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  • 1. Ballesteros, Miguel
    et al.
    Gómez-Rodríguez, Carlos
    Nivre, Joakim
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Optimizing Planar and 2-Planar Parsers with MaltOptimizer2012In: Revista de Procesamiento de Lenguaje Natural (SEPLN), ISSN 1135-5948, E-ISSN 1989-7553, Vol. 49, 171-178 p.Article in journal (Refereed)
  • 2. Ballesteros, Miguel
    et al.
    Nivre, Joakim
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Going to the Roots of Dependency Parsing2013In: Computational linguistics - Association for Computational Linguistics (Print), ISSN 0891-2017, E-ISSN 1530-9312, Vol. 39, no 1, 5-13 p.Article in journal (Refereed)
    Abstract [en]

    Dependency trees used in syntactic parsing often include a root node representing a dummy word prefixed or suffixed to the sentence, a device that is generally considered a mere technical convenience and is tacitly assumed to have no impact on empirical results. We demonstrate that this assumption is false and that the accuracy of data-driven dependency parsers can in fact be sensitive to the existence and placement of the dummy root node. In particular, we show that a greedy, left-to-right, arc-eager transition-based parser consistently performs worse when the dummy root node is placed at the beginning of the sentence (following the current convention in data-driven dependency parsing) than when it is placed at the end or omitted completely. Control experiments with an arc-standard transition-based parser and an arc-factored graph-based parser reveal no consistent preferences but nevertheless exhibit considerable variation in results depending on root placement. We conclude that the treatment of dummy root nodes in data-driven dependency parsing is an underestimated source of variation in experiments and may also be a parameter worth tuning for some parsers.

  • 3. Ballesteros, Miguel
    et al.
    Nivre, Joakim
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    MaltOptimizer: Fast and Effective Parser Optimization2016In: Natural Language Engineering, ISSN 1351-3249, E-ISSN 1469-8110, Vol. 22, no 2, 187-213 p.Article in journal (Refereed)
    Abstract [en]

    Statistical parsers often require careful parameter tuning and feature selection. This is a nontrivial task for application developers who are not interested in parsing for its own sake, and it can be time-consuming even for experienced researchers. In this paper we present MaltOptimizer, a tool developed to automatically explore parameters and features for MaltParser, a transition-based dependency parsing system that can be used to train parser's given treebank data. MaltParser provides a wide range of parameters for optimization, including nine different parsing algorithms, an expressive feature specification language that can be used to define arbitrarily rich feature models, and two machine learning libraries, each with their own parameters. MaltOptimizer is an interactive system that performs parser optimization in three stages. First, it performs an analysis of the training set in order to select a suitable starting point for optimization. Second, it selects the best parsing algorithm and tunes the parameters of this algorithm. Finally, it performs feature selection and tunes machine learning parameters. Experiments on a wide range of data sets show that MaltOptimizer quickly produces models that consistently outperform default settings and often approach the accuracy achieved through careful manual optimization.

  • 4.
    Basirat, Ali
    et al.
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology. University of Tehran.
    Faili, Heshaam
    Nivre, Joakim
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    A statistical model for grammar mapping2016In: Natural Language Engineering, ISSN 1351-3249, E-ISSN 1469-8110, Vol. 22, no 2, 215-255 p.Article in journal (Refereed)
    Abstract [en]

    The two main classes of grammars are (a) hand-crafted grammars, which are developed bylanguage experts, and (b) data-driven grammars, which are extracted from annotated corpora.This paper introduces a statistical method for mapping the elementary structures of a data-driven grammar onto the elementary structures of a hand-crafted grammar in order to combinetheir advantages. The idea is employed in the context of Lexicalized Tree-Adjoining Grammars(LTAG) and tested on two LTAGs of English: the hand-crafted LTAG developed in theXTAG project, and the data-driven LTAG, which is automatically extracted from the PennTreebank and used by the MICA parser. We propose a statistical model for mapping anyelementary tree sequence of the MICA grammar onto a proper elementary tree sequence ofthe XTAG grammar. The model has been tested on three subsets of the WSJ corpus thathave average lengths of 10, 16, and 18 words, respectively. The experimental results show thatfull-parse trees with average F1 -scores of 72.49, 64.80, and 62.30 points could be built from94.97%, 96.01%, and 90.25% of the XTAG elementary tree sequences assigned to the subsets,respectively. Moreover, by reducing the amount of syntactic lexical ambiguity of sentences,the proposed model significantly improves the efficiency of parsing in the XTAG system.

  • 5.
    Basirat, Ali
    et al.
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Nivre, Joakim
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Greedy Universal Dependency Parsing with Right Singular Word Vectors2016Conference paper (Refereed)
    Abstract [en]

    A set of continuous feature vectors formed by right singular vectors of a transformed co-occurrence matrix are used with the Stanford neural dependency parser to train parsing models for a limited number of languages in the corpus of universal dependencies. We show that the feature vector can help the parser to remain greedy and be as accurate as (or even more accurate than) some other greedy and non-greedy parsers.

  • 6.
    Basirat, Ali
    et al.
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Nivre, Joakim
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Real-valued Syntactic Word Vectors (RSV) for Greedy Neural Dependency Parsing2017Conference paper (Refereed)
    Abstract [en]

    We show that a set of real-valued word vectors formed by right singular vectors of a transformed co-occurrence matrix are meaningful for determining different types of dependency relations between words. Our experimental results on the task of dependency parsing confirm the superiority of the word vectors to the other sets of word vectors generated by popular methods of word embedding. We also study the effect of using these vectors on the accuracy of dependency parsing in different languages versus using more complex parsing architectures.

  • 7. Bengoetxea, Kepa
    et al.
    Agirre, Eneko
    Nivre, Joakim
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Zhang, Yue
    Gojenola, Koldo
    On WordNet Semantic Classes and Dependency Parsing2014In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2014, 649-655 p.Conference paper (Refereed)
  • 8. Björkelund, Anders
    et al.
    Nivre, Joakim
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Non-Deterministic Oracles for Unrestricted Non-Projective Transition-Based Dependency Parsing2015In: Proceedings of the 14th International Conference on Parsing Technologies, 2015, 76-86 p.Conference paper (Refereed)
  • 9.
    Bohnet, Bernd
    et al.
    University of Birmingham.
    Nivre, Joakim
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Boguslavsky, Igor
    Russian Academy of Science.
    Farkas, Richard
    Szeged University.
    Ginter, Filip
    University of Turku.
    Hajic, Jan
    Charles University, Prague.
    Joint Morphological and Syntactic Analysis for Richly Inflected Languages2013In: Transactions of the Association for Computational Linguistics, ISSN 2307-387X, Vol. 1, no 4, 415-428 p.Article in journal (Refereed)
  • 10. Bunt, Harry
    et al.
    Maletti, Andreas
    Nivre, Joakim
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Grammars, Parsers and Recognizers2014In: Journal of Logic and Computation, Vol. 24, no 2, 309- p.Article in journal (Refereed)
  • 11. Bunt, Harry
    et al.
    Merlo, PaolaNivre, JoakimUppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Trends in Parsing Technology: Dependency Parsing, Domain Adaptation and Deep Parsing2010Collection (editor) (Other academic)
  • 12.
    Calacean, Mihaela
    et al.
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Nivre, Joakim
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    A Data-Driven Dependency Parser for Romanian2009In: Proceedings of the Seventh International Workshop on Treebanks and Linguistic Theories. / [ed] Frank van Eynde, Anette Frank & Koenraad de Smedt, 2009, 65-76 p.Conference paper (Refereed)
  • 13.
    de Lhoneux, Miryam
    et al.
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Nivre, Joakim
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Should Have, Would Have, Could Have: Investigating Verb Group Representations for Parsing with Universal Dependencies.2016In: Proceedings of the Workshop on Multilingual and Crosslingual Methods in NLP, Stroudsburg: Association for Computational Linguistics (ACL) , 2016, 10-19 p.Conference paper (Refereed)
    Abstract [en]

    Treebanks have recently been released for a number of languages with the harmonized annotation created by the Universal Dependencies project. The representation of certain constructions in UD are known to be suboptimal for parsing and may be worth transforming for the purpose of parsing. In this paper, we focus on the representation of verb groups. Several studies have shown that parsing works better when auxiliaries are the head of auxiliary dependency relations which is not the case in UD. We therefore transformed verb groups in UD treebanks, parsed the test set and transformed it back, and contrary to expectations, observed significant decreases in accuracy. We provide suggestive evidence that improvements in previous studies were obtained because the transformation helps disambiguating POS tags of main verbs and auxiliaries. The question of why parsing accuracy decreases with this approach in the case of UD is left open.

  • 14.
    de Lhoneux, Miryam
    et al.
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Nivre, Joakim
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    UD Treebank Sampling for Comparative Parser Evaluation2016Conference paper (Refereed)
  • 15.
    de Lhoneux, Miryam
    et al.
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Stymne, Sara
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Nivre, Joakim
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Arc-Hybrid Non-Projective Dependency Parsing with a Static-Dynamic Oracle2017In: IWPT 2017 15th International Conference on Parsing Technologies: Proceedings of the Conference, Pisa, Italy: Association for Computational Linguistics, 2017, 99-104 p.Conference paper (Refereed)
    Abstract [en]

    We extend the arc-hybrid transition system for dependency parsing with a SWAP transition that enables reordering of the words and construction of non-projective trees. Although this extension potentially breaks the arc-decomposability of the transition system, we show that the existing dynamic oracle can be modified and combined with a static oracle for the SWAP transition. Experiments on five languages with different degrees of non-projectivity show that the new system gives competitive accuracy and is significantly better than a system trained with a purely static oracle.

  • 16.
    de Lhoneux, Miryam
    et al.
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Stymne, Sara
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Nivre, Joakim
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Old School vs. New School: Comparing Transition-Based Parsers with and without Neural Network Enhancement2017In: Proceedings of the 15th Treebanks and Linguistic Theories Workshop (TLT), 2017, 99-110 p.Conference paper (Refereed)
    Abstract [en]

    In this paper, we attempt a comparison between "new school" transition-based parsers that use neural networks and their classical "old school" coun-terpart. We carry out experiments on treebanks from the Universal Depen-dencies project. To facilitate the comparison and analysis of results, we onlywork on a subset of those treebanks. However, we carefully select this sub-set in the hope to have results that are representative for the whole set oftreebanks. We select two parsers that are hopefully representative of the twoschools; MaltParser and UDPipe and we look at the impact of training sizeon the two models. We hypothesize that neural network enhanced modelshave a steeper learning curve with increased training size. We observe, how-ever, that, contrary to expectations, neural network enhanced models needonly a small amount of training data to outperform the classical models butthe learning curves of both models increase at a similar pace after that. Wecarry out an error analysis on the development sets parsed by the two sys-tems and observe that overall MaltParser suffers more than UDPipe fromlonger dependencies. We observe that MaltParser is only marginally betterthan UDPipe on a restricted set of short dependencies.

  • 17.
    de Lhoneux, Miryam
    et al.
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Yan, Shao
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Basirat, Ali
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Kiperwasser, Eliyahu
    Bar-Ilan University.
    Stymne, Sara
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Goldberg, Yoav
    Bar-Ilan University.
    Nivre, Joakim
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    From raw text to Universal Dependencies: look, no tags!2017In: Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, Vancouver, Canada: Association for Computational Linguistics, 2017, 207-217 p.Conference paper (Refereed)
    Abstract [en]

    We present the Uppsala submission to the CoNLL 2017 shared task on parsing from raw text to universal dependencies. Our system is a simple pipeline consisting of two components. The first performs joint word and sentence segmentation on raw text; the second predicts dependency trees from raw words. The parser bypasses the need for part-of-speech tagging, but uses word embeddings based on universal tag distributions. We achieved a macroaveraged LAS F1 of 65.11 in the official test run and obtained the 2nd best result for sentence segmentation with a score of 89.03. After fixing two bugs, we obtained an unofficial LAS F1 of 70.49.

  • 18. de Marneffe, Marie-Catherine
    et al.
    Dozat, Timothy
    Silveira, Natalia
    Haverinen, Katri
    Ginter, Filip
    Nivre, Joakim
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Manning, Christopher D.
    Universal Stanford Dependencies: A Cross-Linguistic Typology2014In: Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC), 2014, 4585-4592 p.Conference paper (Refereed)
    Abstract [en]

    Revisiting the now de facto standard Stanford dependency representation, we propose an improved taxonomy to capture grammatical relations across languages, including morphologically rich ones. We suggest a two-layered taxonomy: a set of broadly attested universal grammatical relations, to which language-specific relations can be added. We emphasize the lexicalist stance of the Stanford Dependencies, which leads to a particular, partially new treatment of compounding, prepositions, and morphology. We show how existing dependency schemes for several languages map onto the universal taxonomy proposed here and close with consideration of practical implications of dependency representation choices for NLP applications, in particular parsing.

  • 19. Dubremetz, Marie
    et al.
    Nivre, Joakim
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Extraction of Nominal Multiword Expressions in French2014In: Proceedings of the 10th Workshop on Multiword Expressions (MWE), 2014, 72-76 p.Conference paper (Refereed)
  • 20.
    Eryigit, Gülsen
    et al.
    Istanbul Technical University.
    Nivre, Joakim
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Oflazer, Kemal
    Sabanci University.
    Dependency Parsing of Turkish2008In: Computational linguistics - Association for Computational Linguistics (Print), ISSN 0891-2017, E-ISSN 1530-9312, Vol. 34, no 3, 357-389 p.Article in journal (Refereed)
    Abstract [en]

    The suitability of different parsing methods for different languages is an important topic in syntactic parsing. Especially lesser-studied languages, typologically different from the languages for which methods have originally been developed, pose interesting challenges in this respect. This article presents an investigation of data-driven dependency parsing of Turkish, an agglutinative, free constituent order language that can be seen as the representative of a wider class of languages of similar type. Our investigations show that morphological structure plays an essential role in finding syntactic relations in such a language. In particular, we show that employing sublexical units called inflectional groups, rather than word forms, as the basic parsing units improves parsing accuracy. We test our claim on two different parsing methods, one based on a probabilistic model with beam search and the other based on discriminative classifiers and a deterministic parsing strategy, and show that the usefulness of sublexical units holds regardless of the parsing method. We examine the impact of morphological and lexical information in detail and show that, properly used, this kind of information can improve parsing accuracy substantially. Applying the techniques presented in this article, we achieve the highest reported accuracy for parsing the Turkish Treebank.

  • 21. Farahmand, Meghdad
    et al.
    Nivre, Joakim
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Modeling the Statistical Idiosyncrasy of Multiword Expressions2015In: Proceedings of the 11th Workshop on Multiword Expressions, 2015, 34-38 p.Conference paper (Refereed)
  • 22. Farahmand, Meghdad
    et al.
    Smith, Aaron
    Nivre, Joakim
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    A Multiword Expression Data Set: Annotating Non-Compositionality and Conventionalization for English Noun Compounds2015In: Proceedings of the 11th Workshop on Multiword Expressions, 2015, 29-33 p.Conference paper (Refereed)
  • 23.
    Fishel, Mark
    et al.
    University of Tartu.
    Nivre, Joakim
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Voting and Stacking in Data-Driven Dependency Parsing2009In: Proceedigs of the 17th Nordic Conference on Computational Linguistics / [ed] Kristiina Jokinen & Eckhard Bick, Tartu: Tartu University Library , 2009, 219-222 p.Conference paper (Refereed)
  • 24. Foster, Jennifer
    et al.
    Cetinoglu, Ozlem
    Wagner, Joachim
    Le Roux, Joseph
    Hogan, Stephen
    Nivre, Joakim
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Hogan, Deirdre
    van Genabith, Josef
    # hardtoparse: POS Tagging and Parsing the Twitterverse2011In: Proceedings of the Workshop On Analyzing Microtext (AAAI 2011), AAAI Press, 2011, 20-25 p.Conference paper (Refereed)
  • 25. Gomez-Rodriguez, Carlos
    et al.
    Nivre, Joakim
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Divisible Transition Systems and Multiplanar Dependency Parsing2013In: Computational linguistics - Association for Computational Linguistics (Print), ISSN 0891-2017, E-ISSN 1530-9312, Vol. 39, no 4, 799-845 p.Article in journal (Refereed)
    Abstract [en]

    Transition-based parsing is a widely used approach for dependency parsing that combines high efficiency with expressive feature models. Many different transition systems have been proposed, often formalized in slightly different frameworks. In this article, we show that a large number of the known systems for projective dependency parsing can be viewed as variants of the same stack-based system with a small set of elementary transitions that can be composed into complex transitions and restricted in different ways. We call these systems divisible transition systems and prove a number of theoretical results about their expressivity and complexity. In particular, we characterize an important subclass called efficient divisible transition systems that parse planar dependency graphs in linear time. We go on to show, first, how this system can be restricted to capture exactly the set of planar dependency trees and, secondly, how the system can be generalized to k-planar trees by making use of multiple stacks. Using the first known efficient test for k-planarity, we investigate the coverage of k-planar trees in available dependency treebanks and find a very good fit for 2-planar trees. We end with an experimental evaluation showing that our 2-planar parser gives significant improvements in parsing accuracy over the corresponding 1-planar and projective parsers for data sets with non-projective dependency trees and performs on a par with the widely used arc-eager pseudo-projective parser.

  • 26.
    Hajic, Jan
    et al.
    Charles University.
    Ciaramita, Massimiliano
    Google.
    Johansson, Richard
    IRST.
    Kawahara, Daisuke
    Marti, Antonia
    Marquez, Lluis
    Meyers, Adam
    Nivre, Joakim
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Pado, Sebastian
    Stepanek, Jan
    Stranak, Pavel
    Surdeanu, Mihai
    Xue, Nianwen
    Zhang, i
    The CoNLL-2009 Shared Task: Syntactic and Semantic Dependencies in Multiple Languages2009In: Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL 2009): Shared Task, Association for Computational Linguistics , 2009, 1-18 p.Conference paper (Refereed)
  • 27.
    Hall, Johan
    et al.
    Växjö University.
    Nivre, Joakim
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    A Dependency-Driven Parser for German Dependency and Constituency Representations2008In: Proceedings of the ACL Workshop on Parsing German, Stroudsburg, PA: Association for Computational Linguistics (ACL) , 2008, 47-54 p.Conference paper (Refereed)
    Abstract [en]

    We present a dependency-driven parser that parses both dependency structures and constituent structures. Constituency representations are automatically transformed into dependency representations with complex arc labels, which makes it possible to recover the constituent structure with both constituent labels and grammatical functions. We report a labeled attachment score close to 90% for dependency versions of the TIGER and T¨uBa- D/Z treebanks. Moreover, the parser is able to recover both constituent labels and grammatical functions with an F-Score over 75% for T¨uBa-D/Z and over 65% for TIGER.

  • 28.
    Hall, Johan
    et al.
    Växjö University.
    Nivre, Joakim
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Parsing Discontinuous Phrase Structure with Grammatical Functions2008In: Advances in Natural Language Processing: 6th international Conference, GoTAL 2008, Berlin / Heidelberg: Springer , 2008, 169-180 p.Conference paper (Refereed)
    Abstract [en]

    This paper presents a novel technique for parsing discontinuous phrase structure representations, labeled with both phrase labels and grammatical functions. Phrase structure representations are transformed into dependency representations with complex edge labels, which makes it possible to induce a dependency parser model that recovers the phrase structure with both phrase labels and grammatical functions. We perform an evaluation on the German TIGER treebank and the Swedish Talbanken05 treebank and report competitive results for both data sets.

  • 29. Hall, Johan
    et al.
    Nivre, Joakim
    Uppsala University, Humanistisk-samhällsvetenskapliga vetenskapsområdet, Faculty of Languages, Department of Linguistics and Philology. Datorlingvistik.
    Nilsson, Jens
    Discriminative learning for data-driven dependency parsing2006In: Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics, 2006Conference paper (Refereed)
  • 30.
    Hardmeier, Christian
    et al.
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Nivre, Joakim
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Tiedemann, Jörg
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Document-Wide Decoding for Phrase-Based Statistical Machine Translation2012In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Association for Computational Linguistics, 2012, 1179-1190 p.Conference paper (Refereed)
    Abstract [en]

    Independence between sentences is an assumption deeply entrenched in the models and algorithms used for statistical machine translation (SMT), particularly in the popular dynamic programming beam search decoding algorithm. This restriction is an obstacle to research on more sophisticated discourse-level models for SMT. We propose a stochastic local search decoding method for phrase-based SMT, which permits free document-wide dependencies in the models. We explore the stability and the search parameters of this method and demonstrate that it can be successfully used to optimise a document-level semantic language model.

  • 31.
    Hardmeier, Christian
    et al.
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Nivre, Joakim
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Tiedemann, Jörg
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Tree Kernels for Machine Translation Quality Estimation2012In: Proceedings of the 7th Workshop on Statistical Machine Translation, Association for Computational Linguistics, 2012, 109-113 p.Conference paper (Refereed)
    Abstract [en]

    This paper describes Uppsala University’s submissions to the Quality Estimation (QE) shared task at WMT 2012. We present a QE system based on Support Vector Machine regression, using a number of explicitly defined features extracted from the Machine Translation input, output and models in combination with tree kernels over constituency and dependency parse trees for the input and output sentences. We confirm earlier results suggesting that tree kernels can be a useful tool for QE system construction especially in the early stages of system design.

  • 32.
    Hardmeier, Christian
    et al.
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Stymne, Sara
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Tiedemann, Jörg
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Nivre, Joakim
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Docent: A Document-Level Decoder for Phrase-Based Statistical Machine Translation2013In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, Association for Computational Linguistics, 2013, 193-198 p.Conference paper (Refereed)
    Abstract [en]

    We describe Docent, an open-source decoder for statistical machine translation that breaks with the usual sentence-by-sentence paradigm and translates complete documents as units. By taking translation to the document level, our decoder can handle feature models with arbitrary discourse-wide dependencies and constitutes an essential infrastructure component in the quest for discourse-aware SMT models.

  • 33.
    Hardmeier, Christian
    et al.
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Stymne, Sara
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Tiedemann, Jörg
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Smith, Aaron
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Nivre, Joakim
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Anaphora Models and Reordering for Phrase-Based SMT2014In: Proceedings of the Ninth Workshop on Statistical Machine Translation, Association for Computational Linguistics, 2014, 122-129 p.Conference paper (Refereed)
    Abstract [en]

    We describe the Uppsala University systems for WMT14. We look at the integration of a model for translating pronominal anaphora and a syntactic dependency projection model for English–French. Furthermore, we investigate post-ordering and tunable POS distortion models for English–German.

  • 34.
    Hardmeier, Christian
    et al.
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Tiedemann, Jörg
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Nivre, Joakim
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Latent Anaphora Resolution for Cross-Lingual Pronoun Prediction2013In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, 2013, 380-391 p.Conference paper (Refereed)
    Abstract [en]

    This paper addresses the task of predicting the correct French translations of third-person subject pronouns in English discourse, a problem that is relevant as a prerequisite for machine translation and that requires anaphora resolution. We present an approach based on neural networks that models anaphoric links as latent variables and show that its performance is competitive with that of a system with separate anaphora resolution while not requiring any coreference-annotated training data. This demonstrates that the information contained in parallel bitexts can successfully be used to acquire knowledge about pronominal anaphora in an unsupervised way.

  • 35.
    Hardmeier, Christian
    et al.
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Tiedemann, Jörg
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Nivre, Joakim
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Translating Pronouns with Latent Anaphora Resolution2014Conference paper (Other academic)
    Abstract [en]

    We discuss the translation of anaphoric pronouns in statistical machine translation from English into French. Pronoun translation requires resolving the antecedents of the pronouns in the input, a classic discourse processing problem that is usually approached through supervised learning from manually annotated data. We cast cross-lingual pronoun prediction as a classification task and present a neural network architecture that incorporates the links between anaphors and potential antecedents as latent variables, allowing us to train the classifier on parallel text without explicit supervision for the anaphora resolver. We demonstrate that our approach works just as well for classification as using an external coreference resolver whereas its impact in a practical translation experiment is more limited.

  • 36. Ide, Nancy
    et al.
    Calzolari, Nicoletta
    Eckle-Kohler, Judith
    Gibbon, Dafydd
    Hellmann, Sebastian
    Lee, Kiyong
    Nivre, Joakim
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Romary, Laurent
    Community Standards for Linguistically-Annotated Resources2017In: Handbook of Linguistic Annotation / [ed] Ide, Nancy; Pustejovsky, James, Springer , 2017, 113-165 p.Chapter in book (Refereed)
  • 37.
    Kapociute-Dzikiene, Jurgita
    et al.
    Kaunas University of Technology.
    Nivre, Joakim
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Krupavicius, Algis
    Kaunas University of Technology.
    Lithuanian Dependency Parsing with Rich Morphological Features2013In: Proceedings of the Fourth Workshop on Statistical Parsing of Morphologically-Rich Languages, 2013, 12-21 p.Conference paper (Refereed)
  • 38.
    Kuhlmann, Marco
    et al.
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Nivre, Joakim
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Transition-Based Techniques for Non-Projective Dependency Parsing2010In: Northern European Journal of Language Technology (NEJLT), ISSN 2000-1533, Vol. 2, no 1, 1-19 p.Article in journal (Refereed)
    Abstract [en]

    We present an empirical evaluation of three methods for the treatment of non-projective structures in transition-based dependency parsing: pseudo-projective parsing, non-adjacent arc transitions, and online reordering. We compare both the theoretical coverage and the empirical performance of these methods using data from Czech, English and German. The results show that although online reordering is the only method with complete theoretical coverage, all three techniques exhibit high precision but somewhat lower recall on non-projective dependencies and can all improve overall parsing accuracy provided that non-projective dependencies are frequent enough. We also find that the use of non-adjacent arc transitions may lead to a drop in accuracy on projective dependencies in the presence of long-distance non-projective dependencies, an effect that is not found for the two other techniques.

  • 39. Kübler, Sandra
    et al.
    Nivre, JoakimUppsala University, Humanistisk-samhällsvetenskapliga vetenskapsområdet, Faculty of Languages, Department of Linguistics and Philology. Datorlingvistik.
    Proceedings of the Third Workshop on Treebanks and Linguistic Theories2004Conference proceedings (editor) (Refereed)
  • 40. Lager, Torbjörn
    et al.
    Nivre, Joakim
    Uppsala University, Humanistisk-samhällsvetenskapliga vetenskapsområdet, Faculty of Languages, Department of Linguistics and Philology. Datorlingvistik.
    Part of speech tagging from a logical point of view2001In: Logical Aspects of Artificial Intelligence, 2001Conference paper (Refereed)
  • 41. Lavelli, Alberto
    et al.
    Hall, Johan
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Nilsson, Jens
    Växjö universitet.
    Nivre, Joakim
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    MaltParser at the EVALITA 2009 Dependency Parsing Task2009In: Proceedings of EVALITA 2009, 2009Conference paper (Refereed)
  • 42.
    Marie, Dubremetz
    et al.
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Joakim, Nivre
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Extraction of Nominal Multiword Expressions in French2014In: Proceedings of the 10th Workshop on Multiword Expressions (MWE), Gothenburg, Sweden: Association for Computational Linguistics, 2014, 72-76 p.Conference paper (Refereed)
  • 43.
    Marie, Dubremetz
    et al.
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Joakim, Nivre
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Rhetorical Figure Detection: the Case of Chiasmus2015In: Proceedings of the Fourth Workshop on Computational Linguistics for Literature, 2015, 23-31 p.Conference paper (Refereed)
  • 44.
    Marinov, Svetoslav
    et al.
    Uppsala University, Humanistisk-samhällsvetenskapliga vetenskapsområdet, Faculty of Languages, Department of Linguistics and Philology. Datorlingvistik.
    Nivre, Joakim
    Uppsala University, Humanistisk-samhällsvetenskapliga vetenskapsområdet, Faculty of Languages, Department of Linguistics and Philology. Datorlingvistik.
    A data-driven parser for Bulgarian2005In: Proceedings of the Fourth Workshop on Treebanks and Linguistic Theories, 2005Conference paper (Refereed)
  • 45. McDonald, Ryan
    et al.
    Nivre, Joakim
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Analyzing and Integrating Dependency Parsers2011In: Computational linguistics - Association for Computational Linguistics (Print), ISSN 0891-2017, E-ISSN 1530-9312, Vol. 37, no 1, 197-230 p.Article in journal (Refereed)
  • 46.
    Megyesi, Beata
    et al.
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Dahlqvist, Bengt
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Pettersson, Eva
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Gustafson-Capkova, Sofia
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Nivre, Joakim
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Supporting Research Environment for Less Explored Languages: A Case Study of Swedish and Turkish2008In: Resourceful Language Technology: Festschrift in Honor of Anna Sågvall Hein / [ed] Nivre, Joakim, Dahllöf, Mats, Megyesi, Beáta, Uppsala: Uppsala universitet, 2008, 96-110 p.Chapter in book (Other academic)
  • 47.
    Megyesi, Beáta
    et al.
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Dahlqvist, Bengt
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Csató, Éva Á.
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Nivre, Joakim
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    The English-Swedish-Turkish Parallel Treebank2010In: Proceedings of Language Resources and Evaluation (LREC 2010), 2010Conference paper (Refereed)
    Abstract [en]

    We describe a syntactically annotated parallel corpus containing typologically partly different languages, namely English, Swedish and Turkish. The corpus consists of approximately 300 000 tokens in Swedish, 160 000 in Turkish and 150 000 in English, containing both fiction and technical documents. We build the corpus by using the Uplug toolkit for automatic structural markup, such as tokenization and sentence segmentation, as well as sentence and word alignment. In addition, we use basic language resource kits for the linguistic analysis of the languages involved. The annotation is carried on various layers from morphological and part of speech analysis to dependency structures. The tools used for linguistic annotation, e.g. HunPos tagger and MaltParser, are freely available data-driven resources, trained on existing corpora and treebanks for each language. The parallel treebank is used in teaching and linguistic research to study the relationship between the structurally different languages. In order to study the treebank, several tools have been developed for the visualization of the annotation and alignment, allowing search for linguistic patterns.

  • 48. Megyesi, Beáta
    et al.
    Dahlqvist, Bengt
    Pettersson, Eva
    Nivre, Joakim
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Swedish-Turkish Parallel Treebank2008In: Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC), 2008, 470-473 p.Conference paper (Refereed)
  • 49. Nilsson, Jens
    et al.
    Hall, Johan
    Nivre, Joakim
    Uppsala University, Humanistisk-samhällsvetenskapliga vetenskapsområdet, Faculty of Languages, Department of Linguistics and Philology. Datorlingvistik.
    MAMBA meets TIGER: Reconstructing a Swedish treebank from antiquity2005In: Proceedings from the special session on treebanks at NODALIDA 2007, 2005Conference paper (Refereed)
  • 50.
    Nilsson, Jens
    et al.
    Växjö universitet.
    Löwe, Welf
    Växjö universitet.
    Hall, Johan
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Nivre, Joakim
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Parsing Formal Languages using Natural Language Parsing Techniques.2009In: Proceedings of the 11th International Conference on Parsing Technologies (IWPT), Association for Computational Linguistics , 2009, 49-60 p.Conference paper (Refereed)
1234 1 - 50 of 164
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