Search Results for author:
Found 8 papers, 8 papers with code
ReSel: N-ary Relation Extraction from Scientific Text and Tables by Learning to Retrieve and Select
We study the problem of extracting N-ary relation tuples from scientific articles.
SciAnnotate: A Tool for Integrating Weak Labeling Sources for Sequence Labeling
Compared to frequently used text annotation tools, our annotation tool allows for the development of weak labels in addition to providing a manual annotation experience.
Sparse Conditional Hidden Markov Model for Weakly Supervised Named Entity Recognition
Weakly supervised named entity recognition methods train label models to aggregate the token annotations of multiple noisy labeling functions (LFs) without seeing any manually annotated labels.
Named Entity Recognition
Weakly-Supervised Named Entity Recognition
WRENCH: A Comprehensive Benchmark for Weak Supervision
To address these problems, we introduce a benchmark platform, WRENCH, for thorough and standardized evaluation of WS approaches.
BERTifying the Hidden Markov Model for Multi-Source Weakly Supervised Named Entity Recognition
To address this challenge, we propose a conditional hidden Markov model (CHMM), which can effectively infer true labels from multi-source noisy labels in an unsupervised way.
Denoising Multi-Source Weak Supervision for Neural Text Classification
We study the problem of learning neural text classifiers without using any labeled data, but only easy-to-provide rules as multiple weak supervision sources.
Transformer-Based Neural Text Generation with Syntactic Guidance
We study the problem of using (partial) constituency parse trees as syntactic guidance for controlled text generation.
STEAM: Self-Supervised Taxonomy Expansion with Mini-Paths
We propose a self-supervised taxonomy expansion model named STEAM, which leverages natural supervision in the existing taxonomy for expansion.
