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Found 6 papers, 3 papers with code
Self-supervised Answer Retrieval on Clinical Notes
Retrieving answer passages from long documents is a complex task requiring semantic understanding of both discourse and document context.
Learning Contextualized Document Representations for Healthcare Answer Retrieval
Our model leverages a dual encoder architecture with hierarchical LSTM layers and multi-task training to encode the position of clinical entities and aspects alongside the document discourse.
SECTOR: A Neural Model for Coherent Topic Segmentation and Classification
From our extensive evaluation of 20 architectures, we report a highest score of 71. 6% F1 for the segmentation and classification of 30 topics from the English city domain, scored by our SECTOR LSTM model with bloom filter embeddings and bidirectional segmentation.
TASTY: Interactive Entity Linking As-You-Type
We introduce TASTY (Tag-as-you-type), a novel text editor for interactive entity linking as part of the writing process.
Robust Named Entity Recognition in Idiosyncratic Domains
We propose a generic and robust approach for high-recall named entity recognition.
