Search Results for author:
Found 20 papers, 7 papers with code
TBD3: A Thresholding-Based Dynamic Depression Detection from Social Media for Low-Resource Users
To complement this evaluation, we propose a dynamic thresholding technique that adjusts the classifier’s sensitivity as a function of the number of posts a user has.
Lexically-Accelerated Dense Retrieval
We introduce 'LADR' (Lexically-Accelerated Dense Retrieval), a simple-yet-effective approach that improves the efficiency of existing dense retrieval models without compromising on retrieval effectiveness.
Caching Historical Embeddings in Conversational Search
Our achieved high cache hit rates significantly improve the responsiveness of conversational systems while likewise reducing the number of queries managed on the search back-end.
Self-supervised Representation Learning on Electronic Health Records with Graph Kernel Infomax
We propose Graph Kernel Infomax, a self-supervised graph kernel learning approach on the graphical representation of EHR, to overcome the previous problems.
TAR on Social Media: A Framework for Online Content Moderation
Content moderation (removing or limiting the distribution of posts based on their contents) is one tool social networks use to fight problems such as harassment and disinformation.
Certifying One-Phase Technology-Assisted Reviews
Technology-assisted review (TAR) workflows based on iterative active learning are widely used in document review applications.
On Minimizing Cost in Legal Document Review Workflows
Technology-assisted review (TAR) refers to human-in-the-loop machine learning workflows for document review in legal discovery and other high recall review tasks.
Heuristic Stopping Rules For Technology-Assisted Review
Technology-assisted review (TAR) refers to human-in-the-loop active learning workflows for finding relevant documents in large collections.
Goldilocks: Just-Right Tuning of BERT for Technology-Assisted Review
We indeed find that the pre-trained BERT model reduces review cost by 10% to 15% in TAR workflows simulated on the RCV1-v2 newswire collection.
The Analysis from Nonlinear Distance Metric to Kernel-based Drug Prescription Prediction System
Distance metrics and their nonlinear variant play a crucial role in machine learning based real-world problem solving.
Cross-Global Attention Graph Kernel Network Prediction of Drug Prescription
We present an end-to-end, interpretable, deep-learning architecture to learn a graph kernel that predicts the outcome of chronic disease drug prescription.
GUIR at SemEval-2020 Task 12: Domain-Tuned Contextualized Models for Offensive Language Detection
Offensive language detection is an important and challenging task in natural language processing.
Interaction Matching for Long-Tail Multi-Label Classification
We present an elegant and effective approach for addressing limitations in existing multi-label classification models by incorporating interaction matching, a concept shown to be useful for ad-hoc search result ranking.
Efficient Document Re-Ranking for Transformers by Precomputing Term Representations
Deep pretrained transformer networks are effective at various ranking tasks, such as question answering and ad-hoc document ranking.
Training Curricula for Open Domain Answer Re-Ranking
We show that the proposed heuristics can be used to build a training curriculum that down-weights difficult samples early in the training process.
Expansion via Prediction of Importance with Contextualization
We also observe that the performance is additive with the current leading first-stage retrieval methods, further narrowing the gap between inexpensive and cost-prohibitive passage ranking approaches.
Topical Result Caching in Web Search Engines
The results of queries characterized by a topic are kept in the fraction of the cache dedicated to it.
Content-Based Weak Supervision for Ad-Hoc Re-Ranking
One challenge with neural ranking is the need for a large amount of manually-labeled relevance judgments for training.
Effects of Sampling on Twitter Trend Detection
In this work we use a larger feed to investigate the effects of sampling on Twitter trend detection.
A Framework for Public Health Surveillance
With the rapid growth of social media, there is increasing potential to augment traditional public health surveillance methods with data from social media.
