Daniel Zeman

Also published as: Dan Zeman


2020

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Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing
Stephan Oepen | Omri Abend | Lasha Abzianidze | Johan Bos | Jan Hajič | Daniel Hershcovich | Bin Li | Tim O'Gorman | Nianwen Xue | Daniel Zeman
Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing

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MRP 2020: The Second Shared Task on Cross-Framework and Cross-Lingual Meaning Representation Parsing
Stephan Oepen | Omri Abend | Lasha Abzianidze | Johan Bos | Jan Hajic | Daniel Hershcovich | Bin Li | Tim O’Gorman | Nianwen Xue | Daniel Zeman
Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing

The 2020 Shared Task at the Conference for Computational Language Learning (CoNLL) was devoted to Meaning Representation Parsing (MRP) across frameworks and languages. Extending a similar setup from the previous year, five distinct approaches to the representation of sentence meaning in the form of directed graphs were represented in the English training and evaluation data for the task, packaged in a uniform graph abstraction and serialization; for four of these representation frameworks, additional training and evaluation data was provided for one additional language per framework. The task received submissions from eight teams, of which two do not participate in the official ranking because they arrived after the closing deadline or made use of additional training data. All technical information regarding the task, including system submissions, official results, and links to supporting resources and software are available from the task web site at: http://mrp.nlpl.eu

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FGD at MRP 2020: Prague Tectogrammatical Graphs
Daniel Zeman | Jan Hajic
Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing

Prague Tectogrammatical Graphs (PTG) is a meaning representation framework that originates in the tectogrammatical layer of the Prague Dependency Treebank (PDT) and is theoretically founded in Functional Generative Description of language (FGD). PTG in its present form has been prepared for the CoNLL 2020 shared task on Cross-Framework Meaning Representation Parsing (MRP). It is generated automatically from the Prague treebanks and stored in the JSON-based MRP graph interchange format. The conversion is partially lossy; in this paper we describe what part of annotation was included and how it is represented in PTG.

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Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection
Joakim Nivre | Marie-Catherine de Marneffe | Filip Ginter | Jan Hajič | Christopher D. Manning | Sampo Pyysalo | Sebastian Schuster | Francis Tyers | Daniel Zeman
Proceedings of the 12th Language Resources and Evaluation Conference

Universal Dependencies is an open community effort to create cross-linguistically consistent treebank annotation for many languages within a dependency-based lexicalist framework. The annotation consists in a linguistically motivated word segmentation; a morphological layer comprising lemmas, universal part-of-speech tags, and standardized morphological features; and a syntactic layer focusing on syntactic relations between predicates, arguments and modifiers. In this paper, we describe version 2 of the universal guidelines (UD v2), discuss the major changes from UD v1 to UD v2, and give an overview of the currently available treebanks for 90 languages.

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Yorùbá Dependency Treebank (YTB)
Olájídé Ishola | Daniel Zeman
Proceedings of the 12th Language Resources and Evaluation Conference

Low-resource languages present enormous NLP opportunities as well as varying degrees of difficulties. The newly released treebank of hand-annotated parts of the Yoruba Bible provides an avenue for dependency analysis of the Yoruba language; the application of a new grammar formalism to the language. In this paper, we discuss our choice of Universal Dependencies, important dependency annotation decisions considered in the creation of the first annotation guidelines for Yoruba and results of our parsing experiments. We also lay the foundation for future incorporation of other domains with the initial test on Yoruba Wikipedia articles and highlighted future directions for the rapid expansion of the treebank.

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Sentence Meaning Representations Across Languages: What Can We Learn from Existing Frameworks?
Zdeněk Žabokrtský | Daniel Zeman | Magda Ševčíková
Computational Linguistics, Volume 46, Issue 3 - September 2020

This article gives an overview of how sentence meaning is represented in eleven deep-syntactic frameworks, ranging from those based on linguistic theories elaborated for decades to rather lightweight NLP-motivated approaches. We outline the most important characteristics of each framework and then discuss how particular language phenomena are treated across those frameworks, while trying to shed light on commonalities as well as differences.

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Universal Dependency Treebanks for Low-Resource Indian Languages: The Case of Bhojpuri
Atul Kr. Ojha | Daniel Zeman
Proceedings of the WILDRE5– 5th Workshop on Indian Language Data: Resources and Evaluation

This paper presents the first dependency treebank for Bhojpuri, a resource-poor language that belongs to the Indo-Aryan language family. The objective behind the Bhojpuri Treebank (BHTB) project is to create a substantial, syntactically annotated treebank which not only acts as a valuable resource in building language technological tools, also helps in cross-lingual learning and typological research. Currently, the treebank consists of 4,881 annotated tokens in accordance with the annotation scheme of Universal Dependencies (UD). A Bhojpuri tagger and parser were created using machine learning approach. The accuracy of the model is 57.49% UAS, 45.50% LAS, 79.69% UPOS accuracy and 77.64% XPOS accuracy. The paper describes the details of the project including a discussion on linguistic analysis and annotation process of the Bhojpuri UD treebank.

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Predicting Typological Features in WALS using Language Embeddings and Conditional Probabilities: ÚFAL Submission to the SIGTYP 2020 Shared Task
Martin Vastl | Daniel Zeman | Rudolf Rosa
Proceedings of the Second Workshop on Computational Research in Linguistic Typology

We present our submission to the SIGTYP 2020 Shared Task on the prediction of typological features. We submit a constrained system, predicting typological features only based on the WALS database. We investigate two approaches. The simpler of the two is a system based on estimating correlation of feature values within languages by computing conditional probabilities and mutual information. The second approach is to train a neural predictor operating on precomputed language embeddings based on WALS features. Our submitted system combines the two approaches based on their self-estimated confidence scores. We reach the accuracy of 70.7% on the test data and rank first in the shared task.

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Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies
Gosse Bouma | Yuji Matsumoto | Stephan Oepen | Kenji Sagae | Djamé Seddah | Weiwei Sun | Anders Søgaard | Reut Tsarfaty | Dan Zeman
Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies

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Overview of the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies
Gosse Bouma | Djamé Seddah | Daniel Zeman
Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies

This overview introduces the task of parsing into enhanced universal dependencies, describes the datasets used for training and evaluation, and evaluation metrics. We outline various approaches and discuss the results of the shared task.

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Estimating POS Annotation Consistency of Different Treebanks in a Language
Akshay Aggarwal | Daniel Zeman
Proceedings of the 19th International Workshop on Treebanks and Linguistic Theories

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Universal Dependencies for Albanian
Marsida Toska | Joakim Nivre | Daniel Zeman
Proceedings of the Fourth Workshop on Universal Dependencies (UDW 2020)

In this paper, we introduce the first Universal Dependencies (UD) treebank for standard Albanian, consisting of 60 sentences collected from the Albanian Wikipedia, annotated with lemmas, universal part-of-speech tags, morphological features and syntactic dependencies. In addition to presenting the treebank itself, we discuss a selection of linguistic constructions in Albanian whose analysis in UD is not self-evident, including core arguments and the status of indirect objects, pronominal clitics, genitive constructions, prearticulated adjectives, and modal verbs.

2019

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ÚFAL-Oslo at MRP 2019: Garage Sale Semantic Parsing
Kira Droganova | Andrey Kutuzov | Nikita Mediankin | Daniel Zeman
Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning

This paper describes the ÚFAL--Oslo system submission to the shared task on Cross-Framework Meaning Representation Parsing (MRP, Oepen et al. 2019). The submission is based on several third-party parsers. Within the official shared task results, the submission ranked 11th out of 13 participating systems.

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CUNIMalta system at SIGMORPHON 2019 Shared Task on Morphological Analysis and Lemmatization in context: Operation-based word formation
Ronald Cardenas | Claudia Borg | Daniel Zeman
Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology

This paper presents the submission by the Charles University-University of Malta team to the SIGMORPHON 2019 Shared Task on Morphological Analysis and Lemmatization in context. We present a lemmatization model based on previous work on neural transducers (Makarov and Clematide, 2018b; Aharoni and Goldberg, 2016). The key difference is that our model transforms the whole word form in every step, instead of consuming it character by character. We propose a merging strategy inspired by Byte-Pair-Encoding that reduces the space of valid operations by merging frequent adjacent operations. The resulting operations not only encode the actions to be performed but the relative position in the word token and how characters need to be transformed. Our morphological tagger is a vanilla biLSTM tagger that operates over operation representations, encoding operations and words in a hierarchical manner. Even though relative performance according to metrics is below the baseline, experiments show that our models capture important associations between interpretable operation labels and fine-grained morpho-syntax labels.

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Towards Deep Universal Dependencies
Kira Droganova | Daniel Zeman
Proceedings of the Fifth International Conference on Dependency Linguistics (Depling, SyntaxFest 2019)

2018

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Parse Me if You Can: Artificial Treebanks for Parsing Experiments on Elliptical Constructions
Kira Droganova | Daniel Zeman | Jenna Kanerva | Filip Ginter
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
Daniel Zeman | Jan Hajič
Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies

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CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
Daniel Zeman | Jan Hajič | Martin Popel | Martin Potthast | Milan Straka | Filip Ginter | Joakim Nivre | Slav Petrov
Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies

Every year, the Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their learning systems on the same data sets. In 2018, one of two tasks was devoted to learning dependency parsers for a large number of languages, in a real-world setting without any gold-standard annotation on test input. All test sets followed a unified annotation scheme, namely that of Universal Dependencies. This shared task constitutes a 2nd edition—the first one took place in 2017 (Zeman et al., 2017); the main metric from 2017 has been kept, allowing for easy comparison, also in 2018, and two new main metrics have been used. New datasets added to the Universal Dependencies collection between mid-2017 and the spring of 2018 have contributed to increased difficulty of the task this year. In this overview paper, we define the task and the updated evaluation methodology, describe data preparation, report and analyze the main results, and provide a brief categorization of the different approaches of the participating systems.

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A Morphological Analyzer for Shipibo-Konibo
Ronald Cardenas | Daniel Zeman
Proceedings of the Fifteenth Workshop on Computational Research in Phonetics, Phonology, and Morphology

We present a fairly complete morphological analyzer for Shipibo-Konibo, a low-resourced native language spoken in the Amazonian region of Peru. We resort to the robustness of finite-state systems in order to model the complex morphosyntax of the language. Evaluation over raw corpora shows promising coverage of grammatical phenomena, limited only by the scarce lexicon. We make this tool freely available so as to aid the production of annotated corpora and impulse further research in native languages of Peru.

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Challenges in Converting the Index Thomisticus Treebank into Universal Dependencies
Flavio Massimiliano Cecchini | Marco Passarotti | Paola Marongiu | Daniel Zeman
Proceedings of the Second Workshop on Universal Dependencies (UDW 2018)

This paper describes the changes applied to the original process used to convert the Index Thomisticus Treebank, a corpus including texts in Medieval Latin by Thomas Aquinas, into the annotation style of Universal Dependencies. The changes are made both to harmonise the Universal Dependencies version of the Index Thomisticus Treebank with the two other available Latin treebanks and to fix errors and inconsistencies resulting from the original process. The paper details the treatment of different issues in PoS tagging, lemmatisation and assignment of dependency relations. Finally, it assesses the quality of the new conversion process by providing an evaluation against a gold standard.

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Mind the Gap: Data Enrichment in Dependency Parsing of Elliptical Constructions
Kira Droganova | Filip Ginter | Jenna Kanerva | Daniel Zeman
Proceedings of the Second Workshop on Universal Dependencies (UDW 2018)

In this paper, we focus on parsing rare and non-trivial constructions, in particular ellipsis. We report on several experiments in enrichment of training data for this specific construction, evaluated on five languages: Czech, English, Finnish, Russian and Slovak. These data enrichment methods draw upon self-training and tri-training, combined with a stratified sampling method mimicking the structural complexity of the original treebank. In addition, using these same methods, we also demonstrate small improvements over the CoNLL-17 parsing shared task winning system for four of the five languages, not only restricted to the elliptical constructions.

2017

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Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
Jan Hajič | Dan Zeman
Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies

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CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
Daniel Zeman | Martin Popel | Milan Straka | Jan Hajič | Joakim Nivre | Filip Ginter | Juhani Luotolahti | Sampo Pyysalo | Slav Petrov | Martin Potthast | Francis Tyers | Elena Badmaeva | Memduh Gokirmak | Anna Nedoluzhko | Silvie Cinková | Jan Hajič jr. | Jaroslava Hlaváčová | Václava Kettnerová | Zdeňka Urešová | Jenna Kanerva | Stina Ojala | Anna Missilä | Christopher D. Manning | Sebastian Schuster | Siva Reddy | Dima Taji | Nizar Habash | Herman Leung | Marie-Catherine de Marneffe | Manuela Sanguinetti | Maria Simi | Hiroshi Kanayama | Valeria de Paiva | Kira Droganova | Héctor Martínez Alonso | Çağrı Çöltekin | Umut Sulubacak | Hans Uszkoreit | Vivien Macketanz | Aljoscha Burchardt | Kim Harris | Katrin Marheinecke | Georg Rehm | Tolga Kayadelen | Mohammed Attia | Ali Elkahky | Zhuoran Yu | Emily Pitler | Saran Lertpradit | Michael Mandl | Jesse Kirchner | Hector Fernandez Alcalde | Jana Strnadová | Esha Banerjee | Ruli Manurung | Antonio Stella | Atsuko Shimada | Sookyoung Kwak | Gustavo Mendonça | Tatiana Lando | Rattima Nitisaroj | Josie Li
Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies

The Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their learning systems on the same data sets. In 2017, the task was devoted to learning dependency parsers for a large number of languages, in a real-world setting without any gold-standard annotation on input. All test sets followed a unified annotation scheme, namely that of Universal Dependencies. In this paper, we define the task and evaluation methodology, describe how the data sets were prepared, report and analyze the main results, and provide a brief categorization of the different approaches of the participating systems.

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Universal Dependencies
Joakim Nivre | Daniel Zeman | Filip Ginter | Francis Tyers
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts

Universal Dependencies (UD) is a project that seeks to develop cross-linguistically consistent treebank annotation for many languages. This tutorial gives an introduction to the UD framework and resources, from basic design principles to annotation guidelines and existing treebanks. We also discuss tools for developing and exploiting UD treebanks and survey applications of UD in NLP and linguistics.

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Elliptic Constructions: Spotting Patterns in UD Treebanks
Kira Droganova | Daniel Zeman
Proceedings of the NoDaLiDa 2017 Workshop on Universal Dependencies (UDW 2017)

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Slavic Forest, Norwegian Wood
Rudolf Rosa | Daniel Zeman | David Mareček | Zdeněk Žabokrtský
Proceedings of the Fourth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial)

We once had a corp, or should we say, it once had us They showed us its tags, isn’t it great, unified tags They asked us to parse and they told us to use everything So we looked around and we noticed there was near nothing We took other langs, bitext aligned: words one-to-one We played for two weeks, and then they said, here is the test The parser kept training till morning, just until deadline So we had to wait and hope what we get would be just fine And, when we awoke, the results were done, we saw we’d won So, we wrote this paper, isn’t it good, Norwegian wood.

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Universal Dependencies for Arabic
Dima Taji | Nizar Habash | Daniel Zeman
Proceedings of the Third Arabic Natural Language Processing Workshop

We describe the process of creating NUDAR, a Universal Dependency treebank for Arabic. We present the conversion from the Penn Arabic Treebank to the Universal Dependency syntactic representation through an intermediate dependency representation. We discuss the challenges faced in the conversion of the trees, the decisions we made to solve them, and the validation of our conversion. We also present initial parsing results on NUDAR.

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Core Arguments in Universal Dependencies
Daniel Zeman
Proceedings of the Fourth International Conference on Dependency Linguistics (Depling 2017)

2016

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If You Even Don’t Have a Bit of Bible: Learning Delexicalized POS Taggers
Zhiwei Yu | David Mareček | Zdeněk Žabokrtský | Daniel Zeman
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Part-of-speech (POS) induction is one of the most popular tasks in research on unsupervised NLP. Various unsupervised and semi-supervised methods have been proposed to tag an unseen language. However, many of them require some partial understanding of the target language because they rely on dictionaries or parallel corpora such as the Bible. In this paper, we propose a different method named delexicalized tagging, for which we only need a raw corpus of the target language. We transfer tagging models trained on annotated corpora of one or more resource-rich languages. We employ language-independent features such as word length, frequency, neighborhood entropy, character classes (alphabetic vs. numeric vs. punctuation) etc. We demonstrate that such features can, to certain extent, serve as predictors of the part of speech, represented by the universal POS tag.

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Universal Dependencies v1: A Multilingual Treebank Collection
Joakim Nivre | Marie-Catherine de Marneffe | Filip Ginter | Yoav Goldberg | Jan Hajič | Christopher D. Manning | Ryan McDonald | Slav Petrov | Sampo Pyysalo | Natalia Silveira | Reut Tsarfaty | Daniel Zeman
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Cross-linguistically consistent annotation is necessary for sound comparative evaluation and cross-lingual learning experiments. It is also useful for multilingual system development and comparative linguistic studies. Universal Dependencies is an open community effort to create cross-linguistically consistent treebank annotation for many languages within a dependency-based lexicalist framework. In this paper, we describe v1 of the universal guidelines, the underlying design principles, and the currently available treebanks for 33 languages.

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Towards Comparability of Linguistic Graph Banks for Semantic Parsing
Stephan Oepen | Marco Kuhlmann | Yusuke Miyao | Daniel Zeman | Silvie Cinková | Dan Flickinger | Jan Hajič | Angelina Ivanova | Zdeňka Urešová
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

We announce a new language resource for research on semantic parsing, a large, carefully curated collection of semantic dependency graphs representing multiple linguistic traditions. This resource is called SDP~2016 and provides an update and extension to previous versions used as Semantic Dependency Parsing target representations in the 2014 and 2015 Semantic Evaluation Exercises. For a common core of English text, this third edition comprises semantic dependency graphs from four distinct frameworks, packaged in a unified abstract format and aligned at the sentence and token levels. SDP 2016 is the first general release of this resource and available for licensing from the Linguistic Data Consortium in May 2016. The data is accompanied by an open-source SDP utility toolkit and system results from previous contrastive parsing evaluations against these target representations.

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Planting Trees in the Desert: Delexicalized Tagging and Parsing Combined
Daniel Zeman | David Mareček | Zhiwei Yu | Zdeněk Žabokrtský
Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Oral Papers

2015

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SemEval 2015 Task 18: Broad-Coverage Semantic Dependency Parsing
Stephan Oepen | Marco Kuhlmann | Yusuke Miyao | Daniel Zeman | Silvie Cinková | Dan Flickinger | Jan Hajič | Zdeňka Urešová
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

2014

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SemEval 2014 Task 8: Broad-Coverage Semantic Dependency Parsing
Stephan Oepen | Marco Kuhlmann | Yusuke Miyao | Daniel Zeman | Dan Flickinger | Jan Hajič | Angelina Ivanova | Yi Zhang
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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In-House: An Ensemble of Pre-Existing Off-the-Shelf Parsers
Yusuke Miyao | Stephan Oepen | Daniel Zeman
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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Machine Translation of Medical Texts in the Khresmoi Project
Ondřej Dušek | Jan Hajič | Jaroslava Hlaváčová | Michal Novák | Pavel Pecina | Rudolf Rosa | Aleš Tamchyna | Zdeňka Urešová | Daniel Zeman
Proceedings of the Ninth Workshop on Statistical Machine Translation

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HindEnCorp - Hindi-English and Hindi-only Corpus for Machine Translation
Ondřej Bojar | Vojtěch Diatka | Pavel Rychlý | Pavel Straňák | Vít Suchomel | Aleš Tamchyna | Daniel Zeman
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

We present HindEnCorp, a parallel corpus of Hindi and English, and HindMonoCorp, a monolingual corpus of Hindi in their release version 0.5. Both corpora were collected from web sources and preprocessed primarily for the training of statistical machine translation systems. HindEnCorp consists of 274k parallel sentences (3.9 million Hindi and 3.8 million English tokens). HindMonoCorp amounts to 787 million tokens in 44 million sentences. Both the corpora are freely available for non-commercial research and their preliminary release has been used by numerous participants of the WMT 2014 shared translation task.

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HamleDT 2.0: Thirty Dependency Treebanks Stanfordized
Rudolf Rosa | Jan Mašek | David Mareček | Martin Popel | Daniel Zeman | Zdeněk Žabokrtský
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

We present HamleDT 2.0 (HArmonized Multi-LanguagE Dependency Treebank). HamleDT 2.0 is a collection of 30 existing treebanks harmonized into a common annotation style, the Prague Dependencies, and further transformed into Stanford Dependencies, a treebank annotation style that became popular in recent years. We use the newest basic Universal Stanford Dependencies, without added language-specific subtypes. We describe both of the annotation styles, including adjustments that were necessary to make, and provide details about the conversion process. We also discuss the differences between the two styles, evaluating their advantages and disadvantages, and note the effects of the differences on the conversion. We regard the stanfordization as generally successful, although we admit several shortcomings, especially in the distinction between direct and indirect objects, that have to be addressed in future. We release part of HamleDT 2.0 freely; we are not allowed to redistribute the whole dataset, but we do provide the conversion pipeline.

2013

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CUni Multilingual Matrix in the WMT 2013 Shared Task
Karel Bílek | Daniel Zeman
Proceedings of the Eighth Workshop on Statistical Machine Translation

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Coordination Structures in Dependency Treebanks
Martin Popel | David Mareček | Jan Štěpánek | Daniel Zeman | Zdeněk Žabokrtský
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2012

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Automatic MT Error Analysis: Hjerson Helping Addicter
Jan Berka | Ondřej Bojar | Mark Fishel | Maja Popović | Daniel Zeman
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

We present a complex, open source tool for detailed machine translation error analysis providing the user with automatic error detection and classification, several monolingual alignment algorithms as well as with training and test corpus browsing. The tool is the result of a merge of automatic error detection and classification of Hjerson (Popović, 2011) and Addicter (Zeman et al., 2011) into the pipeline and web visualization of Addicter. It classifies errors into categories similar to those of Vilar et al. (2006), such as: morphological, reordering, missing words, extra words and lexical errors. The graphical user interface shows alignments in both training corpus and test data; the different classes of errors are colored. Also, the summary of errors can be displayed to provide an overall view of the MT system's weaknesses. The tool was developed in Linux, but it was tested on Windows too.

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HamleDT: To Parse or Not to Parse?
Daniel Zeman | David Mareček | Martin Popel | Loganathan Ramasamy | Jan Štěpánek | Zdeněk Žabokrtský | Jan Hajič
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

We propose HamleDT ― HArmonized Multi-LanguagE Dependency Treebank. HamleDT is a compilation of existing dependency treebanks (or dependency conversions of other treebanks), transformed so that they all conform to the same annotation style. While the license terms prevent us from directly redistributing the corpora, most of them are easily acquirable for research purposes. What we provide instead is the software that normalizes tree structures in the data obtained by the user from their original providers.

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Data Issues of the Multilingual Translation Matrix
Daniel Zeman
Proceedings of the Seventh Workshop on Statistical Machine Translation

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CUNI: Feature Selection and Error Analysis of a Transition-Based Parser
Daniel Zeman
Proceedings of the Workshop on Machine Translation and Parsing in Indian Languages

2011

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Hierarchical Phrase-Based MT at the Charles University for the WMT 2011 Shared Task
Daniel Zeman
Proceedings of the Sixth Workshop on Statistical Machine Translation

2010

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Hierarchical Phrase-Based MT at the Charles University for the WMT 2010 Shared Task
Daniel Zeman
Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR

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Data Issues in English-to-Hindi Machine Translation
Ondřej Bojar | Pavel Straňák | Daniel Zeman
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

Statistical machine translation to morphologically richer languages is a challenging task and more so if the source and target languages differ in word order. Current state-of-the-art MT systems thus deliver mediocre results. Adding more parallel data often helps improve the results; if it doesn't, it may be caused by various problems such as different domains, bad alignment or noise in the new data. In this paper we evaluate the English-to-Hindi MT task from this data perspective. We discuss several available parallel data sources and provide cross-evaluation results on their combinations using two freely available statistical MT systems. We demonstrate various problems encountered in the data and describe automatic methods of data cleaning and normalization. We also show that the contents of two independently distributed data sets can unexpectedly overlap, which negatively affects translation quality. Together with the error analysis, we also present a new tool for viewing aligned corpora, which makes it easier to detect difficult parts in the data even for a developer not speaking the target language.

2009

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A Simple Generative Pipeline Approach to Dependency Parsing and Semantic Role Labeling
Daniel Zeman
Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL 2009): Shared Task

2008

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Reusable Tagset Conversion Using Tagset Drivers
Daniel Zeman
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

Part-of-speech or morphological tags are important means of annotation in a vast number of corpora. However, different sets of tags are used in different corpora, even for the same language. Tagset conversion is difficult, and solutions tend to be tailored to a particular pair of tagsets. We propose a universal approach that makes the conversion tools reusable. We also provide an indirect evaluation in the context of a parsing task.

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Cross-Language Parser Adaptation between Related Languages
Daniel Zeman | Philip Resnik
Proceedings of the IJCNLP-08 Workshop on NLP for Less Privileged Languages

2005

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Improving Parsing Accuracy by Combining Diverse Dependency Parsers
Daniel Zeman | Zdeněk Žabokrtský
Proceedings of the Ninth International Workshop on Parsing Technology

2002

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Can Subcategorization Help a Statistical Dependency Parser?
Daniel Zeman
COLING 2002: The 19th International Conference on Computational Linguistics

2001

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How Much Will a RE-Based Preprocessor Help a Statistical Parser?
Daniel Zeman
Proceedings of the Seventh International Workshop on Parsing Technologies

2000

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Learning Verb Subcategorization from Corpora: Counting Frame Subsets
Daniel Zeman | Anoop Sarkar
Proceedings of the Second International Conference on Language Resources and Evaluation (LREC’00)

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Automatic Extraction of Subcategorization Frames for Czech
Anoop Sarkar | Daniel Zeman
COLING 2000 Volume 2: The 18th International Conference on Computational Linguistics

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