Search Results for author:
Found 41 papers, 19 papers with code
RaKUn: Rank-based Keyword extraction via Unsupervised learning and Meta vertex aggregation
Keyword extraction is used for summarizing the content of a document and supports efficient document retrieval, and is as such an indispensable part of modern text-based systems.
tax2vec: Constructing Interpretable Features from Taxonomies for Short Text Classification
The use of background knowledge is largely unexploited in text classification tasks.
SNoRe: Scalable Unsupervised Learning of Symbolic Node Representations
Learning from complex real-life networks is a lively research area, with recent advances in learning information-rich, low-dimensional network node representations.
Ranked #17 on
Node Classification
on Coauthor CS
OutRank: Speeding up AutoML-based Model Search for Large Sparse Data sets with Cardinality-aware Feature Ranking
The proposed approach's feasibility is demonstrated by speeding up the state-of-the-art AutoML system on a synthetic data set with no performance loss.
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.
Ensemble- and Distance-Based Feature Ranking for Unsupervised Learning
In this work, we propose two novel (groups of) methods for unsupervised feature ranking and selection.
Unsupervised Feature Ranking via Attribute Networks
The need for learning from unlabeled data is increasing in contemporary machine learning.
Embedding-based Silhouette Community Detection
Mining complex data in the form of networks is of increasing interest in many scientific disciplines.
AttViz: Online exploration of self-attention for transparent neural language modeling
Neural language models are becoming the prevailing methodology for the tasks of query answering, text classification, disambiguation, completion and translation.
ReliefE: Feature Ranking in High-dimensional Spaces via Manifold Embeddings
The utility of ReliefE for high-dimensional data sets is ensured by its implementation that utilizes sparse matrix algebraic operations.
Multi-Label Classification
Vocal Bursts Intensity Prediction
Exploring Neural Language Models via Analysis of Local and Global Self-Attention Spaces
Large pretrained language models using the transformer neural network architecture are becoming a dominant methodology for many natural language processing tasks, such as question answering, text classification, word sense disambiguation, text completion and machine translation.
Link Analysis meets Ontologies: Are Embeddings the Answer?
The increasing amounts of semantic resources offer valuable storage of human knowledge; however, the probability of wrong entries increases with the increased size.
TNT-KID: Transformer-based Neural Tagger for Keyword Identification
With growing amounts of available textual data, development of algorithms capable of automatic analysis, categorization and summarization of these data has become a necessity.
Fuzzy Jaccard Index: A robust comparison of ordered lists
In real-life, and in particular high-dimensional domains, where only a small percentage of the whole feature space might be relevant, a robust and confident feature ranking leads to interpretable findings as well as efficient computation and good predictive performance.
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.
Propositionalization and Embeddings: Two Sides of the Same Coin
This paper outlines some of the modern data processing techniques used in relational learning that enable data fusion from different input data types and formats into a single table data representation, focusing on the propositionalization and embedding data transformation approaches.
Transfer Learning for Node Regression Applied to Spreading Prediction
This work is one of the first to explore transferability of the learned representations for the task of node regression; we show there exist pairs of networks with similar structure between which the trained models can be transferred (zero-shot), and demonstrate their competitive performance.
Deep Node Ranking for Neuro-symbolic Structural Node Embedding and Classification
Network node embedding is an active research subfield of complex network analysis.
Language comparison via network topology
In our experiments, we employ eight different network topology metrics, and empirically showcase on a parallel corpus, how the methods can be used for modeling the relations between nine selected languages.
Prediction Uncertainty Estimation for Hate Speech Classification
As a result of social network popularity, in recent years, hate speech phenomenon has significantly increased.
Feature Importance Estimation with Self-Attention Networks
Black-box neural network models are widely used in industry and science, yet are hard to understand and interpret.
COVID-19 therapy target discovery with context-aware literature mining
The abundance of literature related to the widespread COVID-19 pandemic is beyond manual inspection of a single expert.
Predicting Generalization in Deep Learning via Metric Learning -- PGDL Shared task
The competition "Predicting Generalization in Deep Learning (PGDL)" aims to provide a platform for rigorous study of generalization of deep learning models and offer insight into the progress of understanding and explaining these models.
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.
Semantic Reasoning from Model-Agnostic Explanations
The derived semantic explanations are potentially more informative, as they describe the key attributes in the context of more general background knowledge, e. g., at the biological process level.
Compressibility of Distributed Document Representations
Contemporary natural language processing (NLP) revolves around learning from latent document representations, generated either implicitly by neural language models or explicitly by methods such as doc2vec or similar.
Prioritization of COVID-19-related literature via unsupervised keyphrase extraction and document representation learning
The COVID-19 pandemic triggered a wave of novel scientific literature that is impossible to inspect and study in a reasonable time frame manually.
BERT meets Shapley: Extending SHAP Explanations to Transformer-based Classifiers
Transformer-based neural networks offer very good classification performance across a wide range of domains, but do not provide explanations of their predictions.
Zero-shot Cross-lingual Content Filtering: Offensive Language and Hate Speech Detection
We present a system for zero-shot cross-lingual offensive language and hate speech classification.
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.
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.
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.
Retrieval-efficiency trade-off of Unsupervised Keyword Extraction
Efficiently identifying keyphrases that represent a given document is a challenging task.
Dynamic Surrogate Switching: Sample-Efficient Search for Factorization Machine Configurations in Online Recommendations
The main idea underlying this paradigm considers an incrementally updated model of the relation between the hyperparameter space and the output (target) space; the data for this model are obtained by evaluating the main learning engine, which is, for example, a factorization machine-based model.
Sentiment Classification by Incorporating Background Knowledge from Financial Ontologies
Ontologies are increasingly used for machine reasoning over the last few years.
DDeMON: Ontology-based function prediction by Deep Learning from Dynamic Multiplex Networks
Biological systems can be studied at multiple levels of information, including gene, protein, RNA and different interaction networks levels.
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.
Drifter: Efficient Online Feature Monitoring for Improved Data Integrity in Large-Scale Recommendation Systems
Real-world production systems often grapple with maintaining data quality in large-scale, dynamic streams.
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.
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.
