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Found 11 papers, 4 papers with code
Medical Crossing: a Cross-lingual Evaluation of Clinical Entity Linking
Medical data annotation requires highly qualified expertise.
RuCCoN: Clinical Concept Normalization in Russian
We present RuCCoN, a new dataset for clinical concept normalization in Russian manually annotated by medical professionals.
Benchmarking Multilabel Topic Classification in the Kyrgyz Language
Kyrgyz is a very underrepresented language in terms of modern natural language processing resources.
Machine Learning for SAT: Restricted Heuristics and New Graph Representations
Boolean satisfiability (SAT) is a fundamental NP-complete problem with many applications, including automated planning and scheduling.
DetIE: Multilingual Open Information Extraction Inspired by Object Detection
Our model sets the new state of the art performance of 67. 7% F1 on CaRB evaluated as OIE2016 while being 3. 35x faster at inference than previous state of the art.
Ranked #1 on
Open Information Extraction
on LSOIE
Near-Zero-Shot Suggestion Mining with a Little Help from WordNet
In this work, we explore the constructive side of online reviews: advice, tips, requests, and suggestions that users provide about goods, venues, services, and other items of interest.
Ad Lingua: Text Classification Improves Symbolism Prediction in Image Advertisements
Understanding image advertisements is a challenging task, often requiring non-literal interpretation.
Improving unsupervised neural aspect extraction for online discussions using out-of-domain classification
Deep learning architectures based on self-attention have recently achieved and surpassed state of the art results in the task of unsupervised aspect extraction and topic modeling.
RecVAE: a New Variational Autoencoder for Top-N Recommendations with Implicit Feedback
Recent research has shown the advantages of using autoencoders based on deep neural networks for collaborative filtering.
Ranked #2 on
Recommendation Systems
on Netflix
AspeRa: Aspect-Based Rating Prediction Based on User Reviews
We propose a novel Aspect-based Rating Prediction model (AspeRa) that estimates user rating based on review texts for the items.
AspeRa: Aspect-based Rating Prediction Model
We propose a novel end-to-end Aspect-based Rating Prediction model (AspeRa) that estimates user rating based on review texts for the items and at the same time discovers coherent aspects of reviews that can be used to explain predictions or profile users.
