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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

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

hmBERT: Historical Multilingual Language Models for Named Entity Recognition

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hmBERT: Historical Multilingual Language Models for Named En...

2022 Conference and Labs of the Evaluation Forum, CLEF 2022

作者： Schweter, Stefan März, Luisa Schmid, Katharina Çano, Erion Bayerische Staatsbibliothek München Digital Library/ Munich Digitization Center Munich Germany Digital Philology Research Group Data Mining and Machine Learning University of Vienna Austria Natural Language Processing Expert Center Data:Lab Volkswagen AG Munich Germany

Compared to standard Named Entity Recognition (NER), identifying persons, locations, and organizations in historical texts constitutes a big challenge. To obtain machine-readable corpora, the historical text is usually scanned and Optical Character Recognition (OCR) needs to be performed. As a result, the historical corpora contain errors. Also, entities like location or organization can change over time, which poses another challenge. Overall, historical texts come with several peculiarities that differ greatly from modern texts and large labeled corpora for training a neural tagger are hardly available for this domain. In this work, we tackle NER for historical German, English, French, Swedish, and Finnish by training large historical language models. We circumvent the need for large amounts of labeled data by using unlabeled data for pretraining a language model. We propose hmBert, a historical multilingual BERT-based language model, and release the model in several versions of different sizes. Furthermore, we evaluate the capability of hmBert by solving downstream NER as part of this year’s HIPE-2022 shared task and provide detailed analysis and insights. For the Multilingual Classical Commentary coarse-grained NER challenge, our tagger HISTeria outperforms the other teams’ models for two out of three languages. © 2022 Copyright for this paper by its authors.

关键词： Optical character recognition

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

Is the Computation of Abstract Sameness Relations Human-Like in Neural Language Models?

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arXiv 2022年

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

In recent years, deep neural language models have made strong progress in various NLP tasks. This work explores one facet of the question whether state-of-the-art NLP models exhibit elementary mechanisms known from human cognition. The exploration is focused on a relatively primitive mechanism for which there is a lot of evidence from various psycholinguistic experiments with infants. The computation of "abstract sameness relations" is assumed to play an important role in human language acquisition and processing, especially in learning more complex grammar rules. In order to investigate this mechanism in BERT and other pre-trained language models (PLMs), the experiment designs from studies with infants were taken as the starting point. On this basis, we designed experimental settings in which each element from the original studies was mapped to a component of language models. Even though the task in our experiments was relatively simple, the results suggest that the cognitive faculty of computing abstract sameness relations is stronger in infants than in all investigated PLMs. Our implementation is available on https://***/lmthoma/computation_ASRs. Copyright © 2022, The Authors. All rights reserved.

关键词： Natural language processing systems

CSRCZ: A dataset About Corporate Social Responsibility in Czech Republic

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arXiv 2023年

作者： Vogli, Xhesilda Çano, Erion Department of Management Faculty of Economics and Management Czech University of Life Sciences Czech Republic Digital Philology Data Mining and Machine Learning University of Vienna Austria

As stakeholders' pressure on corporates for disclosing their corporate social responsibility operations grows, it is crucial to understand how efficient corporate disclosure systems are in bridging the gap between corporate social responsibility reports and their actual practice. Meanwhile, research on corporate social responsibility is still not aligned with the recent data-driven strategies, and little public data are available. This paper aims to describe CSRCZ, a newly created dataset based on disclosure reports from the websites of 1 000 companies that operate in Czech Republic. Each company was analyzed based on three main parameters: company size, company industry, and company initiatives. We describe the content of the dataset as well as its potential use for future research. We believe that CSRCZ has implications for further research, since it is the first publicly available dataset of its kind. Copyright © 2023, The Authors. All rights reserved.

关键词： Economic and social effects

Focused contrastive training for test-based constituency analysis

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arXiv 2021年

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

We propose a scheme for self-training of grammaticality models for constituency analysis based on linguistic tests. A pre-trained language model is fine-tuned by contrastive estimation of grammatical sentences from a corpus, and ungrammatical sentences that were perturbed by a syntactic test, a transformation that is motivated by constituency theory. We show that consistent gains can be achieved if only certain positive instances are chosen for training, depending on whether they could be the result of a test transformation. This way, the positives, and negatives exhibit similar characteristics, which makes the objective more challenging for the language model, and also allows for additional markup that indicates the position of the test application within the sentence. Copyright © 2021, The Authors. All rights reserved.

关键词： Computational linguistics

HmBERT: Historical Multilingual Language Models for Named Entity Recognition

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arXiv 2022年

Compared to standard Named Entity Recognition (NER), identifying persons, locations, and organizations in historical texts constitutes a big challenge. To obtain machine-readable corpora, the historical text is usually scanned and Optical Character Recognition (OCR) needs to be performed. As a result, the historical corpora contain errors. Also, entities like location or organization can change over time, which poses another challenge. Overall, historical texts come with several peculiarities that differ greatly from modern texts and large labeled corpora for training a neural tagger are hardly available for this domain. In this work, we tackle NER for historical German, English, French, Swedish, and Finnish by training large historical language models. We circumvent the need for large amounts of labeled data by using unlabeled data for pretraining a language model. We propose hmBert, a historical multilingual BERT-based language model, and release the model in several versions of different sizes. Furthermore, we evaluate the capability of hmBert by solving downstream NER as part of this year's HIPE-2022 shared task and provide detailed analysis and insights. For the Multilingual Classical Commentary coarse-grained NER challenge, our tagger HISTeria outperforms the other teams' models for two out of three languages. Copyright © 2022, The Authors. All rights reserved.

关键词： Optical character recognition

KnowMAN: Weakly supervised multinomial adversarial networks

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arXiv 2021年

作者： März, Luisa Asgari, Ehsaneddin Braune, Fabienne Zimmermann, Franziska Roth, Benjamin Digital Philology Research Group Data Mining and Machine Learning University of Vienna Austria Nlp Expert Center Data:Lab Volkswagen Ag Munich Germany

The absence of labeled data for training neural models is often addressed by leveraging knowledge about the specific task, resulting in heuristic but noisy labels. The knowledge is captured in labeling functions, which detect certain regularities or patterns in the training samples and annotate corresponding labels for training. This process of weakly supervised training may result in an over-reliance on the signals captured by the labeling functions and hinder models to exploit other signals or to generalize well. We propose KnowMAN, an adversarial scheme that enables to control influence of signals associated with specific labeling functions. KnowMAN forces the network to learn representations that are invariant to those signals and to pick up other signals that are more generally associated with an output label. KnowMAN strongly improves results compared to direct weakly supervised learning with a pre-trained transformer language model and a feature-based baseline. © 2021, CC BY.

关键词： Computational linguistics