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Found 19 papers, 5 papers with code
E8-IJS@LT-EDI-ACL2022 - BERT, AutoML and Knowledge-graph backed Detection of Depression
Depression is a mental illness that negatively affects a person’s well-being and can, if left untreated, lead to serious consequences such as suicide.
Fusion of linguistic, neural and sentence-transformer features for improved term alignment
Crosslingual terminology alignment task has many practical applications.
EMBEDDIA at SemEval-2022 Task 8: Investigating Sentence, Image, and Knowledge Graph Representations for Multilingual News Article Similarity
In this paper, we present the participation of the EMBEDDIA team in the SemEval-2022 Task 8 (Multilingual News Article Similarity).
EMBEDDIA Tools, Datasets and Challenges: Resources and Hackathon Contributions
This paper presents tools and data sources collected and released by the EMBEDDIA project, supported by the European Union’s Horizon 2020 research and innovation program.
Interesting cross-border news discovery using cross-lingual article linking and document similarity
Team Name: team-8 Embeddia Tool: Cross-Lingual Document Retrieval Zosa et al. Dataset: Estonian and Latvian news datasets abstract: Contemporary news media face increasing amounts of available data that can be of use when prioritizing, selecting and discovering new news.
SEKE: Specialised Experts for Keyword Extraction
Relying on the insight that real-world keyword detection often requires handling of diverse content, we propose a novel supervised keyword extraction approach based on the mixture of experts (MoE) technique.
Evaluating and explaining training strategies for zero-shot cross-lingual news sentiment analysis
We investigate zero-shot cross-lingual news sentiment detection, aiming to develop robust sentiment classifiers that can be deployed across multiple languages without target-language training data.
ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain
This paper describes the 2nd edition of the ICML Topological Deep Learning Challenge that was hosted within the ICML 2024 ELLIS Workshop on Geometry-grounded Representation Learning and Generative Modeling (GRaM).
AutoML-guided Fusion of Entity and LLM-based Representations for Document Classification
This work demonstrates that injecting embedded information from knowledge bases can augment the performance of contemporary Large Language Model (LLM)-based representations for the task of text classification.
A Computational Analysis of the Dehumanisation of Migrants from Syria and Ukraine in Slovene News Media
Dehumanisation involves the perception and or treatment of a social group's members as less than human.
Multi-Task Learning for Features Extraction in Financial Annual Reports
For assessing various performance indicators of companies, the focus is shifting from strictly financial (quantitative) publicly disclosed information to qualitative (textual) information.
AHAM: Adapt, Help, Ask, Model -- Harvesting LLMs for literature mining
Our system aims to reduce both the ratio of outlier topics to the total number of topics and the similarity between topic definitions.
Latent Graphs for Semi-Supervised Learning on Biomedical Tabular Data
In the domain of semi-supervised learning, the current approaches insufficiently exploit the potential of considering inter-instance relationships among (un)labeled data.
Measuring Catastrophic Forgetting in Cross-Lingual Transfer Paradigms: Exploring Tuning Strategies
As cross-lingual transfer strategies, we compare the intermediate-training (\textit{IT}) that uses each language sequentially and cross-lingual validation (\textit{CLV}) that uses a target language already in the validation phase of fine-tuning.
Retrieval-efficiency trade-off of Unsupervised Keyword Extraction
Efficiently identifying keyphrases that represent a given document is a challenging task.
Out of Thin Air: Is Zero-Shot Cross-Lingual Keyword Detection Better Than Unsupervised?
We find that the pretrained models fine-tuned on a multilingual corpus covering languages that do not appear in the test set (i. e. in a zero-shot setting), consistently outscore unsupervised models in all six languages.
Knowledge Graph informed Fake News Classification via Heterogeneous Representation Ensembles
Increasing amounts of freely available data both in textual and relational form offers exploration of richer document representations, potentially improving the model performance and robustness.
Extending Neural Keyword Extraction with TF-IDF tagset matching
Keyword extraction is the task of identifying words (or multi-word expressions) that best describe a given document and serve in news portals to link articles of similar topics.
Identification of COVID-19 related Fake News via Neural Stacking
Identification of Fake News plays a prominent role in the ongoing pandemic, impacting multiple aspects of day-to-day life.
