Csaba Oravecz


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eTranslation’s Submissions to the WMT 2019 News Translation Task
Csaba Oravecz | Katina Bontcheva | Adrien Lardilleux | László Tihanyi | Andreas Eisele
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)

This paper describes the submissions of the eTranslation team to the WMT 2019 news translation shared task. The systems have been developed with the aim of identifying and following rather than establishing best practices, under the constraints imposed by a low resource training and decoding environment normally used for our production systems. Thus most of the findings and results are transferable to systems used in the eTranslation service. Evaluations suggest that this approach is able to produce decent models with good performance and speed without the overhead of using prohibitively deep and complex architectures.


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A New Integrated Open-source Morphological Analyzer for Hungarian
Attila Novák | Borbála Siklósi | Csaba Oravecz
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

The goal of a Hungarian research project has been to create an integrated Hungarian natural language processing framework. This infrastructure includes tools for analyzing Hungarian texts, integrated into a standardized environment. The morphological analyzer is one of the core components of the framework. The goal of this paper is to describe a fast and customizable morphological analyzer and its development framework, which synthesizes and further enriches the morphological knowledge implemented in previous tools existing for Hungarian. In addition, we present the method we applied to add semantic knowledge to the lexical database of the morphology. The method utilizes neural word embedding models and morphological and shallow syntactic knowledge.


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The Hungarian Gigaword Corpus
Csaba Oravecz | Tamás Váradi | Bálint Sass
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

The paper reports on the development of the Hungarian Gigaword Corpus (HGC), an extended new edition of the Hungarian National Corpus, with upgraded and redesigned linguistic annotation and an increased size of 1.5 billion tokens. Issues concerning the standard steps of corpus collection and preparation are discussed with special emphasis on linguistic analysis and annotation due to Hungarian having some challenging characteristics with respect to computational processing. As the HGC is designed to serve as a resource for a wide range of linguistic research as well as for the interested public, a number of issues had to be resolved which were raised by trying to find a balance between the above two application areas. The following main objectives have been defined for the development of the HGC, focusing on the pivotal concept of increase in: - size: extending the corpus to minimum 1 billion words, - quality: using new technology for development and analysis, - coverage and representativity: taking new samples of language use and including further variants (transcribed spoken language data and user generated content (social media) from the internet in particular).


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Poster paper: HunPos – an open source trigram tagger
Péter Halácsy | András Kornai | Csaba Oravecz
Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions


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Using a morphological analyzer in high precision POS tagging of Hungarian
Péter Halácsy | András Kornai | Csaba Oravecz | Viktor Trón | Dániel Varga
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

The paper presents an evaluation of maxent POS disambiguation systems that incorporate an open source morphological analyzer to constrain the probabilistic models. The experiments show that the best proposed architecture, which is the first application of the maximum entropy framework in a Hungarian NLP task, outperforms comparable state of the art tagging methods and is able to handle out of vocabulary items robustly, allowing for efficient analysis of large (web-based) corpora.

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Web-based frequency dictionaries for medium density languages
András Kornai | Péter Halácsy | Viktor Nagy | Csaba Oravecz | Viktor Trón | Dániel Varga
Proceedings of the 2nd International Workshop on Web as Corpus


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Combining Symbolic and Statistical Methods in Morphological Analysis and Unknown Word Guessing
Attila Novák | Viktor Nagy | Csaba Oravecz
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

Highly inflectional/agglutinative languages like Hungarian typically feature possible word forms in such a magnitude that automatic methods that provide morphosyntactic annotation on the basis of some training corpus often face the problem of data sparseness. A possible solution to this problem is to apply a comprehensive morphological analyser, which is able to analyse almost all wordforms alleviating the problem of unseen tokens. However, although in a smaller number, there will still remain forms which are unknown even to the morphological analyzer and should be handled by some guesser mechanism. The paper will describe a hybrid method which combines symbolic and statistical information to provide lemmatization and suffix analyses for unknown word forms. Evaluation is carried out with respect to the induction of possible analyses and their respective lexical probabilities for unknown word forms in a part-of-speech tagging system.


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Efficient Stochastic Part-of-Speech Tagging for Hungarian
Csaba Oravecz | Péter Dienes
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)


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Principled Hidden Tagset Design for Tiered Tagging of Hungarian
Dan Tufiş | Péter Dienes | Csaba Oravecz | Tamás Váradi
Proceedings of the Second International Conference on Language Resources and Evaluation (LREC’00)

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Bottom-Up Tagset Design from Maximally Reduced Tagset
Péter Dienes | Csaba Oravecz
Proceedings of the COLING-2000 Workshop on Linguistically Interpreted Corpora