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Found 10 papers, 1 papers with code
Reproducing Neural Ensemble Classifier for Semantic Relation Extraction inScientific Papers
Within the natural language processing (NLP) community, shared tasks play an important role.
COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation
To combat COVID-19, both clinicians and scientists need to digest vast amounts of relevant biomedical knowledge in scientific literature to understand the disease mechanism and related biological functions.
Exploration and Discovery of the COVID-19 Literature through Semantic Visualization
We are developing semantic visualization techniques in order to enhance exploration and enable discovery over large datasets of complex networks of relations.
TMR: Evaluating NER Recall on Tough Mentions
We propose the Tough Mentions Recall (TMR) metrics to supplement traditional named entity recognition (NER) evaluation by examining recall on specific subsets of "tough" mentions: unseen mentions, those whose tokens or token/type combination were not observed in training, and type-confusable mentions, token sequences with multiple entity types in the test data.
Designing Multimodal Datasets for NLP Challenges
In this paper, we argue that the design and development of multimodal datasets for natural language processing (NLP) challenges should be enhanced in two significant respects: to more broadly represent commonsense semantic inferences; and to better reflect the dynamics of actions and events, through a substantive alignment of textual and visual information.
SemEval-2022 Task 9: R2VQ – Competence-based Multimodal Question Answering
In this task, we identify a challenge that is reflective of linguistic and cognitive competencies that humans have when speaking and reasoning.
Evaluating Retrieval for Multi-domain Scientific Publications
This paper provides an overview of the xDD/LAPPS Grid framework and provides results of evaluating the AskMe retrievalengine using the BEIR benchmark datasets.
Competence-based Question Generation
Models of natural language understanding often rely on question answering and logical inference benchmark challenges to evaluate the performance of a system.
Dense Paraphrasing for Textual Enrichment
Understanding inferences and answering questions from text requires more than merely recovering surface arguments, adjuncts, or strings associated with the query terms.
Common Ground Tracking in Multimodal Dialogue
Within Dialogue Modeling research in AI and NLP, considerable attention has been spent on ``dialogue state tracking'' (DST), which is the ability to update the representations of the speaker's needs at each turn in the dialogue by taking into account the past dialogue moves and history.
