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Found 19 papers, 11 papers with code
Metadata Shaping: A Simple Approach for Knowledge-Enhanced Language Models
We observe proposed methods typically start with a base LM and data that has been annotated with entity metadata, then change the model, by modifying the architecture or introducing auxiliary loss terms to better capture entity knowledge.
NECA: Network-Embedded Deep Representation Learning for Categorical Data
We propose NECA, a deep representation learning method for categorical data.
Metadata Shaping: Natural Language Annotations for the Tail
Since rare entities and facts are prevalent in the queries users submit to popular applications such as search and personal assistant systems, improving the ability of LMs to reliably capture knowledge over rare entities is a pressing challenge studied in significant prior work.
Cross-Domain Data Integration for Named Entity Disambiguation in Biomedical Text
Named entity disambiguation (NED), which involves mapping textual mentions to structured entities, is particularly challenging in the medical domain due to the presence of rare entities.
Precise High-Dimensional Asymptotics for Quantifying Heterogeneous Transfers
Intuitively, the transfer effect from one task to another task depends on dataset shifts such as sample sizes and covariance matrices.
Bootleg: Chasing the Tail with Self-Supervised Named Entity Disambiguation
A challenge for named entity disambiguation (NED), the task of mapping textual mentions to entities in a knowledge base, is how to disambiguate entities that appear rarely in the training data, termed tail entities.
Ranked #1 on
Entity Disambiguation
on AIDA-CoNLL
(Micro-F1 metric)
Train and You'll Miss It: Interactive Model Iteration with Weak Supervision and Pre-Trained Embeddings
Our goal is to enable machine learning systems to be trained interactively.
Understanding and Improving Information Transfer in Multi-Task Learning
We investigate multi-task learning approaches that use a shared feature representation for all tasks.
On the Generalization Effects of Linear Transformations in Data Augmentation
We validate our proposed scheme on image and text datasets.
Ivy: Instrumental Variable Synthesis for Causal Inference
To relax these assumptions, we propose Ivy, a new method to combine IV candidates that can handle correlated and invalid IV candidates in a robust manner.
Understanding the Downstream Instability of Word Embeddings
To theoretically explain this tradeoff, we introduce a new measure of embedding instability---the eigenspace instability measure---which we prove bounds the disagreement in downstream predictions introduced by the change in word embeddings.
Slice-based Learning: A Programming Model for Residual Learning in Critical Data Slices
In real-world machine learning applications, data subsets correspond to especially critical outcomes: vulnerable cyclist detections are safety-critical in an autonomous driving task, and "question" sentences might be important to a dialogue agent's language understanding for product purposes.
Snorkel: Rapid Training Data Creation with Weak Supervision
In a user study, subject matter experts build models 2. 8x faster and increase predictive performance an average 45. 5% versus seven hours of hand labeling.
Robust Sparse Coding via Self-Paced Learning
Sparse coding (SC) is attracting more and more attention due to its comprehensive theoretical studies and its excellent performance in many signal processing applications.
SwellShark: A Generative Model for Biomedical Named Entity Recognition without Labeled Data
We present SwellShark, a framework for building biomedical named entity recognition (NER) systems quickly and without hand-labeled data.
Ranked #2 on
Weakly-Supervised Named Entity Recognition
on BC5CDR
Fonduer: Knowledge Base Construction from Richly Formatted Data
We focus on knowledge base construction (KBC) from richly formatted data.
Databases
Data Programming: Creating Large Training Sets, Quickly
Additionally, in initial user studies we observed that data programming may be an easier way for non-experts to create machine learning models when training data is limited or unavailable.
Incremental Knowledge Base Construction Using DeepDive
Populating a database with unstructured information is a long-standing problem in industry and research that encompasses problems of extraction, cleaning, and integration.
Feature Engineering for Knowledge Base Construction
Our approach to KBC is based on joint probabilistic inference and learning, but we do not see inference as either a panacea or a magic bullet: inference is a tool that allows us to be systematic in how we construct, debug, and improve the quality of such systems.
