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

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ce matin , il rapidemment mangé une pomme rouge et une poire . Example 19: Fragment of the MAF part (sample_passage.maf.xml) produced from PASSAGE conversion from Passage format passage2tiger2.pl 2012-08-28 18:25:11 PASSAGE format with French tagset 56
Example 20: Fragment of the part (sample_passage.tiger2.xml) produced from PASSAGE and relying on the MAF part and an external PASSAGE tagset (PassageTAGSET.xml) , which specifies the possible features (pos, cat, lemma, form, …) and values (in particular for the edge labels and for the partof-speech ce matin 57
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 and 18: The preposition durch typically has
Page 19 and 20: Non-Projective Structures in Indian
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: In a second experiment, a dataset w
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
Page 69 and 70: gerundives for a total amount of so
Page 71 and 72: object+indirect object/object one.
Page 73 and 74: (VP (VB patch) ) ) ) (VP (VBZ is) (
Page 75 and 76: Types Adverb. Adject. Verbs Nouns T
Page 77 and 78: Eventually we may comment that ther
Page 79 and 80: DeepBank: A Dynamically Annotated T
Page 81 and 82: from another already existing one,
Page 83 and 84: 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
Page 101 and 102:
data, and improvements in the infra
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 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
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
Page 153 and 154:
16] and we have earlier reported fr
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
Page 167 and 168:
allowed an appropriate coverage of
Page 169 and 170:
damentales [...] 10 (the universal
Page 171 and 172:
for all lex_pair(s,t) do if head an
Page 173 and 174:
the treatment of typical translatio
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
Page 187 and 188:
100 90 80 Unlabelled attachment 70
Page 189 and 190:
vidner , tilhørere og tiltalte wit
Page 191 and 192:
mance is not inconsiderable, as was
Page 193 and 194:
Genitives in Hindi Treebank: An Att
Page 195 and 196:
A genitive can occur with complex p
Page 197 and 198:
quite high. This motivates us towar
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A Treebank-based Investigation of IPP-triggering Verbs in Dutch

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