Search Results for author:
Found 9 papers, 4 papers with code
CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learning
Named Entity Recognition (NER) in Few-Shot setting is imperative for entity tagging in low resource domains.
Ranked #7 on
Few-shot NER
on Few-NERD (INTRA)
(using extra training data)
NePTuNe: Neural Powered Tucker Network for Knowledge Graph Completion
Knowledge graphs link entities through relations to provide a structured representation of real world facts.
Ranked #11 on
Link Prediction
on FB15k-237
Simple and Effective Few-Shot Named Entity Recognition with Structured Nearest Neighbor Learning
We present a simple few-shot named entity recognition (NER) system based on nearest neighbor learning and structured inference.
Revisiting Few-sample BERT Fine-tuning
We empirically test the impact of these factors, and identify alternative practices that resolve the commonly observed instability of the process.
SPARSE: Structured Prediction using Argument-Relative Structured Encoding
We apply our approach to the second-order structured prediction task studied in the 2016/2017 Belief and Sentiment analysis evaluations (BeSt): given a document and its entities, relations, and events (including metadata and mentions), determine the sentiment of each entity towards every relation and event in the document.
Nested Named Entity Recognition Revisited
We propose a novel recurrent neural network-based approach to simultaneously handle nested named entity recognition and nested entity mention detection.
Going out on a limb: Joint Extraction of Entity Mentions and Relations without Dependency Trees
We also compare our model with an end-to-end tree-based LSTM model (SPTree) by Miwa and Bansal (2016) and show that our model performs within 1{\%} on entity mentions and 2{\%} on relations.
Ranked #9 on
Relation Extraction
on ACE 2004
