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Found 6 papers, 3 papers with code
Learning to Borrow– Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph Completion
In this paper, we propose and evaluate several methods to address this problem, where we borrow LDPs from the entity pairs that co-occur in sentences in the corpus (i. e. with mentions entity pairs) to represent entity pairs that do not co-occur in any sentence in the corpus (i. e. without mention entity pairs).
Learning to Borrow -- Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph Completion
In this paper, we propose and evaluate several methods to address this problem, where we borrow LDPs from the entity pairs that co-occur in sentences in the corpus (i. e. with mention entity pairs) to represent entity pairs that do not co-occur in any sentence in the corpus (i. e. without mention entity pairs).
Graph Convolution over Multiple Dependency Sub-graphs for Relation Extraction
We propose a contextualised graph convolution network over multiple dependency-based sub-graphs for relation extraction.
Do not let the history haunt you -- Mitigating Compounding Errors in Conversational Question Answering
The Conversational Question Answering (CoQA) task involves answering a sequence of inter-related conversational questions about a contextual paragraph.
Contextualised Graph Attention for Improved Relation Extraction
This paper presents a contextualized graph attention network that combines edge features and multiple sub-graphs for improving relation extraction.
Combining Long Short Term Memory and Convolutional Neural Network for Cross-Sentence n-ary Relation Extraction
We propose in this paper a combined model of Long Short Term Memory and Convolutional Neural Networks (LSTM-CNN) that exploits word embeddings and positional embeddings for cross-sentence n-ary relation extraction.
