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

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[...] den ersten Gipfel [...] [...] [...] [...] le premier sommet [...] [...] [...] Figure 1: Sample KAF annotation for a German-French sentence pair The last step evaluates how well did the MT system translate the linguistic phenomena of interest. The evaluation is based on n-gram similarity, thus counting the overlapping word sequences between the hypothesis (automatic translation) and the reference (the previously identified checkpoint instances). The evaluation module requires as input the source and target language texts in KAF format, as well as the word alignments between them, the XML file produced at the previous step and the automatic translation to be evaluated. Each matched instance is evaluated separately and, on this basis, the final score for the MT system is being computed. Figure 3 presents the evaluation of the noun phrases in figure 1. In this case, the hypothesis translation contains all the possible n-grams identified in the reference (6 variants for the 3-word phrase le premier sommet), so the instance receives the maximum score (6/6). 148
Figure 2: A Kybot profile for a nominal group 4 MT Evaluation Method Our extension with respect to the original version of the tool refers to the usage of alignments from the Alpine treebank. Previous papers on the topic [15, 9] had mentioned the limitation of automatically computed alignments and suggested methods to overcome the alignment noise, but none could compete the accuracy of a handaligned corpus. Therefore the strength of our approach consists in the integration of manually checked word alignments in the evaluation pipeline. This required a special preprocessing step of extracting word alignments from the treebank and converting them to the format used by DELiC4MT. As an illustration, figure 4 depicts an aligned sentence pair from our treebank. Green lines indicate exact alignments and red lines represent fuzzy alignments. The corresponding alignments (from German to French) in the DELiC4MT format are: 0-0 1-2 2-3 3-4 4-5 5-6 5-7 6-8 6-9 6-10 6-11 7-12 8-14 8-15 ... This means that the first word in the source sentence is aligned to the first one in the target sentence, taking into consideration that word numbering starts from 0. The advantage of using a treebank over a word-aligned corpus is that the treebank contains other alignment types than “simple“ 1-1 word alignments. This often happens in German due to its frequent compounds, which are then represented as 1-n word alignments. For instance, the German compound Montblanc-Abenteuer (EN: Mont Blanc adventure) in figure 4 is aligned to the French noun phrase aventure au Mont Blanc. This correspondence is translated into the following word alignments: 6-8 6-9 6-10 6-11. 149
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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
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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
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
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
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Page 129 and 130: annotation is also based on a depen
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Page 133 and 134: dency relations (PRED, OBJ, SBJ, AD
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Page 137 and 138: Feature Function Column Name Addres
Page 139 and 140: Using Parallel Treebanks for Machin
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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 161 and 162: of the 5th International Conference
Page 163 and 164: Translational divergences and their
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Page 177 and 178: Phenomenon Attested example Std. co
Page 179 and 180: Figure 1: French Social Media Bank
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Page 183 and 184: Det Adj N (a) Danish Det Adj N (b)
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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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