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Found 4 papers, 2 papers with code
Neural Relation Extraction Within and Across Sentence Boundaries
iDepNN models the shortest and augmented dependency paths via recurrent and recursive neural networks to extract relationships within (intra-) and across (inter-) sentence boundaries.
Ranked #1 on
Relation Extraction
on MUC6
Replicated Siamese LSTM in Ticketing System for Similarity Learning and Retrieval in Asymmetric Texts
The task is challenging due to significant term mismatch in the query and ticket pairs of asymmetric lengths, where subject is a short text but description and solution are multi-sentence texts.
Deep Temporal-Recurrent-Replicated-Softmax for Topical Trends over Time
We also introduce a metric (named as SPAN) to quantify the capability of dynamic topic model to capture word evolution in topics over time.
Table Filling Multi-Task Recurrent Neural Network for Joint Entity and Relation Extraction
This paper proposes a novel context-aware joint entity and word-level relation extraction approach through semantic composition of words, introducing a Table Filling Multi-Task Recurrent Neural Network (TF-MTRNN) model that reduces the entity recognition and relation classification tasks to a table-filling problem and models their interdependencies.
