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AlbNER: A Corpus for Named Entity Recognition in Albanian

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arXiv 2023年

作者： Çano, Erion Digital Philology Data Mining Machine Learning University of Vienna Austria

Scarcity of resources such as annotated text corpora for under-resourced languages like Albanian is a serious impediment in computational linguistics and natural language processing research. This paper presents AlbNER, a corpus of 900 sentences with labeled named entities, collected from Albanian Wikipedia articles. Preliminary results with BERT and RoBERTa variants fine-tuned and tested with AlbNER data indicate that model size has slight impact on NER performance, whereas language transfer has a significant one. AlbNER corpus and these obtained results should serve as baselines for future experiments. Copyright © 2023, The Authors. All rights reserved.

关键词： Natural language processing systems

AlbMoRe: A Corpus of Movie Reviews for Sentiment Analysis in Albanian

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arXiv 2023年

作者： Çano, Erion Digital Philology Data Mining and Machine Learning University of Vienna Austria

Lack of available resources such as text corpora for low-resource languages seriously hinders research on natural language processing and computational linguistics. This paper presents AlbMoRe, a corpus of 800 sentiment annotated movie reviews in Albanian. Each text is labeled as positive or negative and can be used for sentiment analysis research. Preliminary results based on traditional machine learning classifiers trained with the AlbMoRe samples are also reported. They can serve as comparison baselines for future research experiments. Copyright © 2023, The Authors. All rights reserved.

关键词： Sentiment analysis

SHADE: Deep Density-based Clustering

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arXiv 2024年

作者： Beer, Anna Weber, Pascal Miklautz, Lukas Leiber, Collin Durani, Walid Böhm, Christian Plant, Claudia Data Mining and Machine Learning University of Vienna Vienna Austria UniVie Doctoral School Computer Science Vienna Austria Database Systems and Data Mining LMU Munich Munich Germany Munich Center for Machine Learning Munich Germany UniVie Vienna Austria

Detecting arbitrarily shaped clusters in high-dimensional noisy data is challenging for current clustering methods. We introduce SHADE (Structure-preserving High-dimensional Analysis with Density-based Exploration), the first deep clustering algorithm that incorporates density-connectivity into its loss function. Similar to existing deep clustering algorithms, SHADE supports high-dimensional and large data sets with the expressive power of a deep autoencoder. In contrast to most existing deep clustering methods that rely on a centroid-based clustering objective, SHADE incorporates a novel loss function that captures density-connectivity. SHADE thereby learns a representation that enhances the separation of density-connected clusters. SHADE detects a stable clustering and noise points fully automatically without any user input. It outperforms existing methods in clustering quality, especially on data that contain non-Gaussian clusters, such as video data. Moreover, the embedded space of SHADE is suitable for visualization and interpretation of the clustering results as the individual shapes of the clusters are preserved. © 2024, CC BY.

关键词： Clustering algorithms

SHADE: Deep Density-based Clustering 24

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SHADE: Deep Density-based Clustering

24th IEEE International Conference on data mining, ICDM 2024

作者： Beer, Anna Weber, Pascal Miklautz, Lukas Leiber, Collin Durani, Walid Bohm, Christian Plant, Claudia University of Vienna Faculty of Computer Science Vienna Austria UniVie Doctoral School Computer Science Vienna Austria LMU Munich Database Systems and Data Mining Munich Germany Munich Center for Machine Learning Munich Germany ds: UniVie University of Vienna Vienna Austria

ISBN: (纸本)9798331506681

Detecting arbitrarily shaped clusters in high-dimensional noisy data is challenging for current clustering methods. We introduce SHADE, the first deep clustering algorithm that incorporates density-connectivity into its loss function. Similar to existing deep clustering algorithms, SHADE supports high-dimensional and large data sets with the expressive power of a deep autoencoder. In contrast to most existing deep clustering methods that rely on a centroid-based clustering objective, SHADE incorporates a novel loss function that captures density-connectivity. It thereby learns a representation that enhances the separation of density-connected clusters. SHADE detects a stable clustering and noise points fully automatically without any user input. It outperforms existing methods in clustering quality, especially on data that contain non-Gaussian clusters, such as video data. Moreover, the embedded space of SHADE is suitable for visualization and interpretation of the clustering results as the individual shapes of the clusters are preserved. © 2024 IEEE.

关键词： Clustering DBSCAN Deep Clustering Density-based Clustering

AlbNews: A Corpus of Headlines for Topic Modeling in Albanian

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arXiv 2024年

作者： Çano, Erion Lamaj, Dario Digital Philology Data Mining and Machine Learning University of Vienna Austria Cognitive Science Department of Applied Informatics Comenius University Bratislava Slovakia

The scarcity of available text corpora for low-resource languages like Albanian is a serious hurdle for research in natural language processing tasks. This paper introduces AlbNews, a collection of 600 topically labeled news headlines and 2600 unlabeled ones in Albanian. The data can be freely used for conducting topic modeling research. We report the initial classification scores of some traditional machine learning classifiers trained with the AlbNews samples. These results show that basic models outrun the ensemble learning ones and can serve as a baseline for future experiments. Copyright © 2024, The Authors. All rights reserved.

关键词： learning algorithms

Batch Layer Normalization A new normalization layer for CNNs and RNNs 22

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Batch Layer Normalization A new normalization layer for CNNs...

Proceedings of the 6th International Conference on Advances in Artificial Intelligence

作者： Amir Ziaee Erion ÇAno Design Computing Group TU Wien Austria Research Group Data Mining and Machine Learning University of Vienna Austria

ISBN: (纸本)9781450396943

This study introduces a new normalization layer termed Batch Layer Normalization (BLN) to reduce the problem of internal covariate shift in deep neural network layers. As a combined version of batch and layer normalization, BLN adaptively puts appropriate weight on mini-batch and feature normalization based on the inverse size of mini-batches to normalize the input to a layer during the learning process. It also performs the exact computation with a minor change at inference times, using either mini-batch statistics or population statistics. The decision process to either use statistics of mini-batch or population gives BLN the ability to play a comprehensive role in the hyper-parameter optimization process of models. The key advantage of BLN is the support of the theoretical analysis of being independent of the input data, and its statistical configuration heavily depends on the task performed, the amount of training data, and the size of batches. Test results indicate the application potential of BLN and its faster convergence than batch normalization and layer normalization in both Convolutional and Recurrent Neural Networks. The code of the experiments is publicly available online.1

关键词： Neural Networks Internal Covariate Shift Normalization

Motif Discovery Framework for Psychiatric EEG data Classification

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arXiv 2025年

作者： Kraljevska, Melanija Hlavackova-Schindler, Katerina Miklautz, Lukas Plant, Claudia Research Group Data Mining and Machine Learning Faculty of Computer Science University of Vienna Währingerstrasse 29 Vienna1090 Austria ds:UniVie University of Vienna Austria

In current medical practice, patients undergoing depression treatment must wait four to six weeks before a clinician can assess medication response due to the delayed noticeable effects of antidepressants. Identification of a treatment response at any earlier stage is of great importance, since it can reduce the emotional and economic burden connected with the treatment. We approach the prediction of a patient response to a treatment as a classification problem, by utilizing the dynamic properties of EEG recordings on the 7th day of the treatment. We present a novel framework that applies motif discovery to extract meaningful features from EEG data distinguishing between depression treatment responders and non-responders. We applied our framework also to classification tasks in other psychiatric EEG datasets, namely to patients with symptoms of schizophrenia, pediatric patients with intractable seizures, and Alzheimer disease and dementia. We achieved high classification precision in all data sets. The results demonstrate that the dynamic properties of the EEGs may support clinicians in decision making both in diagnosis and in the prediction depression treatment response as early as on the 7th day of the treatment. To our best knowledge, our work is the first one using motifs in the depression diagnostics in general. © 2025, CC BY.

关键词： Electroencephalography

Topic Segmentation of Research Article Collections

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arXiv 2022年

作者： Çano, Erion Roth, Benjamin Digital Philology Research Group Data Mining and Machine Learning University of Vienna Austria

Collections of research article data harvested from the web have become common recently since they are important resources for experimenting on tasks such as named entity recognition, text summarization, or keyword generation. In fact, certain types of experiments require collections that are both large and topically structured, with records assigned to separate research disciplines. Unfortunately, the current collections of publicly available research articles are either small or heterogeneous and unstructured. In this work, we perform topic segmentation of a paper data collection that we crawled and produce a multitopic dataset of roughly seven million paper data records. We construct a taxonomy of topics extracted from the data records and then annotate each document with its corresponding topic from that taxonomy. As a result, it is possible to use this newly proposed dataset in two modalities: as a heterogeneous collection of documents from various disciplines or as a set of homogeneous collections, each from a single research topic. Copyright © 2022, The Authors. All rights reserved.

关键词： Taxonomies

Pattern Discovery in an EEG database of Depression Patients: Preliminary Results 14

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Pattern Discovery in an EEG Database of Depression Patients:...

14th International Conference on Measurement, MEASUREMENT 2023

作者： Hlavackova-Schindler, Katerina Pacher, Christina Plant, Claudia Lazarenko, Mykola Palus, Milan Hlinka, Jaroslav Kathpalia, Aditi Brunovsky, Martin University of Vienna Data Mining and Machine Learning Research Group Faculty of Computer Science Vienna Austria Czech Academy of Sciences Institute of Computer Science Department of Complex Systems Prague Czech Republic National Institute of Mental Health Clinical Research Programme Klecany Czech Republic

ISBN: (纸本)9788097262976

The ability to predict response to medication treatment of depressed patients, either early in the course of therapy or before treatment even begins can avoid trials of ineffective therapy and save patients from prolonged intervals of suffering. Symptom alleviation requires 4-6 weeks after starting current antidepressive medication. Based on the data basis of the patients and their EEG before and on the 7th day of treatment we apply data mining, causal discovery and machine learning approaches to discover interactive patterns between patient's brain regions to separate the treatment responders from non-responders. In this paper we report the preliminary results of our international project "learning Synchronization Patterns in Multivariate Neural Signals for Prediction of Response to Antidepressants"ongoing at the University of Vienna, the Czech Academy of Sciences and the National Institute of Mental Health in the Czech Republic. © 2023 Institute of Measurement Science, Slovak Academy o.

关键词： data mining