Search Results for author:
Found 18 papers, 2 papers with code
Leveraging knowledge sources for detecting self-reports of particular health issues on social media
This paper investigates incorporating quality knowledge sources developed by experts for the medical domain as well as syntactic information for classification of tweets into four different health oriented categories.
Competing Independent Modules for Knowledge Integration and Optimization
This paper presents a neural framework of untied independent modules, used here for integrating off the shelf knowledge sources such as language models, lexica, POS information, and dependency relations.
CLaC at SMM4H 2020: Birth Defect Mention Detection
For the detection of personal tweets, where a parent speaks of a child’s birth defect, CLaC combines ELMo word embeddings and gazetteer lists from external resources with a GCNN (for encoding dependencies), in a multi layer, transformer inspired architecture.
Multi-input Recurrent Independent Mechanisms for leveraging knowledge sources: Case studies on sentiment analysis and health text mining
This paper presents a way to inject and leverage existing knowledge from external sources in a Deep Learning environment, extending the recently proposed Recurrent Independent Mechnisms (RIMs) architecture, which comprises a set of interacting yet independent modules.
Interacting Knowledge Sources, Inspection and Analysis: Case-studies on Biomedical text processing
In this paper we investigate the recently proposed multi-input RIM for inspectability.
Comparing and combining some popular NER approaches on Biomedical tasks
Lastly, we implement a system that learns to combine the predictions of SEQ and SpanPred, generating systems that consistently give high recall and high F1 across all 4 datasets.
Ranked #6 on
Named Entity Recognition (NER)
on NCBI-disease
CLaC at SemEval-2023 Task 2: Comparing Span-Prediction and Sequence-Labeling approaches for NER
This paper summarizes the CLaC submission for the MultiCoNER 2 task which concerns the recognition of complex, fine-grained named entities.
CLaCLab at SocialDisNER: Using Medical Gazetteers for Named-Entity Recognition of Disease Mentions in Spanish Tweets
This paper summarizes the CLaC submission for SMM4H 2022 Task 10 which concerns the recognition of diseases mentioned in Spanish tweets.
Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations
To close the gap, we organized the BioCreative LitCovid track to call for a community effort to tackle automated topic annotation for COVID-19 literature.
CLaC-np at SemEval-2021 Task 8: Dependency DGCNN
MeasEval aims at identifying quantities along with the entities that are measured with additional properties within English scientific documents.
CLaC-BP at SemEval-2021 Task 8: SciBERT Plus Rules for MeasEval
We analyze ablation experiments and demonstrate how the system components, namely tokenizer, unit identifier, modifier classifier, and language model, affect the overall score.
CLaC at SemEval-2020 Task 5: Muli-task Stacked Bi-LSTMs
We consider detection of the span of antecedents and consequents in argumentative prose a structural, grammatical task.
Adverse Drug Effect and Personalized Health Mentions, CLaC at SMM4H 2019, Tasks 1 and 4
CLaC labs participated in Task 1 and 4 of SMM4H 2019.
CLaC at SMM4H Task 1, 2, and 4
CLaC Labs participated in Tasks 1, 2, and 4 using the same base architecture for all tasks with various parameter variations.
