich participated in the first 3 of the 4 sub-tasks at the Social Media Mining for Health Applications (SMM4H) shared task.
Author Name Disambiguation in MEDLINE Based on Journal Descriptors and Semantic Types
The evaluation of different feature models shows that their inclusion has an impact equivalent to that of other important features such as email address.
The PsyMine Corpus - A Corpus annotated with Psychiatric Disorders and their Etiological Factors
We present the first version of a corpus annotated for psychiatric disorders and their etiological factors.
Using Large Biomedical Databases as Gold Annotations for Automatic Relation Extraction
We show how to use large biomedical databases in order to obtain a gold standard for training a machine learning system over a corpus of biomedical text.
Dependency parsing for interaction detection in pharmacogenomics
We give an overview of our approach to the extraction of interactions between pharmacogenomic entities like drugs, genes and diseases and suggest classes of interaction types driven by data from PharmGKB and partly following the top level ontology WordNet and biomedical types from BioNLP.
