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

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prose texts from the PROIEL corpus, in order to improve the performances on prose texts 16 . Appendix: Feature Models The feature models used for the experiments are reported in the following tables. The lines of the tables are grouped by Feature Function and Column Name. For an explanation of Feature Functions, Address Functions (both parsing-algorithmspecific functions and dependency-graph functions), as well as of the syntax of the feature-model files, see [1]. Values of FEATS are split by “|” in Left[0], Left[1] for MO; in Left[0], Left[1], Right[0] for Exp4-Cov; in Stack[0], Stack[1], Stack[2], Stack[3] for Exp4-Stack. Feature Function Column Name Address Function InputColumn FEATS Left[0]; Right[0] InputColumn FORM Left[0]; Right[0]; Right[1]; head(Left[0]) InputColumn POSTAG LeftContext[0] ;Left[0]; Left[1]; RightContext[0]; Right[0]; Right[1]; Right[2]; Right[3] OutputColum DEPREL Left[0]; Right[0]; ldep(Left[0]); ldep(Left[0]); ldep(Right[0]); rdep(Left[0]) Table 10: MO feature model References [1] MaltParser user guide. http://www.maltparser.org/userguide.html. [2] M. Ballesteros and J. Nivre. MaltOptimizer: a system for MaltParser optimization. In Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC 12), Istanbul, 2012. European Language Resources Association (ELRA). 16 We would like to thank J. Nivre and M. Ballestreros for their kind support with Malt and MaltOptimizer. Professor R. Förtsch and R. Krempel (CoDArchLab, Cologne) also provided decisive help with the infrastructures of the laboratory for digital archaeology at the University of Cologne. 142
Feature Function Column Name Address Function InputColumn FEATS Left[0]; Left[1]; Right[0]. InputColumn FORM LeftContext[0]; LeftContext[1]; LeftContext[2]; Left[0]; Left[1]; Left[2]; RightContext[0]; Right[0]. InputColumn LEMMA LeftContext[0]; LeftContext[1]; LeftContext[2]; LeftContext[3]; Left[0]; Left[1]; Left[2]; Left[3]; RightContext[0]; Right- Context[1]; RightContext[2]; Right[0]; Right[1]; Right[2]; Right[3]; head(Left[0]). InputColumn POSTAG LeftContext[0]; LeftContext[1]; LeftContext[2]; LeftContext[3]; Left[0]; Left[1]; Left[2]; Left[3]; RightContext[0]; Right- Context[1]; RightContext[2]; Right[0]; Right[1]; Right[2]; Right[3]; head(Left[0]), OutputColumn DEPREL Left[0]; Right[0]; ldep(Left[0]); ldep(Right[0]); rdep(Left[0]). Table 11: Exp4-Cov feature model Feature Function Column Name Address Function InptuColumn FEATS Stack[0]; Stack[1]. InptuColumn FORMS Input[0]; Lookahead[0]; Stack[0]; Stack[1] InputColumn LEMMA Input[0]; Lookahead[0]; Lookahead[1]; Lookahead[2]; Lookahead[3]; Stack[0]; Stack[1]; Stack[2]. InputColumn POSTAG Input[0]; Lookahead[0]; Lookahead[1]; Lookahead[2]; Lookahead[3]. InputColumn POSTAG Stack[0]; Stack[1]; Stack[2]. OutputColumn DEPREL Stack[0]; Stack[1]; ldep(Stack[0]); ldep(Stack[1]); rdep(Stack[0]); rdep(Stack[1]). Table 12: Exp4-Stack feature model 143
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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) (
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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
Page 85 and 86: In the derivation show in Figure 1,
Page 87 and 88: 4 Patching Coverage Gaps with An Ap
Page 89 and 90: will enable further improvements in
Page 91 and 92: ParDeepBank: Multiple Parallel Deep
Page 93 and 94: 2 The ParDeepBank The PTB has emerg
Page 95 and 96: undergone extensive scientific scru
Page 97 and 98: the second combines the structures
Page 99 and 100: Sofia University). Each sentence wa
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Page 103 and 104: The Effect of Treebank Annotation G
Page 105 and 106: ut without feature structures. This
Page 107 and 108: only the lexicon is fine-grained to
Page 109 and 110: Automatic Coreference Annotation in
Page 111 and 112: manually annotated Czech sentences.
Page 113 and 114: citizens of Bilbao] are very involv
Page 115 and 116: 4.1.3 Coreference Selector Module T
Page 117 and 118: Nominal P R F1 MUC 75.33% 81.33% 78
Page 119 and 120: [9] G. Doddington, A. Mitchell, M.
Page 121 and 122: Analyzing the Most Common Errors in
Page 123 and 124: Graph 1 shows results of subsequent
Page 125 and 126: in the semantic type). This situati
Page 127 and 128: Will a Parser Overtake Achilles? Fi
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: ton and Stacklazy), we trained a mo
Page 139 and 140: Using Parallel Treebanks for Machin
Page 141 and 142: phrases are generated by a shallow
Page 143 and 144: Figure 2: A Kybot p
Page 145 and 146: SUJ NP SENT MOD PP NP PP PP NP NP P
Page 147 and 148: Checkpoint Instances Google PT Our
Page 149 and 150: 6 Conclusions In this paper we have
Page 151 and 152: An integrated web-based treebank an
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Page 155 and 156: Figure 2: Screenshot of the interfa
Page 157 and 158: ple subcategorization frames may be
Page 159 and 160: 4 Integrated interface for annotati
Page 161 and 162: of the 5th International Conference
Page 163 and 164: Translational divergences and their
Page 165 and 166: posed so far, Cyrus’ work did not
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Page 175 and 176: Building a treebank of noisy user-g
Page 177 and 178: Phenomenon Attested example Std. co
Page 179 and 180: Figure 1: French Social Media Bank
Page 181 and 182: Impact of treebank characteristics
Page 183 and 184: Det Adj N (a) Danish Det Adj N (b)
Page 185 and 186: 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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