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

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References [1] Drach, Erich (1937) Grundgedanken der Deutschen Satzlehre. Frankfurt/Main. [2] Faruqui, Manaal and Padó, Sebastian (2010) Training and Evaluating a German Named Entity Recognizer with Semantic Generalization. In Proceedings of the Tenth Conference on Natural Language Processing (Konvens 2010), Saarbrücken, Germany, pp. 129–134. [3] Finkel, Jenny Rose, Grenager, Trond and Manning, Christopher (2005) Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL 2005), Ann Arbor, USA, pp. 363–370. [4] Herling, Simon Heinrich Adolf (1821) Über die Topik der deutschen Sprache. In Abhandlungen des frankfurterischen Gelehrtenvereins für deutsche Sprache, pp. 296–362, 394. Frankfurt/Main. Drittes Stück. [5] Höhle, Tilmann N. (1986) Der Begriff ‘Mittelfeld’. Anmerkungen über die Theorie der topologischen Felder. In A. Schöne (Ed.) Kontroversen alte und neue. Akten des 7. Internationalen Germanistenkongresses Göttingen, pp. 329–340. Tübingen: Niemeyer. [6] Ploch, Danuta, Hennig, Leonhard, Duka, Angelina, De Luca, Ernesto William and Albayrak, Sahin (2012) GerNED: A German Corpus for Named Entity Disambiguation. In Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC 2012), 20-27 May 2012, Istanbul, Turkey. [7] Schiller, Anne, Teufel, Simone, Stöckert, Christine and Thielen, Christine (1999) Guidelines für das Tagging deutscher Textcorpora mit STTS. Technical report, Universities of Stuttgart and Tübingen, Germany. URL: http://www.sfs.unituebingen.de/fileadmin/static/ascl/resources/stts-1999.pdf [8] Telljohann, Heike, Hinrichs, Erhard W., Kübler, Sandra, Zinsmeister, Heike and Beck, Kathrin (2012) Stylebook for the Tübingen Treebank of Written German (TüBa-D/Z). Department of General and Computational Linguistics, University of Tübingen, Germany. URL: http://www.sfs.uni-tuebingen.de/fileadmin/static/ascl/resources/tuebadzstylebook-1201.pdf [9] Tjong Kim Sang, Erik F. and De Meulder, Fien (2003) Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition. In Proceedings of the Seventh Conference on Natural Language Learning (CoNLL 2003), Edmonton, Canada, pp. 142–147. 24
Non-Projective Structures in Indian Language Treebanks Riyaz Ahmad Bhat and Dipti Misra Sharma Language Technology Research Center, IIIT-Hyderabad, India E-mail: riyaz.bhat@research.iiit.ac.in,dipti@iiit.ac.in Abstract In recent years non-projective structures have been widely studied across different languages. These dependency structures have been reported to restrict the parsing efficiency and pose problems for grammatical formalisms. Nonprojective structures are particularly frequent in morphologically rich languages like Czech and Hindi [8], [10]. In Hindi a major chunk of parse errors are due to non-projective structures [6], which motivates a thorough analysis of these structures, both at linguistic and formal levels, in Hindi and other related languages. In this work we study non-projectivity in Indian languages (ILs) which are morphologically richer with relatively free word order. We present a formal characterization and linguistic categorization of non-projective dependency structures across four Indian Language Treebanks. 1 Introduction Non-projective structures in contrast to projective dependency structures contain a node with a discontinuous yield. These structures are common in natural languages, particularly frequent in morphologically rich languages with flexible word order like Czech, German etc. In the recent past the formal characterization of nonprojective structures have been thoroughly studied, motivated by the challenges these structures pose to the dependency parsing [7], [11], [5]. Other studies have tried to provide an adequate linguistic description of non-projectivity in individual languages [4], [10]. Mannem et.al [10] have done a preliminary study on Hyderabad Dependency Treebank (HyDT) a pilot dependency treebank of Hindi containing 1865 sentences annotated with dependency structures. They have identified different construction types present in the treebank with non-projectivity. In this work we present our analysis of non-projectivity across four IL treebanks. ILs are morphologically richer, grammatical relations are expressed via morphology of words rather than the syntax. This allows words in these language to move around in the sentence structure. Such movements quite often, as we will see in subsequent sections, lead to non-projectivity in the dependency structure. We studied treebanks of four Indian Languages viz Hindi (Indo-Aryan), Urdu (Indo-Aryan), Bangla (Indo- Aryan) and Telugu (Dravidian). They all have an unmarked Subject-Object-Verb (SOV) word order, however the order can be altered under appropriate pragmatic conditions. Movement of arguments and modifiers away from the head is the major phenomenon observed that induces non-projectivity in these languages. 25
Page 1 and 2: A Treebank-based Investigation of I
Page 3 and 4: participle and a (te-)infinitival c
Page 5 and 6: Some verbs occur twice, since they
Page 7 and 8: Profiling Feature Selection for Nam
Page 9 and 10: prepositional objects (FOPP, OPP).
Page 11 and 12: the limited size of annotated data
Page 13 and 14: with high precision typically have
Page 15 and 16: ‘This was “not significantly”
Page 17: The preposition durch typically has
Page 21 and 22: the sequential order of nodes in a
Page 23 and 24: extra-posed relative clause that ge
Page 25 and 26: Experiments on Dependency Parsing o
Page 27 and 28: for mitigating the effects of spars
Page 29 and 30: obtained with MALTParser is 76.61%
Page 31 and 32: as a standardised serialisation for
Page 33 and 34: constituency and dependency, possib
Page 35 and 36: SynAF and/or in . However, they sha
Page 37 and 38: shows how some elements that are no
Page 39 and 40: Example
Page 41 and 42: ‏ Example 8: represent
Page 43 and 44:
Page 45 and 46: Chinese) as the direct object NP.
Page 47 and 48: Example 15: Tokens and Word Forms
Page 49 and 50: In a second experiment, a dataset w
Page 51 and 52:
Page 53 and 54: [3] Leech G. N., Barnett, R. & Kahr
Page 55 and 56: Effectively long-distance dependenc
Page 57 and 58: In French, another case of eLDD is
Page 59 and 60: elativization, it-clefts or questio
Page 61 and 62: 4.2.3 Annotation methodology Becaus
Page 63 and 64: Number of occurrences in FTB +SEQTB
Page 65 and 66: producing treebank based LFG approx
Page 67 and 68: 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) (
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Types Adverb. Adject. Verbs Nouns T
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Eventually we may comment that ther
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DeepBank: A Dynamically Annotated T
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from another already existing one,
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to parse now does. The extra manual
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In the derivation show in Figure 1,
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4 Patching Coverage Gaps with An Ap
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will enable further improvements in
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ParDeepBank: Multiple Parallel Deep
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2 The ParDeepBank The PTB has emerg
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undergone extensive scientific scru
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the second combines the structures
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Sofia University). Each sentence wa
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data, and improvements in the infra
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The Effect of Treebank Annotation G
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ut without feature structures. This
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only the lexicon is fine-grained to
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Automatic Coreference Annotation in
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manually annotated Czech sentences.
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citizens of Bilbao] are very involv
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4.1.3 Coreference Selector Module T
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Nominal P R F1 MUC 75.33% 81.33% 78
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[9] G. Doddington, A. Mitchell, M.
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Analyzing the Most Common Errors in
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Graph 1 shows results of subsequent
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in the semantic type). This situati
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Will a Parser Overtake Achilles? Fi
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annotation is also based on a depen
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Set Name Sentences Tokens % Train/T
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dency relations (PRED, OBJ, SBJ, AD
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ton and Stacklazy), we trained a mo
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Feature Function Column Name Addres
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Using Parallel Treebanks for Machin
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phrases are generated by a shallow
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Figure 2: A Kybot p
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SUJ NP SENT MOD PP NP PP PP NP NP P
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Checkpoint Instances Google PT Our
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6 Conclusions In this paper we have
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An integrated web-based treebank an
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16] and we have earlier reported fr
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Figure 2: Screenshot of the interfa
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ple subcategorization frames may be
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4 Integrated interface for annotati
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of the 5th International Conference
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Translational divergences and their
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posed so far, Cyrus’ work did not
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allowed an appropriate coverage of
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damentales [...] 10 (the universal
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for all lex_pair(s,t) do if head an
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the treatment of typical translatio
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Building a treebank of noisy user-g
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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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