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Found 10 papers, 5 papers with code
Don’t Burst Blindly: For a Better Use of Natural Language Processing to Fight Opinion Bubbles in News Recommendations
Online news consumption plays an important role in shaping the political opinions of citizens.
Combining Objective and Subjective Perspectives for Political News Understanding
Such tools should provide objective and subjective aspects at different granularity levels to make the analyses useful in practice.
Do Similar Entities have Similar Embeddings?
A common tacit assumption is the KGE entity similarity assumption, which states that these KGEMs retain the graph's structure within their embedding space, \textit{i. e.}, position similar entities within the graph close to one another.
PyGraft: Configurable Generation of Synthetic Schemas and Knowledge Graphs at Your Fingertips
In some data-sensitive fields such as education or medicine, access to public datasets is even more limited.
Schema First! Learn Versatile Knowledge Graph Embeddings by Capturing Semantics with MASCHInE
These models learn a vector representation of knowledge graph entities and relations, a. k. a.
Treat Different Negatives Differently: Enriching Loss Functions with Domain and Range Constraints for Link Prediction
In an extensive and controlled experimental setting, we show that the proposed loss functions systematically provide satisfying results which demonstrates both the generality and superiority of our proposed approach.
Characterizing Financial Market Coverage using Artificial Intelligence
This paper scrutinizes a database of over 4900 YouTube videos to characterize financial market coverage.
Sem@$K$: Is my knowledge graph embedding model semantic-aware?
That is why, in this paper, we extend our previously introduced metric Sem@K that measures the capability of models to predict valid entities w. r. t.
Multi-source Data Mining for e-Learning
This challenge is the main focus of our work where we propose to mine multi-source data in order to extract interesting frequent patterns.
Lead2Gold: Towards exploiting the full potential of noisy transcriptions for speech recognition
The transcriptions used to train an Automatic Speech Recognition (ASR) system may contain errors.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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