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Found 5 papers, 2 papers with code
SPOCK at FinCausal 2022: Causal Information Extraction Using Span-Based and Sequence Tagging Models
Causal information extraction is an important task in natural language processing, particularly in finance domain.
Improving Neural Ranking Models with Traditional IR Methods
Neural ranking methods based on large transformer models have recently gained significant attention in the information retrieval community, and have been adopted by major commercial solutions.
A Cross-Domain Evaluation of Approaches for Causal Knowledge Extraction
Causal knowledge extraction is the task of extracting relevant causes and effects from text by detecting the causal relation.
Word Sense Induction with Knowledge Distillation from BERT
This paper proposes a two-stage method to distill multiple word senses from a pre-trained language model (BERT) by using attention over the senses of a word in a context and transferring this sense information to fit multi-sense embeddings in a skip-gram-like framework.
Position Masking for Improved Layout-Aware Document Understanding
Natural language processing for document scans and PDFs has the potential to enormously improve the efficiency of business processes.
