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Found 13 papers, 8 papers with code
Dataset Debt in Biomedical Language Modeling
Large-scale language modeling and natural language prompting have demonstrated exciting capabilities for few and zero shot learning in NLP.
BEEDS: Large-Scale Biomedical Event Extraction using Distant Supervision and Question Answering
Automatic extraction of event structures from text is a promising way to extract important facts from the evergrowing amount of biomedical literature.
Extend, don’t rebuild: Phrasing conditional graph modification as autoregressive sequence labelling
Deriving and modifying graphs from natural language text has become a versatile basis technology for information extraction with applications in many subfields, such as semantic parsing or knowledge graph construction.
WBI at MEDIQA 2021: Summarizing Consumer Health Questions with Generative Transformers
This paper describes our contribution for the MEDIQA-2021 Task 1 question summarization competition.
Biomedical Event Extraction as Multi-turn Question Answering
Biomedical event extraction from natural text is a challenging task as it searches for complex and often nested structures describing specific relationships between multiple molecular entities, such as genes, proteins, or cellular components.
Establishing Trustworthiness: Rethinking Tasks and Model Evaluation
Language understanding is a multi-faceted cognitive capability, which the Natural Language Processing (NLP) community has striven to model computationally for decades.
ActiveAED: A Human in the Loop Improves Annotation Error Detection
This problem has been addressed with Annotation Error Detection (AED) models, which can flag such errors for human re-annotation.
The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset
As language models grow ever larger, the need for large-scale high-quality text datasets has never been more pressing, especially in multilingual settings.
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions.
BigBIO: A Framework for Data-Centric Biomedical Natural Language Processing
Training and evaluating language models increasingly requires the construction of meta-datasets --diverse collections of curated data with clear provenance.
HunFlair: An Easy-to-Use Tool for State-of-the-Art Biomedical Named Entity Recognition
Summary: Named Entity Recognition (NER) is an important step in biomedical information extraction pipelines.
NLProlog: Reasoning with Weak Unification for Question Answering in Natural Language
In contrast, neural models can cope very well with ambiguity by learning distributed representations of words and their composition from data, but lead to models that are difficult to interpret.
NLProlog: Reasoning with Weak Unification for Natural Language Question Answering
Currently, most work in natural language processing focuses on neural networks which learn distributed representations of words and their composition, thereby performing well in the presence of large linguistic variability.
