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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, p. 171-178Article 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, p. 5-13Article 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, p. 187-213Article 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.
    de Lhoneux, Miryam
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Kulmizev, Artur
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Kurfal, Murathan
    Department of Linguistics, Stockholm University.
    Nivre, Joakim
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Östling, Robert
    Department of Linguistics, Stockholm University.
    Polyglot Parsing for One Thousand and One Languages (And Then Some)2019Conference paper (Other academic)
  • 5.
    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, p. 215-255Article 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.

  • 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.
    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.

  • 7.
    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 vectors2019In: Journal of experimental and theoretical artificial intelligence (Print), ISSN 0952-813X, E-ISSN 1362-3079Article in journal (Refereed)
    Abstract [en]

    We introduce a word embedding method that generates a set of real-valued word vectors from a distributional semantic space. The semantic space is built with a set of context units (words) which are selected by an entropy-based feature selection approach with respect to the certainty involved in their contextual environments. We show that the most predictive context of a target word is its preceding word. An adaptive transformation function is also introduced that reshapes the data distribution to make it suitable for dimensionality reduction techniques. The final low-dimensional word vectors are formed by the singular vectors of a matrix of transformed data. We show that the resulting word vectors are as good as other sets of word vectors generated with popular word embedding methods.

  • 8.
    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.

  • 9. 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, p. 649-655Conference paper (Refereed)
  • 10. 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, p. 76-86Conference paper (Refereed)
  • 11.
    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, p. 415-428Article in journal (Refereed)
  • 12. Bouma, Gosse
    et al.
    Hajič, Jan
    Haug, Dag
    Nivre, Joakim
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Solberg, Per Erik
    Øvrelid, Lilja
    Expletives in Universal Dependency Treebanks2018In: Proceedings of the Second Workshop on Universal Dependencies (UDW 2018), 2018, p. 18-26Conference paper (Refereed)
  • 13. 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, p. 309-Article in journal (Refereed)
  • 14. 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)
  • 15.
    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, p. 65-76Conference paper (Refereed)
  • 16.
    Constant, Matthieu
    et al.
    Univ Paris Diderot, Univ Paris Est, INRIA, Alpage,LIGM,UMR 8049, Paris, France.
    Nivre, Joakim
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    A Transition-Based System for Joint Lexical and Syntactic Analysis2016In: PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1 / [ed] Erk, K Smith, NA, 2016, p. 161-171Conference paper (Refereed)
    Abstract [en]

    We present a transition-based system that jointly predicts the syntactic structure and lexical units of a sentence by building two structures over the input words: a syntactic dependency tree and a forest of lexical units including multiword expressions (MWEs). This combined representation allows us to capture both the syntactic and semantic structure of MWEs, which in turn enables deeper downstream semantic analysis, especially for semi-compositional MWEs. The proposed system extends the arc-standard transition system for dependency parsing with transitions for building complex lexical units. Experiments on two different data sets show that the approach significantly improves MWE identification accuracy (and sometimes syntactic accuracy) compared to existing joint approaches.

  • 17.
    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, p. 10-19Conference 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.

  • 18.
    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)
  • 19.
    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, p. 99-104Conference 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.

  • 20.
    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, p. 99-110Conference 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.

  • 21.
    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, p. 207-217Conference 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.

  • 22. 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, p. 4585-4592Conference 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.

  • 23.
    de Marneffe, Marie-Catherine
    et al.
    Ohio State Univ, Dept Linguist, Columbus, OH 43210 USA.
    Nivre, Joakim
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Dependency Grammar2019In: Annual review of linguistics, E-ISSN 2333-9691, Vol. 5, p. 197-218Article in journal (Refereed)
    Abstract [en]

    Dependency grammar is a descriptive and theoretical tradition in linguistics that can be traced back to antiquity. It has long been influential in the European linguistics tradition and has more recently become a mainstream approach to representing syntactic and semantic structure in natural language processing. In this review, we introduce the basic theoretical assumptions of dependency grammar and review some key aspects in which different dependency frameworks agree or disagree. We also discuss advantages and disadvantages of dependency representations and introduce Universal Dependencies, a framework for multilingual dependency-based morphosyntactic annotation that has been applied to more than 60 languages.

  • 24. 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, p. 72-76Conference paper (Refereed)
  • 25.
    Dubremetz, Marie
    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.
    Extraction of Nominal Multiword Expressions in French2014In: Proceedings of the 10th Workshop on Multiword Expressions (MWE), Gothenburg, Sweden: Association for Computational Linguistics, 2014, p. 72-76Conference paper (Refereed)
  • 26.
    Dubremetz, Marie
    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.
    Machine Learning for Rhetorical Figure Detection: More Chiasmus with Less Annotation2017In: Proceedings of the 21st Nordic Conference of Computational Linguistics, Gothenburg, Sweden, 2017, p. 37-45Conference paper (Refereed)
  • 27.
    Dubremetz, Marie
    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.
    Rhetorical Figure Detection: Chiasmus, Epanaphora, EpiphoraIn: Frontiers in Digital Humanities, E-ISSN 2297-2668Article in journal (Refereed)
  • 28.
    Dubremetz, Marie
    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.
    Rhetorical Figure Detection: Chiasmus, Epanaphora, Epiphora2018In: Frontiers in Digital Humanities, ISSN 2297-2668, Vol. 5, no 10Article in journal (Refereed)
    Abstract [en]

    Rhetorical figures are valuable linguistic data for literary analysis. In this article, we target the detection of three rhetorical figures that belong to the family of repetitive figures: chiasmus (I go where I please, and I please where I go.), epanaphora also called anaphora (“Poor old European Commission! Poor old European Council.”) and epiphora (“This house is mine. This car is mine. You are mine.”). Detecting repetition of words is easy for a computer but detecting only the ones provoking a rhetorical effect is difficult because of many accidental and irrelevant repetitions. For all figures, we train a log-linear classifier on a corpus of political debates. The corpus is only very partially annotated, but we nevertheless obtain good results, with more than 50% precision for all figures. We then apply our models to totally different genres and perform a comparative analysis, by comparing corpora of fiction, science and quotes. Thanks to the automatic detection of rhetorical figures, we discover that chiasmus is more likely to appear in the scientific context whereas epanaphora and epiphora are more common in fiction.

  • 29.
    Dubremetz, Marie
    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.
    Rhetorical Figure Detection: the Case of Chiasmus2015In: Proceedings of the Fourth Workshop on Computational Linguistics for Literature, 2015, p. 23-31Conference paper (Refereed)
  • 30.
    Dubremetz, Marie
    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.
    Syntax Matters for Rhetorical Structure: The Case of Chiasmus2016In: Proceedings of the Fifth Workshop on Computational Linguistics for Literature, 2016, p. 47-53Conference paper (Refereed)
  • 31.
    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.