A Treebank-based Investigation of IPP-triggering Verbs in Dutch

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semantic type, annotators' mistakes are not so frequent. This type of disagreement arises from (1) semantic closeness of some relation types, (2) semantic ambiguity/vagueness of some contexts, and (3) in the case of the relation of explication also from the complex nature of the relation. Acknowledgments We gratefully acknowledge support from the Grant Agency of the Czech Republic (grants P406/12/0658 and P406/2010/0875) and the Ministry of Education, Youth and Sports in the Czech Republic, program KONTAKT (ME10018) and the LINDAT-Clarin project (LM2010013). References [1] Cohen J. (1960) A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), pp. 37–46 [2] Hajič J., Vidová Hladká B., Böhmová A., Hajičová E. (2000) The Prague Dependency Treebank: A Three-Level Annotation Scenario. In: Treebanks: Building and Using Syntactically Annotated Corpora (ed. Anne Abeille), Kluver Academic Publishers [3] Hajič J., Panevová J., Hajičová E., Sgall P., Pajas P., Štěpánek J., Havelka J., Mikulová M., Žabokrtský Z., Ševčíková-Razímová M. (2006) Prague Dependency Treebank 2.0. Software prototype, Linguistic Data Consortium, Philadelphia, PA, USA, ISBN 1-58563-370-4, www.ldc.upenn.edu [4] Mikulová M. et al. (2005) Annotation on the tectogrammatical layer in the Prague Dependency Treebank. Annotation manual. Available from http://ufal.mff.cuni.cz/pdt2.0/doc/manuals/en/t-layer/pdf/t-man-en.pdf [5] Miltsakaki E., Robaldo L., Lee A., Joshi A. (2008) Sense annotation in the Penn Discourse Treebank. In: Computational Linguistics and Intelligent Text Processing, Lecture Notes in Computer Science, Vol 4919, pp. 275–286 [6] Mírovský J., Mladová L., Zikánová Š. (2010) Connective-Based Measuring of the Inter-Annotator Agreement in the Annotation of Discourse in PDT. In: Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010), Beijing, China, pp. 775–781 [7] Mírovský J., Mladová L., Žabokrtský Z.: Annotation Tool for Discourse in PDT. In: Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010), Tsinghua University Press, Beijing, China, pp. 9–12 [8] Mladová L., Zikánová Š., Hajičová E. (2008) From Sentence to Discourse: Building an Annotation Scheme for Discourse Based on Prague Dependency Treebank. In: Proceedings of the 6th International Conference on Language Resources and Evaluation (LREC 2008), Marrakech, Morocco [9] Prasad R., Dinesh N., Lee A., Miltsakaki E., Robaldo L., Joshi A., Webber B. (2008) The Penn Discourse Treebank 2.0. In Proceedings of the 6th International Conference on Language Resources and Evaluation, CD-ROM 132
Will a Parser Overtake Achilles? First experiments on parsing the Ancient Greek Dependency Treebank Francesco Mambrini∗ and Marco Passarotti† ∗University of Cologne (Germany) †Università Cattolica del Sacro Cuore, Milan (Italy) Abstract We present a number of experiments on parsing the Ancient Greek Dependency Treebank (AGDT), i.e. the largest syntactically annotated corpus of Ancient Greek currently available (350k words ca). Although the AGDT is rather unbalanced and far from being representative of all genres and periods of Ancient Greek, no attempt has been made so far to perform automatic dependency parsing of Ancient Greek texts. By testing and evaluating one probabilistic dependency parser (MaltParser), we focus on how to improve the parsing accuracy and how to customize a feature model that fits the distinctive properties of Ancient Greek syntax. Also, we prove the impact of genre and author diversity on parsing performances. 1 Introduction Among the languages currently spoken in the world, Greek has one of the longest documented histories. The first texts written in Greek that have survived to our days date from the mid of the second millennium BCE (around 1420-1400). From the phase that is commonly known as Ancient Greek (9th Century BCE - 6th Century CE), a vast literature has been preserved and thoroughly studied. In rough numbers, two of the most important digital collection of Ancient Greek texts, which are far from being exhaustive, the Thesaurus Linguae Graecae (TLG) 1 and the Perseus Digital Library 2 , contain respectively more than 105 and 13 million words. 1.1 Annotated corpora of Ancient Greek Some of the Ancient Greek texts are today included in two different treebanks. The Ancient Greek Dependency Treebank (AGDT) [3] is a dependency-based treebank of literary works of the Archaic and Classical age published by Perseus 3 . 1 TLG: http://www.tlg.uci.edu/, which goes so far as to the fall of Byzantium (1453 BCE). 2 Perseus Digital Library: http://www.perseus.tufts.edu/hopper/ 3 AGDT: http://nlp.perseus.tufts.edu/syntax/treebank/greek.html 133
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A Treebank-based Investigation of I
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participle and a (te-)infinitival c
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Some verbs occur twice, since they
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Profiling Feature Selection for Nam
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prepositional objects (FOPP, OPP).
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the limited size of annotated data
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with high precision typically have
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‘This was “not significantly”
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The preposition durch typically has
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Non-Projective Structures in Indian
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the sequential order of nodes in a
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extra-posed relative clause that ge
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Experiments on Dependency Parsing o
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for mitigating the effects of spars
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obtained with MALTParser is 76.61%
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as a standardised serialisation for
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constituency and dependency, possib
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SynAF and/or in . However, they sha
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shows how some elements that are no
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Example
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‏ Example 8: represent
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Chinese) as the direct object NP.
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Example 15: Tokens and Word Forms
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In a second experiment, a dataset w
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[3] Leech G. N., Barnett, R. & Kahr
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Effectively long-distance dependenc
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In French, another case of eLDD is
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elativization, it-clefts or questio
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4.2.3 Annotation methodology Becaus
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Number of occurrences in FTB +SEQTB
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producing treebank based LFG approx
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Logical Form Representation for Lin
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gerundives for a total amount of so
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object+indirect object/object one.
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(VP (VB patch) ) ) ) (VP (VBZ is) (
Page 75 and 76: Types Adverb. Adject. Verbs Nouns T
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Page 87 and 88: 4 Patching Coverage Gaps with An Ap
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Page 91 and 92: ParDeepBank: Multiple Parallel Deep
Page 93 and 94: 2 The ParDeepBank The PTB has emerg
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Page 113 and 114: citizens of Bilbao] are very involv
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Page 117 and 118: Nominal P R F1 MUC 75.33% 81.33% 78
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Page 121 and 122: Analyzing the Most Common Errors in
Page 123 and 124: Graph 1 shows results of subsequent
Page 125: in the semantic type). This situati
Page 129 and 130: annotation is also based on a depen
Page 131 and 132: Set Name Sentences Tokens % Train/T
Page 133 and 134: dency relations (PRED, OBJ, SBJ, AD
Page 135 and 136: ton and Stacklazy), we trained a mo
Page 137 and 138: Feature Function Column Name Addres
Page 139 and 140: Using Parallel Treebanks for Machin
Page 141 and 142: phrases are generated by a shallow
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Page 147 and 148: Checkpoint Instances Google PT Our
Page 149 and 150: 6 Conclusions In this paper we have
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Page 163 and 164: Translational divergences and their
Page 165 and 166: posed so far, Cyrus’ work did not
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Phenomenon Attested example Std. co
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Figure 1: French Social Media Bank
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Impact of treebank characteristics
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Det Adj N (a) Danish Det Adj N (b)
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sv: Bestämmelserna i detta avtal f
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100 90 80 Unlabelled attachment 70
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vidner , tilhørere og tiltalte wit
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mance is not inconsiderable, as was
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Genitives in Hindi Treebank: An Att
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A genitive can occur with complex p
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quite high. This motivates us towar
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A Treebank-based Investigation of IPP-triggering Verbs in Dutch

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