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

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Another part of the development was its compatibility to the TigerXML model, i.e making sure that existing data in TigerXML can be converted to without loss of information. To test this, we implemented a mapping from TigerXML to the API. This allows us to import already existing data in TigerXML into the API and export it to , and vice versa, though this may lead to information losses in some cases. Testing native TigerXML files and converting them back and forth, we can ensure no information is lost. Another interesting prospect is converting data from other graph-based formats into . We therefore used the SaltNPepper framework [15], a universal importer to convert a wide range of formats into each other. Pepper is a plug-in based converter which uses the intermediate metamodel Salt to make a direct conversion between several formats. Using SaltNPepper and the API we created a module mapping to the Salt metamodel which we plugged into the framework. This step has allowed us to benefit from all already existing SaltNPepper modules and to convert data e.g. from and into the CoNLL (http://ilk.uvt.nl/conll/#dataforma t) format, the PAULA XML format [16], the GrAF format [17] and more. The CoNLL format, is a field-based format largely used in international parsing evaluations. It is dependency-based, usually restricted to projective dependencies, with several fields such as FORM, LEMMA, CPOSTAG, POSTAG, FEATS, etc. A conversion to is rather straightforward, with no need for nt elements (see Example 17 and 18). 1 il il CL CLS _ 2 suj _ _ 2 mange manger V V _ 0 root _ _ 3 une un D DET _ 4 det _ _ 4 pomme pomme N NC _ 2 obj _ _ 5 . . PONCT PONCT _ 2 ponct _ _ Example 17: CoNLL output for “he eats an apple” Example 18: Fragment of a possible representation for CoNLL When using the CoNLL format, some problems may arise from the fact that it does not follow the two level segmentation model of MAF (with tokens and word forms), leading to compound POS such as P+D for the agglutinate des (‘de’ + ‘les’ = of the). 54
In a second experiment, a dataset was converted from the Passage format [18] produced by the French parser FRMG [19] 4 into the format. Though there is no module for SaltNPepper supporting the Passage format at present, a dedicated generator was constructed for this purpose. The Perl conversion script was easily developed, and, while it remains to test it on complex cases, it demonstrates the potential of to encode relatively complex syntactic annotation formats. The Passage format was developed for the French evaluation campaign also called Passage (http://atoll.inria.fr/passage/). Accordingly, the Passage format is currently produced by several French parsers, and is relatively rich in information. It is a successor of the EASy format, extended to better conform the recommendations of ISO TC37SC4, in particular with the MAF format (at the tokenization level with T and W elements) and with the use of ISO data categories. Passage is both constituency-based, with 6 kinds of chunks (called G) and dependency-based, with 14 kinds of relations anchored by either word forms W or chunks G. The T and W elements are straightforwardly converted into MAF elements and a correspondence (with attribute @corresp) is established at the level of elements t towards the MAF word forms. The chunks become nt elements and the relations become edge elements either within t or nt elements. However, strictly speaking, Passage relations are not oriented (since they entail no explicit notion of governor and governee), but rolebased. Furthermore, the COORD relation (for the coordinations) is ternary and has 3 roles, namely coordonnant (coordinator), coord-g (left coordinated) and coord-d (right coordinated). To fit in the metamodel, it was therefore needed to orient the relations (by choosing a governor and orienting the edge from the governee to its governor) and binarize the COORD relation (using the coordinator as governor). Fortunately, no information is lost in the process. This is achieved using a @label attribute on edge, which combines the name of the relation with a role name (such as the SUJ-V_sujet label for the subject in a SUJ-V relation). Constituency is represented by edges labelled comp within nt elements. Finally, the additional information carried by Passage W elements, such as form, lemma and mstag are moved to the MAF wordForm elements, with a conversion of the mstag flat feature structure (using tags) into a deep expanded feature structure (based on the FSR standard). Example 21 shows the original Passage fragment and Example 19 and 20 the corresponding representation in MAF and . 4 FRMG may be tried on line at http://alpage.inria.fr/parserdemo with the possible production of 3 formats (DepXML,Passage, CoNLL). The format should be soon added, thanks to the script presented in this paper. 55
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: Example 15: Tokens and Word Forms
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
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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