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Found 9 papers, 4 papers with code
Zero-Shot Information Extraction to Enhance a Knowledge Graph Describing Silk Textiles
The knowledge of the European silk textile production is a typical case for which the information collected is heterogeneous, spread across many museums and sparse since rarely complete.
Apples to Apples: A Systematic Evaluation of Topic Models
In this paper, we present a selection of 9 topic modelling techniques from the state of the art reflecting a diversity of approaches to the task, an overview of the different metrics used to compare their performance, and the challenges of conducting such a comparison.
A Multilingual Benchmark to Capture Olfactory Situations over Time
We present a benchmark in six European languages containing manually annotated information about olfactory situations and events following a FrameNet-like approach.
TOMODAPI: A Topic Modeling API to Train, Use and Compare Topic Models
From LDA to neural models, different topic modeling approaches have been proposed in the literature.
Neural Re-rankers for Evidence Retrieval in the FEVEROUS Task
Computational fact-checking has gained a lot of traction in the machine learning and natural language processing communities.
One Configuration to Rule Them All? Towards Hyperparameter Transfer in Topic Models using Multi-Objective Bayesian Optimization
Topic models are statistical methods that extract underlying topics from document collections.
Many-to-one Recurrent Neural Network for Session-based Recommendation
We propose to use a many-to-one recurrent neural network that learns the probability that a user will click on an accommodation based on the sequence of actions he has performed during his browsing session.
Two Stages Approach for Tweet Engagement Prediction
This paper describes the approach proposed by the D2KLab team for the 2020 RecSys Challenge on the task of predicting user engagement facing tweets.
The MeMAD Submission to the WMT18 Multimodal Translation Task
Our experiments show that the effect of the visual features in our system is small.
